{"topic":{"aliases":["推論","inference","prefill","decode","KV cache","KVキャッシュ"],"definition":"技術記事中の推論処理、prefill・decode・KV 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Blog)","quote_start":0,"quote_end":148,"text_sha256":"202f9280493be134af4a186f0d02b99e31b020866b08f95b1c54d5a3d5081f82","block_sha256":"202f9280493be134af4a186f0d02b99e31b020866b08f95b1c54d5a3d5081f82","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_0f7c606d-558f-4edb-99eb-4609a9fb0a95/#blk_d10b5fa7-02c3-4f03-8649-cbde4d6e3fac"},{"id":"occ_f67ebf479c0bfde5a5464004","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_0f7c606d-558f-4edb-99eb-4609a9fb0a95","work_id":"wrk_958dfab7-4e11-4e38-9d35-db36e1f1b2f7","block_id":"blk_fd861c49-8be9-4a0e-a729-d40ff6f56432","section_id":"sec_cac101fb-2a1e-4da7-b4fd-86f82ca10dc9","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":14,"end":23,"exact":"inference","quote":"M890はtraining/inference両用で、agentic workloadを強く意識している。","quote_start":0,"quote_end":53,"text_sha256":"739fd037337f45207bb5782539379715cf510ed2d8550287a22ddea6583b3ca6","block_sha256":"739fd037337f45207bb5782539379715cf510ed2d8550287a22ddea6583b3ca6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_0f7c606d-558f-4edb-99eb-4609a9fb0a95/#blk_fd861c49-8be9-4a0e-a729-d40ff6f56432"},{"id":"occ_ff9efce7dedcba79be0848d1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_116e8058-0998-435c-a807-8bae68191750","section_id":"sec_4c1b7425-e255-41ee-973e-f085a28334b7","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":140,"end":142,"exact":"推論","quote":"る\n        ↓\nGR00Tが具体的なロボット行動を生成する\n        ↓\nJetsonが実機上で推論を実行する\n```","quote_start":85,"quote_end":151,"text_sha256":"d91f573bee7a32857fc5429e78737a96c9ce4f1c603e4263edde4104498dea82","block_sha256":"d91f573bee7a32857fc5429e78737a96c9ce4f1c603e4263edde4104498dea82","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_116e8058-0998-435c-a807-8bae68191750"},{"id":"occ_e0554fb0db1092ac2ac92cd7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_133fec93-78de-4a47-9147-f29405d99d7f","section_id":"sec_acdbe3a4-5cb9-40c9-b849-90089815e874","layer":"code","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"```text\n長期推論・タスク理解\n→ GR00Tの推論機能、言語モデル\n\n世界理解・未来生成\n→ Cosmos\n\n行動生成\n→ GR00T、Cosmosを基盤としたWAM\n\n仮想世界・データ生成\n→ Omniverse、","quote_start":0,"quote_end":112,"text_sha256":"f37db9c546371591cade9cdc56fd8199ce1f6963c81d759e29f9370e66af87f0","block_sha256":"f37db9c546371591cade9cdc56fd8199ce1f6963c81d759e29f9370e66af87f0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_133fec93-78de-4a47-9147-f29405d99d7f"},{"id":"occ_6f567ccaadbcaaa2e331dcff","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_3495ad21-5c05-4d83-a333-3333edf06403","section_id":"sec_f53945ab-3777-45e3-a419-0b72000f8eef","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"ただし、近年のGR00Tは単純な反射的行動だけではなく、推論、指示追従、複数段階の作業への対応も強化されています。したがって、「GR00Tは短期動作しかできない」と考えるのは正確ではありません。([NVIDIA Newsroom][7])","quote_start":0,"quote_end":119,"text_sha256":"a137fb276438679cab36188fab425393517f10fa1d74cf1a0d1d3becb797e3b7","block_sha256":"a137fb276438679cab36188fab425393517f10fa1d74cf1a0d1d3becb797e3b7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_3495ad21-5c05-4d83-a333-3333edf06403"},{"id":"occ_3f84b1c7e22b45dc57cbb405","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_5b72a61f-ce55-417d-ac20-dd635d4f2a79","section_id":"sec_ee570e19-b9b4-40c2-aecb-5c8300a7026b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"さらに2026年に発表されたCosmos 3は、言語、画像、動画、音声、行動を扱い、**物理推論・世界生成・行動生成を一つのモデル系列で統合する方向**へ進んでいます。NVIDIAはCosmos 3をWAMのバックボーンとして利用できるものと位置付けています。([NVIDIA Developer","quote_start":0,"quote_end":148,"text_sha256":"ca9c4fa1a348c1a5b6b7242adc48413f43c7d16b5f29137ea0a7a1ec4d3c9c59","block_sha256":"ca9c4fa1a348c1a5b6b7242adc48413f43c7d16b5f29137ea0a7a1ec4d3c9c59","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_5b72a61f-ce55-417d-ac20-dd635d4f2a79"},{"id":"occ_0b821417dc317999d69991e4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_6f332e0b-4ea7-4e77-a8ca-e8e5e5ed692a","section_id":"sec_733e5854-1a73-4696-b39e-3e369fde4107","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":66,"end":68,"exact":"推論","quote":"来を予測することもありますし、WAMが言語指示を受け取ることもあります。GR00Tのように、VLAでありながら推論や複数ステップの処理を行うモデルもあります。","quote_start":11,"quote_end":90,"text_sha256":"a21e9155b9b8c7623b85e7577ef535e0647266dbb0d867490cef2688ceff21a0","block_sha256":"a21e9155b9b8c7623b85e7577ef535e0647266dbb0d867490cef2688ceff21a0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_6f332e0b-4ea7-4e77-a8ca-e8e5e5ed692a"},{"id":"occ_704dfad5d9e7785e9985aa4b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_bbe64f46-ee0e-4e46-9b86-bbebaa6a2403","section_id":"sec_8b15f18f-acf8-428c-8f26-4d7d9a979944","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":70,"end":72,"exact":"推論","quote":"品 | 読み方 |\n| --- | --- | --- |\n| 世界生成 | Cosmos | 未来状態、物理推論、合成データ |\n| 仮想環境 | Omniverse | デジタルツイン、物理シミュレーション |\n| 開発基盤 | Isaac | 学習、評価、データ生成、展開 |\n| 行動生成 | GR00T ","quote_start":15,"quote_end":172,"text_sha256":"e677b6220dd4a3d4cace715b17cd385be4b1b19c40d067bcbd694dd1acfcee3e","block_sha256":"e677b6220dd4a3d4cace715b17cd385be4b1b19c40d067bcbd694dd1acfcee3e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_bbe64f46-ee0e-4e46-9b86-bbebaa6a2403"},{"id":"occ_934e00d689933096650fa902","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_c18611dc-2a6b-432e-b997-50dcdc07885e","section_id":"sec_acdbe3a4-5cb9-40c9-b849-90089815e874","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"特にCosmos 3は世界生成、物理推論、行動生成を統合する方向へ進み、GR00T N1.7は言語・画像から具体的なロボット行動を生成します。([NVIDIA Developer][2])","quote_start":0,"quote_end":94,"text_sha256":"887e3d9cdcb21fde995484c2fb93718e33e3363363beacc4a351118946a4386a","block_sha256":"887e3d9cdcb21fde995484c2fb93718e33e3363363beacc4a351118946a4386a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_c18611dc-2a6b-432e-b997-50dcdc07885e"},{"id":"occ_b7f1e8bb2025dcc046ec2dea","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_e93db104-677c-444a-a308-c88b2a6ae0e7","section_id":"sec_61fe8dc1-05c6-4873-afec-349ba4b4961d","layer":"character","character_id":"zetu_noia","count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":58,"end":60,"exact":"推論","quote":"、AIデータセンターのときと似ています。NVIDIAはGPU単体ではなく、ラック、ネットワーク、ソフトウェア、推論基盤まで束ねました。ロボットでも同じことをしようとしている。モデルだけではなく、モデルが育つ場所と、モデルが動く身体まで押さえる戦略です。","quote_start":3,"quote_end":129,"text_sha256":"98eee1aa24057086732da978b7c0244afc13bcc87bdb723d3ab621264422f866","block_sha256":"98eee1aa24057086732da978b7c0244afc13bcc87bdb723d3ab621264422f866","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_e93db104-677c-444a-a308-c88b2a6ae0e7"},{"id":"occ_809453f07beae9cfa45b6b48","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_f13d6246-1612-4545-95cd-788aec058669","section_id":"sec_0b89688c-d98e-4d46-ad82-cbe2e4e90463","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":124,"end":126,"exact":"推論","quote":"未来生成、Omniverseは仮想世界、Isaacはロボット開発基盤、GR00Tは行動生成、Jetsonは実機推論に近い。\n- VLA・WAM・WLAの違いは、短期・中期・長期ではなく、明示的に予測する対象で整理する。\n- フィジカルAIでは、シミュレーションと現実の差分をどう埋めるかが最大の難所になる。\n- N","quote_start":69,"quote_end":226,"text_sha256":"4cc635ff766f6235e50f14ec612640b1dc829d0f8dca76cfac6f913a13c692b8","block_sha256":"4cc635ff766f6235e50f14ec612640b1dc829d0f8dca76cfac6f913a13c692b8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_f13d6246-1612-4545-95cd-788aec058669"},{"id":"occ_d3e61033c1d746dc93e0fd9c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_11f7264b-855b-41d8-b760-87dbfa630e9f","work_id":"wrk_739d4c20-62f1-49ee-9121-4689ad38c0d3","block_id":"blk_fdb2f653-16ce-47d7-b16d-e4492f39ad09","section_id":"sec_30a2c5c7-85c2-4bb5-9c28-500939c99b35","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"」と定義するのは適切ではありません。現在のVLAには、複数行動をまとめたアクションチャンク、高レベル計画、言語推論、暗黙的な世界モデルを持つものも存在します。VLA研究でも、計画と実行を分離した階層型や、世界モデルを組み込んだ構成が整理されています。([arXiv][10])","quote_start":27,"quote_end":165,"text_sha256":"ed82a5b08ad4e2b74724e4049470fb260a3c581d90f2bbe45fbf1af76ba07cf1","block_sha256":"ed82a5b08ad4e2b74724e4049470fb260a3c581d90f2bbe45fbf1af76ba07cf1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_11f7264b-855b-41d8-b760-87dbfa630e9f/#blk_fdb2f653-16ce-47d7-b16d-e4492f39ad09"},{"id":"occ_e4f5a95677c792f0c7665794","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_120b7405-aefc-4d5d-a68b-9713613e3e1f","work_id":"wrk_91a0ac80-8870-4ec9-b8a6-4081acad5334","block_id":"blk_0fbdd02e-238a-40f7-a66b-e4966beeaf20","section_id":"sec_3886953f-b1b5-42de-8538-f26b912220ac","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":295,"end":297,"exact":"推論","quote":"実業へ統合する企業 |\n| ネット利用者数 | AI利用者・トークン・エージェント |\n| ページビュー | 推論量・トークン量 |\n| 光回線スワップ | 循環出資・クラウド購入契約 |\n```","quote_start":240,"quote_end":338,"text_sha256":"a560202eabd14b445a971c345ad1cae80eb096a5054fa45474a4b4c541a64253","block_sha256":"a560202eabd14b445a971c345ad1cae80eb096a5054fa45474a4b4c541a64253","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_120b7405-aefc-4d5d-a68b-9713613e3e1f/#blk_0fbdd02e-238a-40f7-a66b-e4966beeaf20"},{"id":"occ_25facc68b96d6af2b073856c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_2e15b009-2e5d-4757-83b5-6ee922c57951","section_id":"sec_0eae083a-7c80-41de-8169-72c1792096de","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"モデル単体の総合性能で常に1位を取るかは分からない。深い推論、長文調査、コーディング、企業向け作業AIでは、ChatGPTやClaudeが引き続き強い可能性がある。","quote_start":0,"quote_end":82,"text_sha256":"a2b6367acefb2274dbb74b61852c0bff456e8e3c7490bee2ed7248e737b208ed","block_sha256":"a2b6367acefb2274dbb74b61852c0bff456e8e3c7490bee2ed7248e737b208ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_2e15b009-2e5d-4757-83b5-6ee922c57951"},{"id":"occ_e85759556e026256b996b5c9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_509612ca-4e9d-42b8-a6b9-fd48b2bad3c8","section_id":"sec_00c349e9-1fba-46b6-94cb-72d34486367e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"Muse Sparkでは、マルチモーダル推論、tool use、visual chain of thought、multi-agent orchestrationが強調されている。これは、単なるチャットモデルではなく、エージェントAIを前提とし","quote_start":0,"quote_end":122,"text_sha256":"f3cd75fddc5e47a622bfd8f197358cfc6220e7282293737eb2cb7004ce522e2b","block_sha256":"f3cd75fddc5e47a622bfd8f197358cfc6220e7282293737eb2cb7004ce522e2b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_509612ca-4e9d-42b8-a6b9-fd48b2bad3c8"},{"id":"occ_53c9a9fde81f43ab8045f5d2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_519a2012-514d-4b5c-8352-fa312540558b","section_id":"sec_1d1ec9f4-f124-4292-aaf6-5e63e88ab0bc","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"は、後から設計を直すのが非常に重い。数十万GPU規模で本命モデルを学習した後に、アーキテクチャ、データ、RL、推論モード、tool use、エージェント設計に欠陥が見つかれば、損失は莫大になる。そのため、Muse Sparkのような前段階モデルで、スケーリング則、安全性、推論時計算、製品接続、ユーザー反応を確認す","quote_start":6,"quote_end":163,"text_sha256":"1510796e0e8278b629a1f9909c29504b1c6f227aa360b7a24571841909955516","block_sha256":"1510796e0e8278b629a1f9909c29504b1c6f227aa360b7a24571841909955516","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_519a2012-514d-4b5c-8352-fa312540558b"},{"id":"occ_39f52c5e36aa194efbf5105a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_845eac77-fbef-4f9d-9da4-bc1cadce7977","section_id":"sec_bbe7c4e9-fbcc-4545-aa71-0df4086c9765","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":45,"end":47,"exact":"推論","quote":"フロンティアAIは、もはや単純な「モデル研究」ではない。  \nそれは、データ、評価、RL、推論時スケーリング、tool use、multi-agent orchestration、安全性、プロダクト導入、収益化まで含む巨大な工業プロセスになっている。","quote_start":0,"quote_end":125,"text_sha256":"3bfca9529b4cb1c62192053c91b24f03c74cb2930872b6993e27c59cde2a4075","block_sha256":"3bfca9529b4cb1c62192053c91b24f03c74cb2930872b6993e27c59cde2a4075","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_845eac77-fbef-4f9d-9da4-bc1cadce7977"},{"id":"occ_ca3ed9b5cce904b7e1dcc2ac","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_a997d907-b75a-45e0-a3f5-a311a964dcf3","section_id":"sec_00c349e9-1fba-46b6-94cb-72d34486367e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"しかし、Muse Sparkは「Metaが本命モデルを作る前に、スケーリング、推論時設計、multi-agent、tool use、安全性、製品導入を確認するための試作機」と考えると、かなり重要な意味を持つ。","quote_start":0,"quote_end":104,"text_sha256":"6491c5c2fa22a1cbd813469bcb67a5fbb222ed503dfe7df9a7618247b6429910","block_sha256":"6491c5c2fa22a1cbd813469bcb67a5fbb222ed503dfe7df9a7618247b6429910","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_a997d907-b75a-45e0-a3f5-a311a964dcf3"},{"id":"occ_204ac429e717f0a23eca347c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_c5fc75d3-d772-4093-a0b8-5662c8c13013","section_id":"sec_eae60cb9-e307-468a-889d-d43d00f60abe","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":74,"end":76,"exact":"推論","quote":"検証したスケーリング設計、multi-agent orchestration、tool use、マルチモーダル推論を本命モデルに拡張し、さらにBusiness Agentや広告AIで収益化できれば、Wang氏はMetaのAI再建を象徴する人物になる。","quote_start":19,"quote_end":144,"text_sha256":"92091d07414f4dd369492524c2854cb50a7d2333d6073a880960455e6d3d0d5c","block_sha256":"92091d07414f4dd369492524c2854cb50a7d2333d6073a880960455e6d3d0d5c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_c5fc75d3-d772-4093-a0b8-5662c8c13013"},{"id":"occ_3753259d244607d032837210","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_d58504f5-5cbc-4561-92f6-811898683af0","section_id":"sec_d25c9390-cd00-49fb-9227-81cdb9c8124d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"第三に、安全性の問題がある。Muse Sparkは強力な推論能力を持つため、オープンソース化には慎重な判断が必要になった。今後、Metaの最強モデルはクローズド化し、オープンに出るのは軽量版や安全化されたモデルになる可能性がある。","quote_start":0,"quote_end":115,"text_sha256":"22cd0ee92161889a0d91eb8ccbcec2e6a952012e5590734c9d891ce03b4b8fc6","block_sha256":"22cd0ee92161889a0d91eb8ccbcec2e6a952012e5590734c9d891ce03b4b8fc6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_d58504f5-5cbc-4561-92f6-811898683af0"},{"id":"occ_9f99d6fc68fc4b35e18e1e8d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_db64c9c8-2410-46c0-9416-522d6fa67e73","section_id":"sec_0eae083a-7c80-41de-8169-72c1792096de","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":80,"end":82,"exact":"推論","quote":"その検証結果を踏まえて作られる、より大きなマルチモーダル・エージェントモデルになるだろう。事前学習、強化学習、推論時スケーリング、multi-agent orchestration、tool use、AIグラス、Business Agent、個人エージェント。これらがうまく統合されれば、Meta AIは単なるCh","quote_start":25,"quote_end":182,"text_sha256":"71bf01cfa56a6b4148d1949f89270ae45d6a54b06b7eb31a09de47284390f157","block_sha256":"71bf01cfa56a6b4148d1949f89270ae45d6a54b06b7eb31a09de47284390f157","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_db64c9c8-2410-46c0-9416-522d6fa67e73"},{"id":"occ_352d9d8a4dba0db33dfc497d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_edc031c1-91a2-435e-9123-18c711bd86de","section_id":"sec_457c7dc9-dbc6-439b-aac9-eaa00cc9dc1a","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"まず、事前学習によって基礎能力を作る。次に、強化学習によって推論や行動の品質を高める。そして、推論時には、モデルにどれだけ考えさせるか、どのように複数の候補を比較するか、どのようにツールを使うかが重要になる。","quote_start":0,"quote_end":104,"text_sha256":"f43a54566edb24d357345c343417d4c103b137fc9ea94f0d16a5d24fe9261914","block_sha256":"f43a54566edb24d357345c343417d4c103b137fc9ea94f0d16a5d24fe9261914","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_edc031c1-91a2-435e-9123-18c711bd86de"},{"id":"occ_6c4bc31b08c1ccc71c038ba6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_f3677dce-c143-4fe7-beda-d31d1fa1bc60","section_id":"sec_1d1ec9f4-f124-4292-aaf6-5e63e88ab0bc","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":124,"end":126,"exact":"推論","quote":"lama 4以降、フロンティアモデル競争でやや出遅れた印象があった。その反省から、モデルの事前学習、強化学習、推論時スケーリング、マルチモーダル、エージェント設計、インフラをまとめて作り直した。その最初の実戦投入がMuse Sparkだった。","quote_start":69,"quote_end":190,"text_sha256":"1be1c7a405fcfd256e95185a5fd72ffc3320c0bc3871e1521181890d0d8131b3","block_sha256":"1be1c7a405fcfd256e95185a5fd72ffc3320c0bc3871e1521181890d0d8131b3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_f3677dce-c143-4fe7-beda-d31d1fa1bc60"},{"id":"occ_abc6efa07fb62ee78295df86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_12eaaf24-14fe-4445-936b-b81ef492d19d","work_id":"wrk_cd2e4a6c-2b36-4815-ba7a-e21b688d91cb","block_id":"blk_f951cf95-077e-40aa-87b4-50af238a605b","section_id":"sec_ad853364-0c22-4646-8055-aecfa697a614","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":"のフロンティアモデルは、事前学習だけで差がつくわけではない。  \nポストトレーニング、RLHF、専門家データ、推論評価、エージェント行動評価、安全性評価、レッドチーミングで差がつく。","quote_start":1,"quote_end":92,"text_sha256":"f1b5d9a71214c5718fb1454b64da5be67c4d54ddf8bf9d0d41814e71c68df0d9","block_sha256":"f1b5d9a71214c5718fb1454b64da5be67c4d54ddf8bf9d0d41814e71c68df0d9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_12eaaf24-14fe-4445-936b-b81ef492d19d/#blk_f951cf95-077e-40aa-87b4-50af238a605b"},{"id":"occ_a1ae4557012ca60f77174c47","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_1fd1fa2f-ec14-43c8-9137-d9f222cb08bb","section_id":"sec_30aa8942-8f88-4d6a-8e9a-31d4b9bcc63e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"## 第1層：ロボット上の高速推論","quote_start":0,"quote_end":17,"text_sha256":"e5bbf69627293b9872876c70c6fe5abad5deec57539633848e8582fcb114a109","block_sha256":"e5bbf69627293b9872876c70c6fe5abad5deec57539633848e8582fcb114a109","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_1fd1fa2f-ec14-43c8-9137-d9f222cb08bb"},{"id":"occ_c2cccdbbcd71c7b573988a8b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_4d80f6f6-1982-4593-a6d8-584fbf734b8a","section_id":"sec_abbde735-ed82-41ec-84c7-ed35ac95cdf4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":47,"end":49,"exact":"推論","quote":"**\\[3\\] DreamZero／WAM**：14B動画拡散モデル、500時間のデータ、単純推論5.7秒、最([arXiv](https://arxiv.org/abs/2602.15922?utm_source=chatgpt.com))","quote_start":0,"quote_end":121,"text_sha256":"cb074bda7d540f7248b1f0043238e84e883741c4fe0f6ab44bbf55b5c5a5be98","block_sha256":"cb074bda7d540f7248b1f0043238e84e883741c4fe0f6ab44bbf55b5c5a5be98","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_4d80f6f6-1982-4593-a6d8-584fbf734b8a"},{"id":"occ_04a5021ebac6e0c830f92c75","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_52aa8d35-b06e-4feb-8550-badc6684f67e","section_id":"sec_2fe6fb27-e344-4768-a423-6c0ae009ad32","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":71,"end":78,"exact":"KVキャッシュ","quote":"向け低遅延GPU\n- 小型DiT・Flow Matching演算\n- CUDA Graph\n- 演算融合\n- KVキャッシュ\n- 高速なGDDR・LPDDR\n- TTS候補のバッチ並列\n- World Expertを必要時だけ起動できる異種計算","quote_start":16,"quote_end":138,"text_sha256":"72ad66b06d6e2a2327636b3b16adc260f911b47fac3f34e37942ec611a0c247a","block_sha256":"72ad66b06d6e2a2327636b3b16adc260f911b47fac3f34e37942ec611a0c247a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_52aa8d35-b06e-4feb-8550-badc6684f67e"},{"id":"occ_4c4017e748efcb434ed3106d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_5f67499f-bea0-48ee-837d-d17b1ecde703","section_id":"sec_fbf370d7-1865-495b-9f46-5323c047c7b1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":9,"end":11,"exact":"推論","quote":"したがって実用的な推論環境は、工学的には、","quote_start":0,"quote_end":21,"text_sha256":"4e28881ed17ce2496ce4ad5d1f6cdfba0998d9890c9de158bf6ce6a04def1daf","block_sha256":"4e28881ed17ce2496ce4ad5d1f6cdfba0998d9890c9de158bf6ce6a04def1daf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_5f67499f-bea0-48ee-837d-d17b1ecde703"},{"id":"occ_e4b8ceaf954f0a98e0d09125","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_66964fe6-3997-4f6e-be50-bfb4f26d70ee","section_id":"sec_bcf41a58-cf2b-42c4-bd98-57f4ccc7e9e9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":50,"end":52,"exact":"推論","quote":"- 大規模な稼働ロボット台数\n- 独自の実世界データ\n- 遠隔操作・人間介入データ\n- 高速なエッジ推論\n- 大規模な中央学習基盤\n- シミュレーション能力\n- 安全性と認証\n- ハードとソフトの垂直統合\n- 複数機体へ技能を移す能力","quote_start":0,"quote_end":117,"text_sha256":"95d0f0a50977b26fc8cea4fb155a424c9e840520c7c7a66f0fb06afdc21700a9","block_sha256":"95d0f0a50977b26fc8cea4fb155a424c9e840520c7c7a66f0fb06afdc21700a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_66964fe6-3997-4f6e-be50-bfb4f26d70ee"},{"id":"occ_a2f27613a4ec8044fa46c2ef","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_6c08a509-1efa-4ff8-859e-509be13cd870","section_id":"sec_0d5c3b67-4bb9-4295-bf6a-880fc5e16ea0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"- 総パラメータ：3.4B\n- 通常推論時：2B active\n- RTX 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条件付き・無条件の2系統\n- Action Expert\n- CUDAワークスペース","quote_start":0,"quote_end":81,"text_sha256":"56d130c6bbb1366ca30b4e507c35bff58326f25266f9c636f901b69c74e6a402","block_sha256":"56d130c6bbb1366ca30b4e507c35bff58326f25266f9c636f901b69c74e6a402","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_8cfe9921-4f46-4998-a85f-09947fab1cc9"},{"id":"occ_6bf2677cc3d5f5afd5dba860","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_a08b5454-a840-43f4-bc13-e7073918a404","section_id":"sec_a57569ed-1dfc-4d53-a08d-273fd522ddc9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"日本語では「推論時スケーリング」と表現できる。","quote_start":0,"quote_end":23,"text_sha256":"85f2670d119e143a7eeb6a9fcb3d3cf4f11a9887aaa12c88781afe874577f297","block_sha256":"85f2670d119e143a7eeb6a9fcb3d3cf4f11a9887aaa12c88781afe874577f297","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_a08b5454-a840-43f4-bc13-e7073918a404"},{"id":"occ_61f5f2bba2c40e059b2a8fd8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_a6987aeb-5903-47dc-be59-9a2d001e551e","section_id":"sec_0d5c3b67-4bb9-4295-bf6a-880fc5e16ea0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"VRAM使用量は論文で公開されていないため、パラメータ数と一般的な推論バッファから見積もると、","quote_start":0,"quote_end":47,"text_sha256":"9fd392ef7d420a4c39c1013e08e10686a25f365089576c011ba781450c945390","block_sha256":"9fd392ef7d420a4c39c1013e08e10686a25f365089576c011ba781450c945390","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_a6987aeb-5903-47dc-be59-9a2d001e551e"},{"id":"occ_df2be7ba01b7ecbd3fa295ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_a7ecdc00-cde3-4bec-931b-42df78a28854","section_id":"sec_abbde735-ed82-41ec-84c7-ed35ac95cdf4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":57,"end":59,"exact":"推論","quote":"\\[2\\] OpenVLA**：7B、97万エピソード、64基のA100で14日・約21,500 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精度 | 推論VRAM |\n| :--- | :--- |\n| BF16 | 16.8GB |\n| INT8 | 10.2GB |\n| INT4 | 7.0GB |\n```","quote_start":0,"quote_end":92,"text_sha256":"07c8e4edc8bcd6bf9f1dbe9c987f56dffc0b529e29b1f37039d0b77f096d2261","block_sha256":"07c8e4edc8bcd6bf9f1dbe9c987f56dffc0b529e29b1f37039d0b77f096d2261","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_15381314-c653-4ce9-9b25-d8495cdbf4d1/#blk_ac55fe4d-bb9b-4c68-8039-0f5f1f21e80e"},{"id":"occ_07d57cba32cde7399f1326a4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_15381314-c653-4ce9-9b25-d8495cdbf4d1","work_id":"wrk_220c8e7e-eccc-4357-89f5-9a6b78a08a7c","block_id":"blk_bb16df82-178e-407a-a250-e3523fa15206","section_id":"sec_c6ae7401-b397-4aa7-acee-af8be2a1182c","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":33,"end":40,"exact":"KVキャッシュ","quote":"- 複数カメラの映像\n- 過去の視覚特徴\n- サブタスク履歴\n- KVキャッシュ\n- 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\n**Inference**  \n**AI Factory**  \n**各国政府によるAIインフラ投資**","quote_start":0,"quote_end":68,"text_sha256":"493831350b23ee7d2617610a9eb1d95eceb42ff94b918fc13fe64bbaa25a7896","block_sha256":"493831350b23ee7d2617610a9eb1d95eceb42ff94b918fc13fe64bbaa25a7896","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_157ef6d2-69fe-48f2-9940-db00dbbbc5b6/#blk_1fb57922-38b5-4d1b-9ac3-a18ababa190e"},{"id":"occ_0387841dcb71daa711f6ac0c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_157ef6d2-69fe-48f2-9940-db00dbbbc5b6","work_id":"wrk_cf72029e-9466-4ece-9543-d960f25780ea","block_id":"blk_87f19fe9-3079-490c-9db8-c51e0cb58f6a","section_id":"sec_5a7e9e0c-9f9e-4c7e-849d-da46fefe9fd6","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":65,"end":74,"exact":"Inference","quote":"mp**  \n**HBM4**  \n**NVL systems**  \n**Networking**  \n**Inference demand**  \n**2027/28 visibility**","quote_start":10,"quote_end":108,"text_sha256":"3487da9276ba38f32af5b0ec6e4cd27b0a49948c3c2223c704a8f05cd9b5055c","block_sha256":"3487da9276ba38f32af5b0ec6e4cd27b0a49948c3c2223c704a8f05cd9b5055c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_157ef6d2-69fe-48f2-9940-db00dbbbc5b6/#blk_87f19fe9-3079-490c-9db8-c51e0cb58f6a"},{"id":"occ_51033f3c32ff925404ba797f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_01f95bb3-acaa-46bb-b91a-1198d6d3a69f","section_id":"sec_f7f7d5d0-ccb3-4254-85ec-77fba0681b01","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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cache、小ブロック読み出しをGPU近傍に置くための、HBMの外側の高速大容量階層である。","quote_start":34,"quote_end":138,"text_sha256":"c6d5a6870b897c6ac3cd8563d15d617382701e6bc913eda3fe4184dff08f61cc","block_sha256":"c6d5a6870b897c6ac3cd8563d15d617382701e6bc913eda3fe4184dff08f61cc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_01f95bb3-acaa-46bb-b91a-1198d6d3a69f"},{"id":"occ_eeccd927f2f147418732d48e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_0203e890-9e36-4e31-81de-01720389e386","section_id":"sec_c4e9f6b6-c1d5-4227-839e-6b8fc2cc4f9f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":124,"end":126,"exact":"推論","quote":"IMM、SOCAMM、eSSD、HBF、XL-FLASHは、余った時に買えばよい部品ではなく、GPUクラスタ、推論サービス、AIストレージ、RAG基盤の稼働率を決める戦略部材になっている。","quote_start":69,"quote_end":163,"text_sha256":"4e35909f31d5c22d149bd1fb354c915fc332952a2afd9c1736690f2590a798f4","block_sha256":"4e35909f31d5c22d149bd1fb354c915fc332952a2afd9c1736690f2590a798f4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_0203e890-9e36-4e31-81de-01720389e386"},{"id":"occ_3b028cd78590dfe38e22a4c3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_12171bac-943e-45d6-8b1b-5340fc3765ec","section_id":"sec_bd8c88ab-6462-483e-a8e6-a8d9a1489083","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"不足継続シナリオ\nQLC eSSD、HDD代替、AI推論、RAG、HBF/XL-FLASHが同時に伸びる。","quote_start":0,"quote_end":53,"text_sha256":"e75bca79a3d204bd47f7b5d48555eb925108d734add57fe176a30cdf8b6c9803","block_sha256":"e75bca79a3d204bd47f7b5d48555eb925108d734add57fe176a30cdf8b6c9803","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_12171bac-943e-45d6-8b1b-5340fc3765ec"},{"id":"occ_0934acd3b1cf15867a5db1b4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_1eadad72-3669-43f4-b8d3-70e290c56000","section_id":"sec_b0718de1-321b-48fe-a9e5-82a6a031bcc1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":527,"end":529,"exact":"推論","quote":" QLC比8〜20倍 | 初期量産 | 標準化なら拡大 | 容量ではなくIOPS単価 |\n| HBF | AI推論用高速Flash階層 | QLC比10〜30倍 | サンプル段階 | 採用次第で急拡大 | HBM補完層 |","quote_start":472,"quote_end":583,"text_sha256":"0cf16416848feb1e741931cd5914aa38e5316a8c4013dceaf0415372dfdb15b8","block_sha256":"0cf16416848feb1e741931cd5914aa38e5316a8c4013dceaf0415372dfdb15b8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_1eadad72-3669-43f4-b8d3-70e290c56000"},{"id":"occ_e76080a306f84e5150f2004e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_1eeee5ce-1278-4b7c-8841-1a0620bfd78e","section_id":"sec_7f61f3c0-f804-4efc-b616-f9dce5d8a9c6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":157,"end":159,"exact":"推論","quote":"LC/QLC eSSD、XL-FLASH、HBF |\n| 最大の利益源 | HBMとサーバーDRAM | AI推論ストレージと高付加価値NAND |\n| 上振れ規模 | Samsung/SKは4〜5兆ドル候補 | Kioxiaは1兆ドル候補、SanDiskはやや強気条件が必要 |\n| 主なリスク | DRAM/H","quote_start":102,"quote_end":259,"text_sha256":"ef04a458decaa56b3651e5bb3a5ea628f2432b6cbb38e014b8d01363a90e287b","block_sha256":"ef04a458decaa56b3651e5bb3a5ea628f2432b6cbb38e014b8d01363a90e287b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_1eeee5ce-1278-4b7c-8841-1a0620bfd78e"},{"id":"occ_10695549fde9b7899ba013db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_27593fa9-e84e-4b11-8c6e-99ebb445611e","section_id":"sec_e57b77a5-b2fd-4f70-9f0a-faf92937618a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":1362,"end":1364,"exact":"推論","quote":"] SanDiskはHBFについて、2026年後半にHBFメモリの初期サンプル、2027年初めにHBF搭載AI推論デバイスのサンプルを目指すと説明しています。(Sandisk)\n[M] KioxiaとNVIDIAは、AIサーバー向けに2027年100 million IOPS級SSDを目指していると報じられ、XL","quote_start":1307,"quote_end":1464,"text_sha256":"15d6618389dcfc41452d78162e1da65e9ddd9a17ed9dabcbdd70447d76e630e7","block_sha256":"15d6618389dcfc41452d78162e1da65e9ddd9a17ed9dabcbdd70447d76e630e7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_27593fa9-e84e-4b11-8c6e-99ebb445611e"},{"id":"occ_a650461eeb2c0ce7aaaec0bc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_35c35bed-bca5-4562-a229-10781c87292d","section_id":"sec_b71906f7-9849-4209-8f85-69360862d6f0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"ここで整理すると、メモリ御三家はHBMとサーバーDRAM、Kioxia/SanDiskはAI推論ストレージと高付加価値NANDという役割分担になる。","quote_start":0,"quote_end":74,"text_sha256":"29e72b46876cb966da34ab73fefed212f170f0bc3e7e802ccd04c20915607fa8","block_sha256":"29e72b46876cb966da34ab73fefed212f170f0bc3e7e802ccd04c20915607fa8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_35c35bed-bca5-4562-a229-10781c87292d"},{"id":"occ_1f6aa18ac7e5d5765df942d7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_698ad902-86d4-4bba-bb94-74a7c9b5d9e9","section_id":"sec_f7f7d5d0-ccb3-4254-85ec-77fba0681b01","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"NAND側でも、AI推論、RAG、KV cache、ベクトルDB、ログ、チェックポイント、HDD代替によって、エンタープライズSSD需要が高付加価値化している。","quote_start":0,"quote_end":80,"text_sha256":"52cc17838ea69f9cfa90a9ac3491915d7381e1652915b60d5ee20180eb615f25","block_sha256":"52cc17838ea69f9cfa90a9ac3491915d7381e1652915b60d5ee20180eb615f25","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_698ad902-86d4-4bba-bb94-74a7c9b5d9e9"},{"id":"occ_7b3aa0bc74bddbf095daa6b9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_76dff0c2-d3ca-4d78-b154-ccd3744c10e4","section_id":"sec_41bae2ab-2bf6-443b-9962-4d1083fe8935","layer":"character","character_id":"zetu_noia","count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":94,"end":96,"exact":"推論","quote":"があり、その外側にサーバーDRAMがあり、さらにeSSD、QLC NAND、HBF、XL-FLASHが広がる。推論が長文化し、エージェントが履歴を持ち、RAGが企業データを抱えるほど、記憶の階層は深くなります。","quote_start":39,"quote_end":144,"text_sha256":"ab1c3953d45ee4e1f4155068f87bd6060d2d161adc2b56212352c62b4403ecf6","block_sha256":"ab1c3953d45ee4e1f4155068f87bd6060d2d161adc2b56212352c62b4403ecf6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_76dff0c2-d3ca-4d78-b154-ccd3744c10e4"},{"id":"occ_f56cd7f90bd09a86536a6952","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_7727e126-3da9-4bd5-8e69-64206c343f6a","section_id":"sec_b0718de1-321b-48fe-a9e5-82a6a031bcc1","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":86,"end":94,"exact":"KV cache","quote":"は演算直結の超低レイテンシDRAMである。\nHBF/XL-FLASHは、モデル重み、巨大ベクトルDB、RAG、KV cache、頻繁な小ブロック読み出しをGPU近傍に置くための「HBMの外側の高速大容量階層」である。","quote_start":31,"quote_end":139,"text_sha256":"bc17269a71c2241c8ee7592e7ced285402c5115e30e5a78626986559ba9c094f","block_sha256":"bc17269a71c2241c8ee7592e7ced285402c5115e30e5a78626986559ba9c094f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_7727e126-3da9-4bd5-8e69-64206c343f6a"},{"id":"occ_7587b3958c2ad4055d6660f6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_77d0549a-7a26-4076-908f-e1fdd5c6e3f1","section_id":"sec_e57b77a5-b2fd-4f70-9f0a-faf92937618a","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"端DRAMウェハを大量に消費する。\nAIサーバーDRAMはGPU/ASIC増加とともに伸びる。\neSSDはAI推論、RAG、KV cache、HDD代替で伸びる。\nNOR/SLC NANDは成熟品撤退により構造不足化している。\nHBF/XL-FLASHは、HBMの外側に新しい高速大容量メモリ階層を作る可能性がある","quote_start":5,"quote_end":162,"text_sha256":"ccda25410ecec8be0a6fd74d78ae7561ea8dc0daa13edba5253bbf7cd900e643","block_sha256":"ccda25410ecec8be0a6fd74d78ae7561ea8dc0daa13edba5253bbf7cd900e643","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_77d0549a-7a26-4076-908f-e1fdd5c6e3f1"},{"id":"occ_45cf018e7f8514c50362857d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_88ab4f7f-ae61-4c72-bdaa-7742029f7319","section_id":"sec_b0718de1-321b-48fe-a9e5-82a6a031bcc1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"SanDiskはHBFについて、2026年後半にHBFメモリの初期サンプル、2027年初めにHBF搭載AI推論デバイスの初期サンプルを目指している[L]。Kioxia側では、NVIDIAと組んだ100 million IOPS級AI SSD構想が報じられており、XL-FLASHやHBF的な構造が候補として論じ","quote_start":0,"quote_end":155,"text_sha256":"84c205d71890f0ec8963dc4d85ebac895993f598a5662b82d995ccd55bc0d44b","block_sha256":"84c205d71890f0ec8963dc4d85ebac895993f598a5662b82d995ccd55bc0d44b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_88ab4f7f-ae61-4c72-bdaa-7742029f7319"},{"id":"occ_39d5482df163d2a3f41934d3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_901273b8-e4fa-41b4-9dd4-bd817b9a2ccb","section_id":"sec_0c733f99-60cb-4cda-aa9e-532835f7a262","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"まず、データセンターで主に使われるのはTLC/QLC NANDを用いたエンタープライズSSDである。AI推論、RAG、ベクトルDB、KV cache、ログ、チェックポイント保存などによって、NAND需要は急速に高付加価値化している。TrendForceは、2026年Q1の上位5社NAND売上が前四半期比83","quote_start":0,"quote_end":154,"text_sha256":"16e1f88ed3370cc370d4bb889468c6289106b0ee54a8e2b18530bbbd1e7a1bc2","block_sha256":"16e1f88ed3370cc370d4bb889468c6289106b0ee54a8e2b18530bbbd1e7a1bc2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_901273b8-e4fa-41b4-9dd4-bd817b9a2ccb"},{"id":"occ_b52bfa60fba671b3c444ca01","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_ad9fd3d7-a927-49e9-b533-c63c672ad7c6","section_id":"sec_0c733f99-60cb-4cda-aa9e-532835f7a262","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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政治リスク、反トラスト、データ","quote_start":93,"quote_end":250,"text_sha256":"a7af79c2f9208ef53b82e45d800de4ac8be501707a0b6a092ee5059799dd3848","block_sha256":"a7af79c2f9208ef53b82e45d800de4ac8be501707a0b6a092ee5059799dd3848","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17972011-3c15-4377-a2ea-f36af8fa6c2f/#blk_deffc262-8057-4c39-8f48-f7ffb1c19c5b"},{"id":"occ_b1d63cabe44a327dcbef595d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17972011-3c15-4377-a2ea-f36af8fa6c2f","work_id":"wrk_fbc5cb25-a3fe-4368-a659-4a3d7a753309","block_id":"blk_f1879b6b-b519-499d-ac6e-7f66a04460b3","section_id":"sec_b71906f7-9849-4209-8f85-69360862d6f0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":154,"end":156,"exact":"推論","quote":"LC/QLC eSSD、XL-FLASH、HBF |\n| 最大の利益源 | 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Cloud Edgeも、5G CoreやRAN機能をエッジで動かし、コンピュータビジョンやAIエッジ推論のようなミッションクリティカル用途に対応する方向です。([services.google.com](https://services.google.com/fh/files/misc/gdce_dat","quote_start":14,"quote_end":171,"text_sha256":"9ad045d0ec1c46b534e543d2ac7c07543bc42fd7f95649227d99ec82f2ba2c6c","block_sha256":"9ad045d0ec1c46b534e543d2ac7c07543bc42fd7f95649227d99ec82f2ba2c6c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f/#blk_606cc276-a5d4-45d5-80bb-1537e32593a9"},{"id":"occ_3b4bb70934fac18c104d11fa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f","work_id":"wrk_f7d6728c-f58d-4c77-958d-67d9910c0d5d","block_id":"blk_739b3c5e-14b1-4946-80af-7692642999d6","section_id":"sec_3ef3affb-74c0-4563-8b69-7f4761d1d245","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"AI-RANの価値は、通信速度だけではなく、**予測可能な上り、低遅延、移動中の安定性、現場AI推論、セキュアな実機制御**にあります。フィジカルAI時代に無線が重要になる理由はここです。人間のインターネットでは「動画が止まらないこと」が価値でしたが、フィジカルAIのインターネットでは、**機械が安","quote_start":0,"quote_end":150,"text_sha256":"802502b9e7848621475c9580a6df612f4df72958e2bdae0857c5e8810666e6a3","block_sha256":"802502b9e7848621475c9580a6df612f4df72958e2bdae0857c5e8810666e6a3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f/#blk_739b3c5e-14b1-4946-80af-7692642999d6"},{"id":"occ_ca56dfc1dae6d81c3c135dd6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f","work_id":"wrk_f7d6728c-f58d-4c77-958d-67d9910c0d5d","block_id":"blk_81cca926-77ff-4899-a83e-964b3a84aefa","section_id":"sec_5a9ed6c6-b043-44dc-a505-93b832042ee5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"フィジカルAI時代において重要になるのは、単に「どのAIが賢いか」ではなく、 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 \nSiemensとNVIDIAも、Industrial AI、デジタ","quote_start":11,"quote_end":168,"text_sha256":"7272a5b90b90a5a5dd7e76f102f6097e3c9a3388e74e5a32a61491e4df4ace26","block_sha256":"7272a5b90b90a5a5dd7e76f102f6097e3c9a3388e74e5a32a61491e4df4ace26","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f/#blk_95169759-45fb-432c-9a1f-31560c54ca37"},{"id":"occ_02ac7f7d3582e10ee1f4ff5c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f","work_id":"wrk_f7d6728c-f58d-4c77-958d-67d9910c0d5d","block_id":"blk_9971eeb7-e327-4fe4-a7f3-cc9b184b8cdf","section_id":"sec_2cb837ad-88d5-4e62-969a-0e1e84cd8ecd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"AI-RANはその先です。  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Stop Systems](https://onestopsystems.com/?srsltid=AfmBOoqPPJ99yYHwdjZ91Fu5OF2UW0-A","quote_start":31,"quote_end":188,"text_sha256":"b803fb717bb860098e095f1bdf4fe22fa401ab01446be2724aeaf8e02673d7dd","block_sha256":"b803fb717bb860098e095f1bdf4fe22fa401ab01446be2724aeaf8e02673d7dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f/#blk_9a39e6c1-d5d9-4084-b5b2-5ecc750ac259"},{"id":"occ_58c783a42ded47498b6a94fe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f","work_id":"wrk_f7d6728c-f58d-4c77-958d-67d9910c0d5d","block_id":"blk_d5f6f413-a564-4c15-b226-b122ea52b055","section_id":"sec_5a9ed6c6-b043-44dc-a505-93b832042ee5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":90,"end":92,"exact":"推論","quote":"きな利益を取りやすいのは、依然として「計算資源と脳側」です。基盤モデル、GPU、AIアクセラレータ、クラウド、推論基盤、AIソフトウェアは強い。一方で、物理世界にAIを下ろすには、通信、無線、MEC、センサー、IoT、実機管理、PQC、堅牢ハードウェアが不可欠になります。","quote_start":35,"quote_end":171,"text_sha256":"0dd9745f83d455a7d446fe3fb6151ce4ed31ab2c8b8c927a41da9de01da37bc4","block_sha256":"0dd9745f83d455a7d446fe3fb6151ce4ed31ab2c8b8c927a41da9de01da37bc4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_17f0f08f-aee1-41a1-8c07-82badf2b6a4f/#blk_d5f6f413-a564-4c15-b226-b122ea52b055"},{"id":"occ_605c81256d98f5fa3482ac3c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_00c59658-cd41-43dc-9e2e-086613767749","section_id":"sec_a914f50d-e21f-48fb-b046-8acde446bbc3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"の低さは、計算資源の制約から考えると、ある意味では救いでもある。  \nもし全員が一斉に作業AIを使い始めれば、推論需要は一気に膨張する。","quote_start":6,"quote_end":74,"text_sha256":"42af8e0b600e0e0afb58d7a661e55ea15bb0ff293f77a775aaf93f3438a6edbd","block_sha256":"42af8e0b600e0e0afb58d7a661e55ea15bb0ff293f77a775aaf93f3438a6edbd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_00c59658-cd41-43dc-9e2e-086613767749"},{"id":"occ_47c5fdf5bc7ffef05a1a0615","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_01709892-66b5-4c07-958f-4f16c1c24d19","section_id":"sec_ee554b35-ac53-453b-addf-68db2433083b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":77,"end":79,"exact":"推論","quote":"\n重い部分は事前生成する。  \nよく使う素材はキャッシュする。  \n軽い反応はローカルAIで行う。  \n重要な推論だけクラウド高性能モデルに投げる。  \nゲームの基本構造は人間が作り、NPC会話やクエストだけAIが生成する。  \n映像の大枠は事前に作り、細部だけ個人化する。  \n音声や字幕はリアルタイムにし、映像","quote_start":22,"quote_end":179,"text_sha256":"0ca9f86164bdb18488ae41ec38fb993d256c59c5feae2491692e8f18a485e296","block_sha256":"0ca9f86164bdb18488ae41ec38fb993d256c59c5feae2491692e8f18a485e296","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_01709892-66b5-4c07-958f-4f16c1c24d19"},{"id":"occ_fcdf5b706332efe5e3dc5bfc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_0f5ba6f0-67d6-4e97-830c-a31b7a16eb6c","section_id":"sec_0099cd34-26a9-4899-a392-46900c920264","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"日常補助をどこまでローカルで行うか。  \n高性能推論をどこまでクラウドに残すか。  \n作業AIをどれだけ安全に普及させるか。  \nAIエージェントの計算需要をどう支えるか。  \nAIのためのAIが生む膨大な推論需要をどう処理するか。","quote_start":0,"quote_end":116,"text_sha256":"c9679da8a88b3d31f07978cc55cda6a52782de3ffae0be82bc239c8875ba0853","block_sha256":"c9679da8a88b3d31f07978cc55cda6a52782de3ffae0be82bc239c8875ba0853","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_0f5ba6f0-67d6-4e97-830c-a31b7a16eb6c"},{"id":"occ_68b5169fdc7342a670e6b2cc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_85a095ec-aefd-4e38-a01f-a3c1b776fa46","section_id":"sec_fa8b0032-6beb-450b-9e12-60eaa98097ee","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"し続ける、ユーザーの好みに合わせて長時間の映像体験を生成し続ける、といった用途は重い。  \nこれはクラウド側の推論コスト低下、動画モデルの軽量化、専用アクセラレータ、キャッシュ、事前生成、ローカル処理との分担が必要になる。  \n一般層に安価に広がるには、5年から10年程度の時間がかかる可能性がある。","quote_start":16,"quote_end":166,"text_sha256":"fd27aacbc345983f5d6d88286ff997df5d8088f40f30f019a06fe12c1fee0892","block_sha256":"fd27aacbc345983f5d6d88286ff997df5d8088f40f30f019a06fe12c1fee0892","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_85a095ec-aefd-4e38-a01f-a3c1b776fa46"},{"id":"occ_c6ff8eaf8017e5c1add2aaee","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_c4a4c3f7-cfa0-4969-a3a1-347b97615c7c","section_id":"sec_d4057004-57b7-46d1-9942-08462d6fd67f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":65,"end":67,"exact":"推論","quote":"は計算資源に対して収益効率が悪いかもしれない。  \nAI動画、AIゲーム、AIキャラ、AI仮想世界は、クラウド推論を大量に使う。  \n人間の支払い能力には限界がある。  \nそのため、体験型AIだけで巨大な計算資源を正当化するのは難しい可能性がある。","quote_start":10,"quote_end":134,"text_sha256":"be1ab9fb4cd7ab63b52c728a2dd3a533de9bc1d1e6b255fd0af4b8955f7aca7e","block_sha256":"be1ab9fb4cd7ab63b52c728a2dd3a533de9bc1d1e6b255fd0af4b8955f7aca7e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_c4a4c3f7-cfa0-4969-a3a1-347b97615c7c"},{"id":"occ_2cc7bec2673632116b095b86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_dfd9134f-a4e3-4b5e-bc4e-59761be6c0ec","section_id":"sec_a3af70ea-62e7-4163-b61a-9c23bf52cf19","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":16,"end":18,"exact":"推論","quote":"一つの依頼が、裏側では何十回もの推論に分解される。","quote_start":0,"quote_end":25,"text_sha256":"d0c714dd75fc239a0d176e04ac080962f0ad52e869e245d940757c4c048c0498","block_sha256":"d0c714dd75fc239a0d176e04ac080962f0ad52e869e245d940757c4c048c0498","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_dfd9134f-a4e3-4b5e-bc4e-59761be6c0ec"},{"id":"occ_7df9b25734ebfb9792a18b5b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d","work_id":"wrk_e8dd57e9-048c-4852-81d1-2a029a08ac21","block_id":"blk_e9a9c719-190b-4c70-9a47-fe39af02845c","section_id":"sec_0099cd34-26a9-4899-a392-46900c920264","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"チャットAIの一部はローカルへ行く。  \nしかし、作業AI、クラウド検索、高度推論、企業AI、物理AI、AIのためのAIは、クラウド需要をむしろ膨らませる。","quote_start":0,"quote_end":78,"text_sha256":"f0c4a70f9e4c35d6dfa3bf50c85f678b66c4a3fdea9c0c3ccf609432b7f5cef7","block_sha256":"f0c4a70f9e4c35d6dfa3bf50c85f678b66c4a3fdea9c0c3ccf609432b7f5cef7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_19d3384e-c9c8-4b8b-95ae-a78bbc4dfb8d/#blk_e9a9c719-190b-4c70-9a47-fe39af02845c"},{"id":"occ_bfb4054e7fc36698ee63bebf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_1c3b81f6-696c-4d69-acf7-ad589032ce63","work_id":"wrk_34a8dbe0-b5df-4af3-9515-dede6c8eb815","block_id":"blk_2eeea479-b418-4ea7-b3f5-a1b609ba5cbb","section_id":"sec_c0abf203-a4a4-4167-83ff-2ad4ad698a25","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":114,"end":116,"exact":"推論","quote":"\n- **メリット:** DSPがないため、消費電力を約50%削減でき、レイテンシ（遅延）も大幅に下がる。AI推論などの低遅延が求められる用途に最適。\n- **Coherentの戦略:** LPOには極めて線形性（Linearity）の高いレーザーが必要となる。Coherentは自社の高品質なEMLやVCSELが","quote_start":59,"quote_end":216,"text_sha256":"7623bb8b85e965c719bf3957055229991996f8c8da7be1895b9b3d4caacae333","block_sha256":"7623bb8b85e965c719bf3957055229991996f8c8da7be1895b9b3d4caacae333","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_1c3b81f6-696c-4d69-acf7-ad589032ce63/#blk_2eeea479-b418-4ea7-b3f5-a1b609ba5cbb"},{"id":"occ_3ba72c44679834044e9e49b0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_1c3b81f6-696c-4d69-acf7-ad589032ce63","work_id":"wrk_34a8dbe0-b5df-4af3-9515-dede6c8eb815","block_id":"blk_3a8029f0-a96a-4781-a5be-0dc38dc8a752","section_id":"sec_9a696aa4-d6a9-468c-9719-e8b033c067ff","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":87,"end":89,"exact":"推論","quote":"性、および市場展望について、徹底的な調査に基づき詳述する。特に、生成AI（Generative AI）の学習・推論インフラにおける800G/1.6T光トランシーバーの役割と、ロボティクスや自律システム（フィジカルAI）におけるセンシング技術の重要性に焦点を当てる。また、光接続技術の未来を左右するPluggable","quote_start":32,"quote_end":189,"text_sha256":"664d1498460225871473046c151d6a01f60a30e458aa7085d9f646c41397f9ea","block_sha256":"664d1498460225871473046c151d6a01f60a30e458aa7085d9f646c41397f9ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_1c3b81f6-696c-4d69-acf7-ad589032ce63/#blk_3a8029f0-a96a-4781-a5be-0dc38dc8a752"},{"id":"occ_098f0de13840bddcec96b3e6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_0434ea12-7ac0-4f37-a14e-de56cfe4fb57","section_id":"sec_bd1d406b-6147-4edc-b9b4-183bfcf5f76d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"次のAIDCは、  \nAIを推論する工場になった。","quote_start":0,"quote_end":25,"text_sha256":"3df5d5252eec24615766e010b232802ee93e8ebd585b435fd3cd50655930e55c","block_sha256":"3df5d5252eec24615766e010b232802ee93e8ebd585b435fd3cd50655930e55c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_0434ea12-7ac0-4f37-a14e-de56cfe4fb57"},{"id":"occ_bee691248487a130bea6ae72","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_0ae00d06-0430-475e-b6ec-1bcb24bece13","section_id":"sec_cddcd6d5-1d2c-4906-99e5-e0594c1f5e08","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":54,"end":56,"exact":"推論","quote":"これまでの生成AIは、巨大なGPUクラスタにモデルを置けばよかった。  \nユーザーが入力する。  \nモデルが推論する。  \n結果を返す。  \n非常に単純化すれば、","quote_start":0,"quote_end":81,"text_sha256":"761ca5e310e26e647aefb290e8aeaaeae2c3655ec501183b7f6120174d4f8a50","block_sha256":"761ca5e310e26e647aefb290e8aeaaeae2c3655ec501183b7f6120174d4f8a50","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_0ae00d06-0430-475e-b6ec-1bcb24bece13"},{"id":"occ_3ce5ae45fe98e4240b1b34b0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_0f4a0e3f-0acf-4477-8f6e-ee12f5dbc56e","section_id":"sec_47014f88-dad9-4ff7-af76-c7902f7d63bf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"あるAgentはモデルの推論結果を待ち、あるAgentはWebサイトからの応答を待ち、あるAgentはファイルを書き込み、あるAgentはユーザーから次の指示を待っている。","quote_start":0,"quote_end":86,"text_sha256":"6617f658cb2fdf5207ba92d75908c39966d4194b0cee4732099116573238b5fa","block_sha256":"6617f658cb2fdf5207ba92d75908c39966d4194b0cee4732099116573238b5fa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_0f4a0e3f-0acf-4477-8f6e-ee12f5dbc56e"},{"id":"occ_2c8173e45b52931f5e1314ea","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_24fdc059-4493-4f9e-b886-960aa2aad4a6","section_id":"sec_688a89bf-4713-4715-9d7f-061a02f4ff68","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"モデル推論を待つ。  \nネットワークを待つ。  \nAPIを待つ。  \n人間を待つ。","quote_start":0,"quote_end":41,"text_sha256":"e85f355e7180c6e2e6a6a69c214f125cd236834a680e5c66b2a1368edb28f140","block_sha256":"e85f355e7180c6e2e6a6a69c214f125cd236834a680e5c66b2a1368edb28f140","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_24fdc059-4493-4f9e-b886-960aa2aad4a6"},{"id":"occ_575768e57dcbb01fd82ca523","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_322690ce-3aef-4209-866b-05d8a842854b","section_id":"sec_a97c7af2-1f1d-400f-af9d-578efa7883eb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"ここには推論用GPU、TPU、AI ASIC、HBMを含めていない。","quote_start":0,"quote_end":34,"text_sha256":"ef0b3663ba452a63277c679c27c5ce005e61827fb51e4ecf55dbde68631ef369","block_sha256":"ef0b3663ba452a63277c679c27c5ce005e61827fb51e4ecf55dbde68631ef369","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_322690ce-3aef-4209-866b-05d8a842854b"},{"id":"occ_dff0c416b02892343b89529c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_669c9861-b955-4c39-83ca-1cb894f94028","section_id":"sec_b69e23b9-914d-41e0-88c3-778073bca546","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":39,"end":46,"exact":"Prefill","quote":"すべてを一種類のGPUで処理するのではなく、  \nTraining向け。  \nPrefill向け。  \nDecode向け。  \nEmbedding向け。  \nVideo向け。  \nNetwork向け。","quote_start":0,"quote_end":100,"text_sha256":"37d2a2f8ebdee8c006328c6e4eae27f52d53217501ee1aa87aaf2678ed85bbeb","block_sha256":"37d2a2f8ebdee8c006328c6e4eae27f52d53217501ee1aa87aaf2678ed85bbeb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_669c9861-b955-4c39-83ca-1cb894f94028"},{"id":"occ_02765c3700c357ba97b0f2d9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_6e18d98e-441d-48ee-9d63-d1058b708742","section_id":"sec_cddcd6d5-1d2c-4906-99e5-e0594c1f5e08","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":22,"end":24,"exact":"推論","quote":"しかしAgentでは違う。  \nAgentは推論した後に、  \nファイルを読む。  \nコードを書く。  \nブラウザを開く。  \nアプリを操作する。  \nビルドする。  \nテストする。  \n失敗したらやり直す。","quote_start":0,"quote_end":104,"text_sha256":"91532b7fb06e2c4f7eaa0f19b70947385963dd6ed997390cfeb02dfe3601d3ea","block_sha256":"91532b7fb06e2c4f7eaa0f19b70947385963dd6ed997390cfeb02dfe3601d3ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_6e18d98e-441d-48ee-9d63-d1058b708742"},{"id":"occ_7a8df143a9fe6649b81f8b5b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_20f6e19f-c172-4e63-83ab-04729ac80c82","work_id":"wrk_ae883f23-4159-4408-941d-addc931dbce4","block_id":"blk_a8754a64-fb9b-4888-98aa-9dca202f92b3","section_id":"sec_bd1d406b-6147-4edc-b9b4-183bfcf5f76d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":140,"end":142,"exact":"推論","quote":"固有処理 → Windows VM  \nApple固有処理 → macOS / physical Mac  \n推論 → GPU / TPU / AI ASIC  \nという分業になる。","quote_start":85,"quote_end":176,"text_sha256":"ebf208ff19a0e3227de9c0aefab85528b7a5edf2d49a8682e07cc29395c4f51e","block_sha256":"ebf208ff19a0e3227de9c0aefab85528b7a5edf2d49a8682e07cc29395c4f51e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_20f6e19f-c172-4e63-83ab-04729ac80c82/#blk_a8754a64-fb9b-4888-98aa-9dca202f92b3"},{"id":"occ_d6c05c58111d76f883729dc8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_282f1fea-deeb-4d6f-9a0c-8692cfb1a953","work_id":"wrk_a7c25c0a-59d8-42ea-add0-0089b799d6c2","block_id":"blk_3ab10b8c-7763-4706-90eb-df13559ddb9b","section_id":"sec_adcca332-bdcd-4310-b209-2ef093d7bf8b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"### 8-2. 「トレーニングプロセスの内容（推論過程や判断根拠を含むパラメータ設定等）」の概要公開","quote_start":0,"quote_end":51,"text_sha256":"fa474636d822299ee621bc7b5d7f1dd830ed81748d8526ebc350f2d4a3a4f06a","block_sha256":"fa474636d822299ee621bc7b5d7f1dd830ed81748d8526ebc350f2d4a3a4f06a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_282f1fea-deeb-4d6f-9a0c-8692cfb1a953/#blk_3ab10b8c-7763-4706-90eb-df13559ddb9b"},{"id":"occ_b3c7527d42551207766fcd93","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_282f1fea-deeb-4d6f-9a0c-8692cfb1a953","work_id":"wrk_a7c25c0a-59d8-42ea-add0-0089b799d6c2","block_id":"blk_e68c4d3e-2c77-48d8-98bd-f0f7cd1c96ac","section_id":"sec_7b55ec9d-28bb-4f97-b246-db3bd52847d5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":102,"end":104,"exact":"推論","quote":"設計仕様（ライセンス状況、必要HW/SW等）\n- 利用規定（用途・禁止用途）\n- トレーニングプロセスの内容（推論過程や判断根拠を含むパラメータ設定等）","quote_start":47,"quote_end":123,"text_sha256":"dd37e713324053837d5bd9cdc7c6aca9145495d1bf8cd3ac1ed478e659d20f51","block_sha256":"dd37e713324053837d5bd9cdc7c6aca9145495d1bf8cd3ac1ed478e659d20f51","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_282f1fea-deeb-4d6f-9a0c-8692cfb1a953/#blk_e68c4d3e-2c77-48d8-98bd-f0f7cd1c96ac"},{"id":"occ_63d1252c8218532d1598fd6a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_28e52d32-205e-4601-9fe8-535cce2d0600","work_id":"wrk_012e50f2-e203-488b-b14f-8859b652eece","block_id":"blk_49274f61-d8de-431f-8a48-aced937ae02d","section_id":"sec_df8e04b9-41db-49af-87b7-d4036cd8baec","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":552,"end":554,"exact":"推論","quote":"す。  \n**含意**：920は、対中仕様H20などの不足で生じたギャップを**用途分化（PR/DT）で埋め、推論～一部学習を国内で自走**させるための中核製品として機能します。","quote_start":497,"quote_end":586,"text_sha256":"f6fa004b4cc860a7453346685ffab556ebc5e57e1908032da2ef84d4b95a84f2","block_sha256":"f6fa004b4cc860a7453346685ffab556ebc5e57e1908032da2ef84d4b95a84f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_28e52d32-205e-4601-9fe8-535cce2d0600/#blk_49274f61-d8de-431f-8a48-aced937ae02d"},{"id":"occ_c301cac3ff8289cb7c2bc677","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_28e52d32-205e-4601-9fe8-535cce2d0600","work_id":"wrk_012e50f2-e203-488b-b14f-8859b652eece","block_id":"blk_5474a4af-faa4-4189-8f8d-43ddebba31a1","section_id":"sec_5e4153fe-28e0-44ae-ae76-db149bddb784","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":44,"end":46,"exact":"推論","quote":"- **短期**：正規・準正規の**NVIDIAルートの不確実性**が一段と高まり、**推論寄り案件はAscend 910C/920＋国産クラスタ**への置換が加速します。**港湾検査強化に伴うリードタイム増**は、**データセンターのキャパ拡張計画**にも影響します。[ファイナンシャル・タ","quote_start":0,"quote_end":146,"text_sha256":"d6c8173d0f330357852e402b86fb70f7fcf930af4bd19e2c8c218cc0c932cab1","block_sha256":"d6c8173d0f330357852e402b86fb70f7fcf930af4bd19e2c8c218cc0c932cab1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_28e52d32-205e-4601-9fe8-535cce2d0600/#blk_5474a4af-faa4-4189-8f8d-43ddebba31a1"},{"id":"occ_c8419b45528189a2e958ec1b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_28e52d32-205e-4601-9fe8-535cce2d0600","work_id":"wrk_012e50f2-e203-488b-b14f-8859b652eece","block_id":"blk_9f5e121c-9d80-4c39-990f-130c7b2db4a8","section_id":"sec_b210d9cd-0e19-4a76-8fe4-35d8aa037366","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"短期的には、**910C／920とCloudMatrix（またはAtlas）構成**で国内の相当部分の推論需要を消化できる見通しです。完全にH100／H20を代替するのは難しいものの、**“国内で回るAI推論インフラ”としては十分に機能し始めています。 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920**は、**6nm／4TB/s級*","quote_start":0,"quote_end":122,"text_sha256":"a59486f21b90d71bef1abf1ce929630ac57981bbfaf32704bacd996fdd9772f3","block_sha256":"a59486f21b90d71bef1abf1ce929630ac57981bbfaf32704bacd996fdd9772f3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_28e52d32-205e-4601-9fe8-535cce2d0600/#blk_ce6a38da-3fde-4dd9-8e22-fce68dc4e7e6"},{"id":"occ_eb65d72926a1d88487a55855","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_28e52d32-205e-4601-9fe8-535cce2d0600","work_id":"wrk_012e50f2-e203-488b-b14f-8859b652eece","block_id":"blk_edeef8fd-7eb0-4632-b8a9-fea78ba69775","section_id":"sec_b210d9cd-0e19-4a76-8fe4-35d8aa037366","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"### 「Huaweiだけで推論需要を満たせるか？」への暫定答","quote_start":0,"quote_end":31,"text_sha256":"7e29c535193d9b5a230a58f663498671005f24ff2c5b94d61b8daad65d684bc7","block_sha256":"7e29c535193d9b5a230a58f663498671005f24ff2c5b94d61b8daad65d684bc7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_28e52d32-205e-4601-9fe8-535cce2d0600/#blk_edeef8fd-7eb0-4632-b8a9-fea78ba69775"},{"id":"occ_8f1c5188cf529ff2e2c72579","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_28e52d32-205e-4601-9fe8-535cce2d0600","work_id":"wrk_012e50f2-e203-488b-b14f-8859b652eece","block_id":"blk_f7d8752f-9e2c-4397-86c7-6b479690b315","section_id":"sec_01da28b3-c6fb-4ce2-9ba6-f3cd09878259","layer":"body","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":219,"end":221,"exact":"推論","quote":"想定しています。NVIDIA 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\n社外検証や大手プラットフォーマーの運用事例では、推論性能がH100比で「6割前後」との見立てが広がっており、コスト効率・供給確度を含めた総合最適で“国内代替の","quote_start":164,"quote_end":321,"text_sha256":"752194bff4aee491eadd10991dc08830bcd0c4c29a78f0a0fe752165f2fefd1a","block_sha256":"752194bff4aee491eadd10991dc08830bcd0c4c29a78f0a0fe752165f2fefd1a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_28e52d32-205e-4601-9fe8-535cce2d0600/#blk_f7d8752f-9e2c-4397-86c7-6b479690b315"},{"id":"occ_ec532dce318636d20bc0ea6e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_0506c6fa-e3b7-47bf-94a5-b0c32182955f","section_id":"sec_346133df-1eed-484d-8939-63fb85a75d68","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":277,"end":279,"exact":"推論","quote":"*価格** 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ASICを提供。ディープラーニング推論向けアクセラレーションを重視。\n- **Broadcom**：**ネットワークとデータ転送**に焦点を当て、AIのトレーニングや推論における効率的な**データ伝送と低遅延**の提供を重視。","quote_start":10,"quote_end":161,"text_sha256":"f36917ecd8c6d24476aa5a5d37551b182b269cf443411291d35bd04c25fdadad","block_sha256":"f36917ecd8c6d24476aa5a5d37551b182b269cf443411291d35bd04c25fdadad","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_218e7e80-683d-4dac-92ad-1859499e4045"},{"id":"occ_16dc6cdb0e66be17e4d8ec8a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_2508b985-fd1b-43e9-8584-d8730ae46fec","section_id":"sec_dbce5403-94a7-46c5-87fb-3b542509cf9a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":108,"end":110,"exact":"推論","quote":"100, 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特徴   \n**Google** **TPU（Tensor Processing Unit）** 推論・トレーニング向け。クラウドAI向けに最適化","quote_start":9,"quote_end":88,"text_sha256":"a0990a31db99dbd2fab903ee11883e7978dbb47d2288620d51feaf445579d519","block_sha256":"a0990a31db99dbd2fab903ee11883e7978dbb47d2288620d51feaf445579d519","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_251bf69e-59d9-42f3-a5e3-f85cb85256cf"},{"id":"occ_415191d260031402e54b1323","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_2e64f53e-1ce0-4509-b890-a467872265ec","section_id":"sec_0650a7a0-5570-4327-a6e7-c422cf41482d","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":126,"end":128,"exact":"推論","quote":"ラレータカード**を提供しており、その中にはネットワーク処理と連携して動作するものがあります。これにより、AI推論において必要とされる**データの転送帯域とスループット**を支えると共に、**低レイテンシの推論**が実現されます。\n- **Broadcomの光ファイバ技術**：Broadcomは、ネットワークの帯","quote_start":71,"quote_end":228,"text_sha256":"edc2bb24495b4fda162c49005df6b91d5079ad6b79740d0e5c04322ac7d821f1","block_sha256":"edc2bb24495b4fda162c49005df6b91d5079ad6b79740d0e5c04322ac7d821f1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_2e64f53e-1ce0-4509-b890-a467872265ec"},{"id":"occ_0941661542874995ecbcdf05","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_35140efe-b1c6-4057-94cb-82074be50720","section_id":"sec_0e3d9297-b2bd-4dc2-a3f0-261931cc0d1f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":121,"end":123,"exact":"推論","quote":"ョン**を提供するプロセッサです。これらのASICは、高スループットで低遅延のデータ通信を実現し、特に**AI推論**や**ディープラーニング推論**を高速化します。\n- **Alaska Ethernet PHYs**： Marvellの通信関連技術がデータセンターのネットワーク帯域を支えており、AIデータトラ","quote_start":66,"quote_end":223,"text_sha256":"85e3d7689624235b6a73aa1591c836d6e41ad4231c2afec1044573d10bcd2780","block_sha256":"85e3d7689624235b6a73aa1591c836d6e41ad4231c2afec1044573d10bcd2780","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_35140efe-b1c6-4057-94cb-82074be50720"},{"id":"occ_6ed4c82e11756329663a82f1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_392db6d1-97a8-4e18-bd0c-30b11b0a0d6e","section_id":"sec_0650a7a0-5570-4327-a6e7-c422cf41482d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":168,"end":170,"exact":"推論","quote":"ワーキング、ストレージ、データ処理**における性能を向上させる役割を果たしており、AIのモデルのトレーニングや推論処理を直接支援するものもあります。","quote_start":113,"quote_end":187,"text_sha256":"4797542be129eeb45d4023b3fb61c14d7012d4c6126c7d0fd4280f3a49038f7f","block_sha256":"4797542be129eeb45d4023b3fb61c14d7012d4c6126c7d0fd4280f3a49038f7f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_392db6d1-97a8-4e18-bd0c-30b11b0a0d6e"},{"id":"occ_ca25d9cebc2739c8f082eb12","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_4c5a7888-ee82-452b-8056-772dd7d7bb9f","section_id":"sec_af0e7eaf-5317-4a6f-a238-c1c39f7cd0da","layer":"body","character_id":null,"count":2,"matched_aliases":["Inference","推論"],"evidence":{"text_basis":"markdown","start":42,"end":51,"exact":"Inference","quote":"**Meta（Facebook）** **MTIA（Meta Training & Inference Accelerator）** 大規模なAI推論・学習に対応","quote_start":0,"quote_end":81,"text_sha256":"be0d91c6d3922b9008fb5f0ff8fd86b15242b242c062e447a684a887ffcd74a0","block_sha256":"be0d91c6d3922b9008fb5f0ff8fd86b15242b242c062e447a684a887ffcd74a0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_4c5a7888-ee82-452b-8056-772dd7d7bb9f"},{"id":"occ_19b9961a1a01b8eb143a817f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_5271499e-f538-4dc9-8617-11573c3416bb","section_id":"sec_5fa3ebac-975a-4263-ae97-a9adc6db305e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"項目 GDDRの特徴 **用途** ゲーム、AI推論、映像処理向け **最高速メモリ** GDDR6X（最大1.44TB/s） **コスト** HBMより安価だが、DDRより高価 **今後の進化** GDDR7（32Gbps、1.6TB/s超）","quote_start":0,"quote_end":122,"text_sha256":"e8b3bf19ab132d4f595a8b4b09f08e89c28badbb14d4ef53d4e27bc9fb27e57c","block_sha256":"e8b3bf19ab132d4f595a8b4b09f08e89c28badbb14d4ef53d4e27bc9fb27e57c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_5271499e-f538-4dc9-8617-11573c3416bb"},{"id":"occ_fd83752aebcffce753171eae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_5fcd3e7a-f120-4ac0-bcb6-c4865627c339","section_id":"sec_fb96c4eb-5cd3-48b5-bc13-242ff3a8dd0f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":57,"end":59,"exact":"推論","quote":"**Google TPUs（HBM搭載）**\n- **AWS Trainium & 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に特化したハードウェアが含まれています。","quote_start":130,"quote_end":212,"text_sha256":"779043b49daaff3a05154074fd1b8d6be75afb342f17ba26a716d911d09784eb","block_sha256":"779043b49daaff3a05154074fd1b8d6be75afb342f17ba26a716d911d09784eb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_7a01a3b0-e976-4a84-aad0-da413014332f"},{"id":"occ_22f77215d5f3bbf3451273b4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_8af83ed2-a0ec-44fa-9d24-9ca48ad3d17c","section_id":"sec_af0e7eaf-5317-4a6f-a238-c1c39f7cd0da","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"**Huawei** **Ascend 910 / 310** 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GPUよりも高い計算性能（TOPS/TeraOPS）を低消費電力で実行可能。\n   - 特定用途（推論処理・トレーニングなど）に特化することで、オーバーヘッドを削減。\n3. **柔軟性が低い（カスタム設計）**\n   \n   - GPUのように多目的用途には対応できず、特定のAIタスクに限定される。\n","quote_start":124,"quote_end":281,"text_sha256":"f14a1eaea08086fb38ed5b3a96345c0431a01cf399f72be218a85f2f22f59772","block_sha256":"f14a1eaea08086fb38ed5b3a96345c0431a01cf399f72be218a85f2f22f59772","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_c809eb21-d575-418f-a169-6a05bf6dca5b"},{"id":"occ_6507ae05bec8e14d07c45d17","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2b771286-9bc4-45c0-b45d-6524baf27996","work_id":"wrk_371c7035-898a-4ffb-9036-25fc84c21bd7","block_id":"blk_dc61e53c-193d-40dd-9ac1-a1a67bb8fe6d","section_id":"sec_a12cc86c-b0dc-4844-b412-55fe86dfa285","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":249,"end":251,"exact":"推論","quote":"** **畳み込み演算（Conv）**\n- 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ゲーム用GPU（GeForce、Radeon）\n- AI推論向けGPU（NVIDIA RTX 4090など）\n- データセンター（一部のGDDR搭載AIアクセラレーター）","quote_start":0,"quote_end":87,"text_sha256":"a7bfe44ccecf1c8cb5af2dcb9ae87f8352963d0eb62095b29c1f409d2597e406","block_sha256":"a7bfe44ccecf1c8cb5af2dcb9ae87f8352963d0eb62095b29c1f409d2597e406","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2b771286-9bc4-45c0-b45d-6524baf27996/#blk_f5fd5b42-ace6-44cb-be76-626a61844a2e"},{"id":"occ_9437f38269b74888bd77d66a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_017368f9-0247-46bf-abe1-03edfe471a19","section_id":"sec_3895746e-8caa-4677-a4b1-13824b60e07d","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":22,"end":24,"exact":"推論","quote":"AMDもEPYCについて、CPU単体でのAI推論だけでなく、GPUホストCPUとしての役割を明確に打ち出しています。AMDは、CPUだけで小〜中規模AI推論を処理できる一方、モデルが大きくなったり応答時間が短くなるとGPUを加え、EPYCとInst","quote_start":0,"quote_end":124,"text_sha256":"d50f41bcc015aff5dd35d8d91590aa5afdc3a3f977e623a80052876451444b68","block_sha256":"d50f41bcc015aff5dd35d8d91590aa5afdc3a3f977e623a80052876451444b68","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_017368f9-0247-46bf-abe1-03edfe471a19"},{"id":"occ_53908a504fdeffb570b40e18","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_0190dc55-5acb-47e8-87dd-f6cc7c7746f2","section_id":"sec_1cd762ba-80a0-472e-9983-0964840d2c3e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":13,"end":15,"exact":"推論","quote":"## \\4. LPU：言語推論専用の低遅延アクセラレータ","quote_start":0,"quote_end":28,"text_sha256":"d5f9afd704d0e0bccffe8c1f0d529506e0673d3989e3a8ccf7d49186587ecbe0","block_sha256":"d5f9afd704d0e0bccffe8c1f0d529506e0673d3989e3a8ccf7d49186587ecbe0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_0190dc55-5acb-47e8-87dd-f6cc7c7746f2"},{"id":"occ_0e4cb9851facf6d78807a923","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_01d4110a-7bf2-42d9-a7e9-360dade446b4","section_id":"sec_aeca1429-2a19-4d31-b76e-1f7203fdea26","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":7,"end":15,"exact":"KV cache","quote":"3つ目は、**KV cacheやメモリ圧を下げること**です。  \n大きなバッチを一気にGPUへ流すとKV cache需要が瞬間的に大きくなります。マイクロバッチ化すると、GPUメモリへの圧力も下がりやすいと論文は説明しています。","quote_start":0,"quote_end":115,"text_sha256":"61eb940d3e1de1aa861ad1d029612fad0a380c4e13f1e6a35951f57975b9621a","block_sha256":"61eb940d3e1de1aa861ad1d029612fad0a380c4e13f1e6a35951f57975b9621a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_01d4110a-7bf2-42d9-a7e9-360dade446b4"},{"id":"occ_a22df5f519e680c74c885db8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_028d3dd1-ea6a-4c02-8474-998893c55026","section_id":"sec_4702fa77-c51d-4de1-a6b8-7ffad0dcbd89","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"## \\4. LPUは「言語推論の固定パイプライン」に寄っている","quote_start":0,"quote_end":32,"text_sha256":"f05b11533ec138be3288d9d9d473564824122d7258c94d0f571ed8a1999534b8","block_sha256":"f05b11533ec138be3288d9d9d473564824122d7258c94d0f571ed8a1999534b8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_028d3dd1-ea6a-4c02-8474-998893c55026"},{"id":"occ_8b35186558ca4739d5e65c28","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_03671ead-1061-4a4b-a459-fb6657544a6a","section_id":"sec_f6e01ba7-3115-4501-a1d3-0dcbd52a27b5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":42,"end":44,"exact":"推論","quote":"AIブーム初期の主役はGPUだった。これは今後も大きく変わらない。大規模学習、大規模推論、画像生成、音声生成、科学技術計算ではGPUやTPUが中心であり続ける。","quote_start":0,"quote_end":80,"text_sha256":"f119f829f6d4b9f48fa5ce61e427e6a290f9052ed55bfc5ab061f032ddf3e20c","block_sha256":"f119f829f6d4b9f48fa5ce61e427e6a290f9052ed55bfc5ab061f032ddf3e20c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_03671ead-1061-4a4b-a459-fb6657544a6a"},{"id":"occ_0edfa63b71753e1122a128f8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_03b1617f-9b5a-489b-833d-1f958bdbdfce","section_id":"sec_3666ba64-35d9-47b3-bc35-956eceae8da9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"## \\1. 定型的なLLM推論","quote_start":0,"quote_end":16,"text_sha256":"7909c9718db4378e682edfca0c26c7e002c4ef028b4d73eea7f148812635aae9","block_sha256":"7909c9718db4378e682edfca0c26c7e002c4ef028b4d73eea7f148812635aae9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_03b1617f-9b5a-489b-833d-1f958bdbdfce"},{"id":"occ_3924512bef9b83271882a0c1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_0612dd39-c5c3-49c8-95c3-b62df2d24afd","section_id":"sec_58e85375-d53a-4052-9dcc-3e885bf11c81","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":52,"end":61,"exact":"Inference","quote":"次に重要なのが、2025年の **“Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures”** です。","quote_start":0,"quote_end":111,"text_sha256":"d1e130d6ddf4fa0aec616d446d15ea5b1fd0ef413ebe44d3d298f2affe2bf709","block_sha256":"d1e130d6ddf4fa0aec616d446d15ea5b1fd0ef413ebe44d3d298f2affe2bf709","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_0612dd39-c5c3-49c8-95c3-b62df2d24afd"},{"id":"occ_f92e78b0ee74716cdedc0b19","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_0fdfb496-62e6-419d-b7e3-5db4151ea796","section_id":"sec_5f99fcd2-dfd9-4824-9760-585990f76325","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"LPUは基本的に推論特化なので、大規模学習のGPU代替にはなりにくいです。","quote_start":0,"quote_end":37,"text_sha256":"078741a5f7937a542107c3a48f5659d811cecd170931a828c4b7ae9f4900b8de","block_sha256":"078741a5f7937a542107c3a48f5659d811cecd170931a828c4b7ae9f4900b8de","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_0fdfb496-62e6-419d-b7e3-5db4151ea796"},{"id":"occ_e030ed8e3491de84d82b8f4a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_11a9455c-a1e7-42ba-ab97-523c772f8eaf","section_id":"sec_feed3066-b80b-4a0e-b2b9-7fd3b9ffedce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"## 推論・学習・エージェントでの役割","quote_start":0,"quote_end":19,"text_sha256":"210ae4194ee30146f50a30553444bdbe141e81dc454d8bb3a057ecf3c8cf2123","block_sha256":"210ae4194ee30146f50a30553444bdbe141e81dc454d8bb3a057ecf3c8cf2123","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_11a9455c-a1e7-42ba-ab97-523c772f8eaf"},{"id":"occ_4febceee2434f0c5b3da9a94","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_12105792-a607-4fc0-aebc-24c625d514fc","section_id":"sec_8ec24d97-a546-4814-9c1b-db768ab1c94b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":66,"end":68,"exact":"推論","quote":"**  \n**GPU = 大量の作業員**  \n**TPU = 行列計算専用工場**  \n**LPU = 言語推論専用の高速ベルトコンベア**","quote_start":11,"quote_end":82,"text_sha256":"400ef8142476f30b492b78d865f8b4ac4782dca72025d1c215ccc85b42ca98dd","block_sha256":"400ef8142476f30b492b78d865f8b4ac4782dca72025d1c215ccc85b42ca98dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_12105792-a607-4fc0-aebc-24c625d514fc"},{"id":"occ_749b4433532e3a438fb8585d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_122a51a1-6504-434f-8476-27696a02f66a","section_id":"sec_f736ba24-50d5-4b61-a96c-5eccdb8168eb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"これはほぼLLM推論だけなので、GPU負荷が中心。","quote_start":0,"quote_end":25,"text_sha256":"0ed3cc394dce69b9585e3ce7e0c280f7c62b5a807d67f43000f153044caf58a8","block_sha256":"0ed3cc394dce69b9585e3ce7e0c280f7c62b5a807d67f43000f153044caf58a8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_122a51a1-6504-434f-8476-27696a02f66a"},{"id":"occ_b42c4a6447e119395b7c9785","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_1688d832-3b4d-4fb7-b585-e0636037f63c","section_id":"sec_1a0df362-7fe6-4bab-9e26-4245d9f21d5a","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":40,"end":42,"exact":"推論","quote":"```\n100万個の画素を処理する\n巨大な行列を計算する\n大量のトークンをバッチ推論する\n```","quote_start":0,"quote_end":48,"text_sha256":"b219142357826a23c849b9e1cba02d4e87b856a34c79038231568736e4a5e881","block_sha256":"b219142357826a23c849b9e1cba02d4e87b856a34c79038231568736e4a5e881","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_1688d832-3b4d-4fb7-b585-e0636037f63c"},{"id":"occ_693b3ebd0f1f96f12648034a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_16af3086-d6a2-4af8-99ad-0f6ba34ed81b","section_id":"sec_7dd33398-5283-46c4-a786-e187e63ed76a","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":9,"end":11,"exact":"推論","quote":"```\n定型LLM推論\n低遅延チャット\n音声AI\n固定モデルの大量推論\nGoogle型の大規模テンソル処理\n社内AIエージェントの一部\n```","quote_start":0,"quote_end":71,"text_sha256":"e465b98eb5590e207d5484e3bd633742b2cd6bc26fc78e76f871c841da995962","block_sha256":"e465b98eb5590e207d5484e3bd633742b2cd6bc26fc78e76f871c841da995962","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_16af3086-d6a2-4af8-99ad-0f6ba34ed81b"},{"id":"occ_b4af4100c6d44223f479a8b4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_1b49fc0a-85f9-4ace-b1d5-1cc4484ba098","section_id":"sec_f5fb003a-62fe-48c2-a63d-107ee26859cb","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論では、生成中の各リクエストが **KV cache** を持つ。これはモデルが過去の文脈を参照しながら次のトークンを生成するための記憶領域であり、長文・多ユーザー・複数候補生成では非常に大きくなる。","quote_start":0,"quote_end":104,"text_sha256":"31b2a826cecd744d136ff6884b4980733f09fa52c106dab6b839e8783befecb0","block_sha256":"31b2a826cecd744d136ff6884b4980733f09fa52c106dab6b839e8783befecb0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_1b49fc0a-85f9-4ace-b1d5-1cc4484ba098"},{"id":"occ_2c6f73923cbca94660fa51ef","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_20db3818-5978-4c67-9d68-5885abebb369","section_id":"sec_4702fa77-c51d-4de1-a6b8-7ffad0dcbd89","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":13,"end":15,"exact":"推論","quote":"GroqはLPUを、LLM推論の高速・低遅延実行に特化したプロセッサとして打ち出しています。公式サイトでも、LPUは推論向けであり、スピードとスケール時の低コストを特徴にしています。([Groq](https://groq.com","quote_start":0,"quote_end":115,"text_sha256":"8d473aae14fe354e7ffc4cf295a987434b1d267e0c91546a48dd0464220d0c3d","block_sha256":"8d473aae14fe354e7ffc4cf295a987434b1d267e0c91546a48dd0464220d0c3d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_20db3818-5978-4c67-9d68-5885abebb369"},{"id":"occ_fd7a4e45b5f7555155b39755","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_243ec311-7270-45d9-a0da-b68c766946bd","section_id":"sec_3aad843a-f437-4379-87a0-f87bf411ef73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"実サービスのAI推論では、ユーザーごとに入力長が違います。","quote_start":0,"quote_end":29,"text_sha256":"c016c661eabd0c3fc7aa23ad1e31f94ab81e9388c19bcb0f94eaaa9e48ba2f92","block_sha256":"c016c661eabd0c3fc7aa23ad1e31f94ab81e9388c19bcb0f94eaaa9e48ba2f92","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_243ec311-7270-45d9-a0da-b68c766946bd"},{"id":"occ_5d069bbff11d10db48504a40","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_24e645bf-1caf-43e2-a8ec-3520b3c523d9","section_id":"sec_37921479-9d10-4e6b-a7e0-f409ca5e4390","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":160,"end":162,"exact":"推論","quote":"含む巨大なソフトウェア・ハードウェア基盤になっています。NVIDIAはTensor Coreについて、学習から推論、HPCまで幅広いAIワークロードを加速する中核部品として説明しています。([NVIDIA](https://www.nvidia.com/en-us/data-center/tensor-cores","quote_start":105,"quote_end":262,"text_sha256":"90e2e33477a6b12052e6cde735876e6490da186a5f4da95e76dc78254d550448","block_sha256":"90e2e33477a6b12052e6cde735876e6490da186a5f4da95e76dc78254d550448","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_24e645bf-1caf-43e2-a8ec-3520b3c523d9"},{"id":"occ_83fe583e5842dd5db20e106a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_26c285ef-024c-47e1-8b89-6d4baf6fe2f1","section_id":"sec_370fa449-df14-46e6-92e1-fa36a26d57b3","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":64,"end":66,"exact":"推論","quote":"LAの大規模学習\n画像生成\n動画生成\n世界モデル\n3Dシミュレーション\nロボット学習\n強化学習\nマルチモーダル推論\nDiffusion Transformer\nTransformer推論\n物理シミュレーション\nレンダリング\n```","quote_start":9,"quote_end":124,"text_sha256":"80b36b6d0434e6429326277d07da0053c08c5bde7f8061278b669abf71deca76","block_sha256":"80b36b6d0434e6429326277d07da0053c08c5bde7f8061278b669abf71deca76","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_26c285ef-024c-47e1-8b89-6d4baf6fe2f1"},{"id":"occ_de5e4d7eaf850bf4728dac22","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_26d40ef3-43f2-484b-8ff6-e8f0bf3e4c7b","section_id":"sec_f6dc8e3a-bd25-4918-897c-5432256d92c4","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"Iエージェントの実行を **CPU中心** に分析しています。要旨では、Agentic AIは従来の単発LLM推論と違い、計画、ツール呼び出し、推論、適応を行う自律的な問題解決システムであり、その外部ツールの多くはCPU上で動く、またはCPUによりオーケストレーションされると説明しています。さらに、CPU/GPU","quote_start":7,"quote_end":164,"text_sha256":"c4c1bd4b1b2f1ffed6276fca43e834702bca6bf145cc3fbb087c6feaca78759a","block_sha256":"c4c1bd4b1b2f1ffed6276fca43e834702bca6bf145cc3fbb087c6feaca78759a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_26d40ef3-43f2-484b-8ff6-e8f0bf3e4c7b"},{"id":"occ_cf11f5131d110408f7fff63b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_2827e4ef-e5b4-439f-8b99-1b8bcd1438f4","section_id":"sec_10a2ceaf-e8ce-43ae-a5c2-57677ee437c9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"LPUは基本的に学習用というより、推論特化で見るべきです。","quote_start":0,"quote_end":29,"text_sha256":"bee68fe885dc0007b7ee324107df6f563b5ec34f0251a7586ff440f0e72d2df2","block_sha256":"bee68fe885dc0007b7ee324107df6f563b5ec34f0251a7586ff440f0e72d2df2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_2827e4ef-e5b4-439f-8b99-1b8bcd1438f4"},{"id":"occ_9dbcce86186dfeedfbb4d79c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_29243854-33cf-4027-82bf-262e8381afb4","section_id":"sec_60bc1286-e5cb-4d25-b160-a7b5674c0701","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":45,"end":53,"exact":"KV cache","quote":"**GPU HBMだけで全部抱えるのではなく、CPU DRAM、CXLメモリ、SSD、分散KV cacheを使ってGPUを補助する**","quote_start":0,"quote_end":67,"text_sha256":"577db07b1bb83a90d0b21b29181509517e700f0a80ace1d3cb5390e26dd34981","block_sha256":"577db07b1bb83a90d0b21b29181509517e700f0a80ace1d3cb5390e26dd34981","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_29243854-33cf-4027-82bf-262e8381afb4"},{"id":"occ_c2d9305074ddd1dd224e6696","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_2aade069-2461-4c37-8148-b9fcfc6d3f7a","section_id":"sec_6bcc2a43-7390-4518-87df-b6181c33d220","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":105,"end":107,"exact":"推論","quote":"ション\n強化学習\n模倣学習\nDiffusion Policy\nロボット軌道生成\n視覚エンコーダ\nマルチモーダル推論\n```","quote_start":50,"quote_end":111,"text_sha256":"8f70a6b12b2efbe7144ab497208ca61f63b36ce84c00fed92b05b08a0f1c1a5f","block_sha256":"8f70a6b12b2efbe7144ab497208ca61f63b36ce84c00fed92b05b08a0f1c1a5f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_2aade069-2461-4c37-8148-b9fcfc6d3f7a"},{"id":"occ_2a1c3925877cd47a2c25154f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_2b2528b5-6f76-41b7-9f1e-7a48e186f798","section_id":"sec_4702fa77-c51d-4de1-a6b8-7ffad0dcbd89","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"```\nLLM推論の計算を\nあらかじめ決めたスケジュールで\n低遅延に流す\n```","quote_start":0,"quote_end":40,"text_sha256":"e14d0c81830f41c5c8d03a1fa2505ea0e24127228d4e12ef01a9ab7675c241f7","block_sha256":"e14d0c81830f41c5c8d03a1fa2505ea0e24127228d4e12ef01a9ab7675c241f7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_2b2528b5-6f76-41b7-9f1e-7a48e186f798"},{"id":"occ_90c0fbe458f5af5f4dee85d0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_2b74809e-eaa7-4e94-9605-18da7d55559a","section_id":"sec_96593a97-1091-44e5-a238-83d7805e4de7","layer":"body","character_id":null,"count":4,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":33,"end":40,"exact":"prefill","quote":"DistServeも同じ問題意識を持つ。既存のLLMサービングではprefillとdecodeを同じGPU群で混在処理するため干渉が起きる。DistServeはprefillとdecodeを分離し、それぞれに合った資源配分を行うことで、TTFT、つまり最初のトークンまでの時間と、","quote_start":0,"quote_end":140,"text_sha256":"1d3a316704c32a05898a3c405e1e80e820964d4711f0466a270cef1bd6636a08","block_sha256":"1d3a316704c32a05898a3c405e1e80e820964d4711f0466a270cef1bd6636a08","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_2b74809e-eaa7-4e94-9605-18da7d55559a"},{"id":"occ_90663f1ba3083674cfe0a675","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_2edc3ea3-4ef5-4ee4-b266-65c862d6ee6b","section_id":"sec_c544a6a5-6261-4dbc-80fa-322c39d5bf5c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"## 推論爆増の時代","quote_start":0,"quote_end":10,"text_sha256":"4a0bb2185920a531265b0f0d1ebbd238b77fda34b4d6e20e064053cf9485eb56","block_sha256":"4a0bb2185920a531265b0f0d1ebbd238b77fda34b4d6e20e064053cf9485eb56","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_2edc3ea3-4ef5-4ee4-b266-65c862d6ee6b"},{"id":"occ_c593911dfb9d5a65e6949645","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3071563b-ab5b-42d5-b892-5d7a69371deb","section_id":"sec_12efc3a6-2d70-4d0f-924f-bc0a1833cafe","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":42,"end":44,"exact":"推論","quote":"```\n行列積\n畳み込み\nTransformerのAttention\nMLP\nバッチ推論\n```","quote_start":0,"quote_end":48,"text_sha256":"c4b952abea54d0b5e16c8437f37a56a51dda00f142f67be31e883e77fcefc65d","block_sha256":"c4b952abea54d0b5e16c8437f37a56a51dda00f142f67be31e883e77fcefc65d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3071563b-ab5b-42d5-b892-5d7a69371deb"},{"id":"occ_14d2b4ea135d5c2f852145d4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_30f1e6e3-67f7-472a-872d-f30fd99ab182","section_id":"sec_370fa449-df14-46e6-92e1-fa36a26d57b3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"GPUは単なるLLM推論チップではなく、**視覚・動画・物理・生成・シミュレーションを統合する万能AI工場** です。","quote_start":0,"quote_end":59,"text_sha256":"870c77ba31ee8ed8e0931a29c28d3b307ca2fcdbb4017fc8463a9437913f8bca","block_sha256":"870c77ba31ee8ed8e0931a29c28d3b307ca2fcdbb4017fc8463a9437913f8bca","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_30f1e6e3-67f7-472a-872d-f30fd99ab182"},{"id":"occ_fa35fa34b443d92269cab04f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3141b2be-28cb-4f6a-9c27-2a4735af7736","section_id":"sec_7ac6c80b-4f0d-4b8e-bd9f-733c3848d0b8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"### \\2. GPU推論を支えるホストCPU需要","quote_start":0,"quote_end":25,"text_sha256":"ea7a81e89fec12861d46e8432dadeba999a7939c664447b19a6804fb675624fb","block_sha256":"ea7a81e89fec12861d46e8432dadeba999a7939c664447b19a6804fb675624fb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3141b2be-28cb-4f6a-9c27-2a4735af7736"},{"id":"occ_6238be225306b9d6609fc1b4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_32ad4ed7-3a53-4dd4-a5c3-e53606db80a0","section_id":"sec_14f77511-f485-4c1a-a2eb-1fa31f400343","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"TPUは、大規模なテンソル計算、学習、推論に強い。  \nLPUは、LLMの低遅延推論、特にトークン生成に強い。","quote_start":0,"quote_end":55,"text_sha256":"d4e87088b2a3a42b4ad3eef5ae621681ef1e2ef558014e8453640b9b1a4ed900","block_sha256":"d4e87088b2a3a42b4ad3eef5ae621681ef1e2ef558014e8453640b9b1a4ed900","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_32ad4ed7-3a53-4dd4-a5c3-e53606db80a0"},{"id":"occ_85d7e2496c1ed889ba107cfa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_32c397f6-4b8b-43e5-9638-4600cb3b056c","section_id":"sec_96593a97-1091-44e5-a238-83d7805e4de7","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"ここで重要なのは、AI推論の性能は単純なFLOPSだけでは決まらないということだ。  \nリクエストをどう並べるか。  \nどのタイミングでGPUに流すか。  \nKV cacheをどう管理するか。  \nCPUで動くツール処理をどこ","quote_start":0,"quote_end":113,"text_sha256":"c357d0f4d558ae7914f71894c8d2412ce5d2c2d74408f2186641cb237202d435","block_sha256":"c357d0f4d558ae7914f71894c8d2412ce5d2c2d74408f2186641cb237202d435","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_32c397f6-4b8b-43e5-9638-4600cb3b056c"},{"id":"occ_110beab9357458ed05c16cad","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_337184ce-41df-485f-b54a-5c7b93fdf657","section_id":"sec_a145f732-392b-49c2-bcbd-fd9ff502d3e9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"処理・RAG・API・I/OにはSMT的な効率化が効く**。  \n**GPU側のLLM行列計算・画像生成・大量推論にはSIMTが効く**。  \n  \n**TPUやLPUでは、GPU以上に「分岐処理」「不規則処理」「OS的な制御」が苦手になりやすい**です。","quote_start":27,"quote_end":155,"text_sha256":"c5966487a1d903a8df915ba168dd7c6fc7a1bd2fdda49e8e312d1035ab8a518f","block_sha256":"c5966487a1d903a8df915ba168dd7c6fc7a1bd2fdda49e8e312d1035ab8a518f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_337184ce-41df-485f-b54a-5c7b93fdf657"},{"id":"occ_429d0f62b826dd3729d09e10","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3821a67e-40aa-4b4b-b04c-d8b25b78163a","section_id":"sec_6acf9d59-e5ea-45c8-9531-a02ed175805a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論のボトルネックは、もはや単純な","quote_start":0,"quote_end":19,"text_sha256":"5457b04b2c8e75821cc32f79f4e296daeb6c57d35738b2620910718eb58af5b3","block_sha256":"5457b04b2c8e75821cc32f79f4e296daeb6c57d35738b2620910718eb58af5b3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3821a67e-40aa-4b4b-b04c-d8b25b78163a"},{"id":"occ_7bb0fbbe626d81d7c1e35718","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3ca2e206-b0c6-4fba-86b4-57cf337997b1","section_id":"sec_741197d7-6d5e-434d-93c5-573a40fa9156","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"## LPUの役割：低遅延の言語・推論ループ","quote_start":0,"quote_end":22,"text_sha256":"7c35638e67c254b3b93a2ef670b623794999268d2011154dc745469e79fc0cf1","block_sha256":"7c35638e67c254b3b93a2ef670b623794999268d2011154dc745469e79fc0cf1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3ca2e206-b0c6-4fba-86b4-57cf337997b1"},{"id":"occ_d892d48812aa989dc0afa4f0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3e8a6a89-28b0-4cbf-a454-d60f7b66fba9","section_id":"sec_eeb195b5-bdb9-4c3c-84e5-7e4ec367e5ac","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"## \\8. ただし、推論が定型化するとTPU/LPUの領域も増える","quote_start":0,"quote_end":34,"text_sha256":"05e239f1bfde52d3ed057aa1bd3268e0e90011c63b80e8265b9ab915d5a2031d","block_sha256":"05e239f1bfde52d3ed057aa1bd3268e0e90011c63b80e8265b9ab915d5a2031d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3e8a6a89-28b0-4cbf-a454-d60f7b66fba9"},{"id":"occ_6e8ffbe011212a99cd14527c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_3ebf10b1-17f0-48bc-98a5-0c68557724b2","section_id":"sec_60bc1286-e5cb-4d25-b160-a7b5674c0701","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"## \\5. FlexGen / LMCache：CPUメモリ・DRAM・ストレージがGPU推論を支える","quote_start":0,"quote_end":52,"text_sha256":"e76520adc3c3ed235f830e6c3de2caadece6ff71cbe397f0602a6c9422d07631","block_sha256":"e76520adc3c3ed235f830e6c3de2caadece6ff71cbe397f0602a6c9422d07631","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_3ebf10b1-17f0-48bc-98a5-0c68557724b2"},{"id":"occ_8412b091812935cca8c111bd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4018bcf3-1e05-4d0e-9694-248554908f49","section_id":"sec_ade72eea-582d-4e81-8d35-284b83454113","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":5,"end":7,"exact":"推論","quote":"## AI推論の流れで見ると分かりやすい","quote_start":0,"quote_end":20,"text_sha256":"15f21c77dbf695547e3f03ff9cf2e7dbc3c264d252c990a3614fa84d77a0d9ff","block_sha256":"15f21c77dbf695547e3f03ff9cf2e7dbc3c264d252c990a3614fa84d77a0d9ff","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4018bcf3-1e05-4d0e-9694-248554908f49"},{"id":"occ_082c064c8a71b33e473b4cdb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_40a01e2a-24a6-4f5a-95bd-834e70055da7","section_id":"sec_001eec6f-709b-4cab-8986-2957a70ced22","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"LPUは基本的に言語推論向けです。  \n画像生成や動画生成には向きません。","quote_start":0,"quote_end":37,"text_sha256":"061d4a13f18d863f5dab52bafd7cf3f17e306b5805f6e06e54fa26f53e73b51c","block_sha256":"061d4a13f18d863f5dab52bafd7cf3f17e306b5805f6e06e54fa26f53e73b51c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_40a01e2a-24a6-4f5a-95bd-834e70055da7"},{"id":"occ_37bd75106f712bfeb173df5e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_40cc1701-41eb-462a-b5b1-50ababb92cd4","section_id":"sec_217a77bd-aa6e-42cf-820a-bb48d5cd649b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"に移っていく可能性が高いです。  \nこの文脈では、AMDやIntelの上昇は単なる出遅れ物色ではなく、**AI推論インフラの設計思想そのものが変わってきたことへの再評価**と見てよいと思います。","quote_start":0,"quote_end":97,"text_sha256":"859a9e426248f6ee2840b183b056117c86a6bb0c441ebef91a180c747200dd93","block_sha256":"859a9e426248f6ee2840b183b056117c86a6bb0c441ebef91a180c747200dd93","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_40cc1701-41eb-462a-b5b1-50ababb92cd4"},{"id":"occ_20e339786c950c311da97afe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_40d260d0-57e2-4a48-849b-53ba8f3d0642","section_id":"sec_08bb8dd1-0363-42f6-88fc-21ce5f968155","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"AIがまだ急速に進化している間はGPUが中心に残りやすい。  \n一方で、推論が大量化し、モデルや用途が安定してくるほど、TPUやLPUのような専用アクセラレータの価値が高まっていくと思います。","quote_start":0,"quote_end":96,"text_sha256":"9078a181b0884c27a7c85bcb07cacb6efa60497ddb16f9f31934f0972fcf9715","block_sha256":"9078a181b0884c27a7c85bcb07cacb6efa60497ddb16f9f31934f0972fcf9715","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_40d260d0-57e2-4a48-849b-53ba8f3d0642"},{"id":"occ_e3e3a3ba9f01bbb2a06aee2a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_43a2e613-f7bf-4299-ade9-f2c43971c594","section_id":"sec_409f5314-0c01-4d07-a2ef-1e6067c0c5ad","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論では、CPUは主に以下を担当します。","quote_start":0,"quote_end":22,"text_sha256":"75797e6a2f48333c663aa66040f8b2579cd56ae2891149bddbd0fea3b2165eff","block_sha256":"75797e6a2f48333c663aa66040f8b2579cd56ae2891149bddbd0fea3b2165eff","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_43a2e613-f7bf-4299-ade9-f2c43971c594"},{"id":"occ_7d09782c9284dd978c302d9e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_45a1ee19-4bbc-4cda-9a42-56630d0877e2","section_id":"sec_0536cbe1-635c-4c53-8177-029476181d49","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"特に画像生成・動画生成がVLAに重要になると、GPU需要は単なる「チャットAI推論」だけでなく、","quote_start":0,"quote_end":48,"text_sha256":"a0c2cab28b1ac9cf0a35b9dde5f3e68d55dd3c9f545c240550dc9e8f286af716","block_sha256":"a0c2cab28b1ac9cf0a35b9dde5f3e68d55dd3c9f545c240550dc9e8f286af716","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_45a1ee19-4bbc-4cda-9a42-56630d0877e2"},{"id":"occ_add9779afa61e84a842d9971","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_48a262db-7050-4bdf-9a65-4fbc773b96fd","section_id":"sec_741197d7-6d5e-434d-93c5-573a40fa9156","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"```\n音声対話\n短いLLM推論\nタスク分解\nエージェントの内部ループ\n人間との応答\nロボットの説明生成\n```","quote_start":0,"quote_end":56,"text_sha256":"1e7edcc0f595f5698b8c17ed2bc691b8f90e27e53b938f76cf40b388a12583d7","block_sha256":"1e7edcc0f595f5698b8c17ed2bc691b8f90e27e53b938f76cf40b388a12583d7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_48a262db-7050-4bdf-9a65-4fbc773b96fd"},{"id":"occ_1d817741b71d14a1d96a1091","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_48b8c827-38e9-437a-a2d2-5358e0fff906","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":2,"end":9,"exact":"prefill","quote":"**prefill** は、入力プロンプト全体を処理して最初のトークンを出す段階です。  \nこれは並列性が高く、GPU計算を飽和させやすい。","quote_start":0,"quote_end":70,"text_sha256":"a2873d10af3922b6f8e795ce752b0d50004545a78a53eebd83b22ad0cd70c9c9","block_sha256":"a2873d10af3922b6f8e795ce752b0d50004545a78a53eebd83b22ad0cd70c9c9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_48b8c827-38e9-437a-a2d2-5358e0fff906"},{"id":"occ_1d2ba168b90cd996f8ec964c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4c4cc8f7-f0b9-4f69-bd76-0a2602e2938b","section_id":"sec_85734f9a-0c32-4d34-9a5f-646477ce1cbc","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":50,"end":52,"exact":"推論","quote":"```\n巨大バッチ\n定型モデル\nXLAで最適化できる計算グラフ\nGoogleクラウド上の大規模学習/推論\n```","quote_start":0,"quote_end":56,"text_sha256":"451e040244971e6a8345efed8fb43bbd29e719d2efdbbe49a6eece9a395fc862","block_sha256":"451e040244971e6a8345efed8fb43bbd29e719d2efdbbe49a6eece9a395fc862","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4c4cc8f7-f0b9-4f69-bd76-0a2602e2938b"},{"id":"occ_80a7cac99117038002fa3829","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4c535dff-be53-4b11-bb81-5bdd731d510e","section_id":"sec_9140b658-5f81-4bca-8d40-df1d662481e5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"従来のLLM推論は、単純化すれば「入力を受け取り、モデルで計算し、出力を返す」処理だった。この場合、主な負荷はGPU上の行列演算に集中する。","quote_start":0,"quote_end":70,"text_sha256":"e0975da59cd09e5036d0a7360afbda4583b56b65576fe7f785f668c60fccbad0","block_sha256":"e0975da59cd09e5036d0a7360afbda4583b56b65576fe7f785f668c60fccbad0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4c535dff-be53-4b11-bb81-5bdd731d510e"},{"id":"occ_423d49f4720239de9cd77e8e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4de7b27e-5b1e-4278-b24c-5939ba60ce7d","section_id":"sec_08785f72-2569-42f3-a55a-aaecced92b31","layer":"code","character_id":null,"count":1,"matched_aliases":["KV 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cache管理\nRAG検索\nDB照会\nWeb検索\nAPI呼び出し\nPython実行\nログ保存\n認証\n課金\nセキュリティ\n```","quote_start":0,"quote_end":104,"text_sha256":"59a62fe4d8ffb607bb2043188b3745cdd3dd25fc7031b1359b39eb1e1e4d6a9a","block_sha256":"59a62fe4d8ffb607bb2043188b3745cdd3dd25fc7031b1359b39eb1e1e4d6a9a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4de7b27e-5b1e-4278-b24c-5939ba60ce7d"},{"id":"occ_01452f380ae2c5c9b248accc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4f07fed4-1d0d-43cb-8a89-6ec6ff9dee46","section_id":"sec_08bb8dd1-0363-42f6-88fc-21ce5f968155","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":59,"end":61,"exact":"推論","quote":"Uは「変化に強い」。**  \n**TPUは「定型化した大規模計算に強い」。**  \n**LPUは「リアルタイム推論に強い」。**","quote_start":4,"quote_end":68,"text_sha256":"8a6b5f3f91806646674cdc84ac30f693487dda97ef9ee2d95e554ea9d142d80a","block_sha256":"8a6b5f3f91806646674cdc84ac30f693487dda97ef9ee2d95e554ea9d142d80a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4f07fed4-1d0d-43cb-8a89-6ec6ff9dee46"},{"id":"occ_bab83f777391451453e388db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_4f62ae7c-feeb-4220-b442-4481728ab599","section_id":"sec_17a23976-3f69-4cb5-83f7-34584d9112fb","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"```\nチャット\n音声AI\nリアルタイム翻訳\nAIエージェントの短い反復推論\nコーディング補助\n低レイテンシAPI\n```","quote_start":0,"quote_end":61,"text_sha256":"3a42ca38ede24a343e69a7173743196cf3aace7985a14d7a4b88a9d2eb5e752a","block_sha256":"3a42ca38ede24a343e69a7173743196cf3aace7985a14d7a4b88a9d2eb5e752a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_4f62ae7c-feeb-4220-b442-4481728ab599"},{"id":"occ_83ce3366fd5252217245a462","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_504b5596-6c22-429c-879f-7cc3d84147f9","section_id":"sec_b25924f8-b6f7-407d-b7d1-b2c30ddb56b3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"**入力 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**Vision-Language Model**、つまり画像や動画を見て、言語で理解・推論するモデルです。  \nVLAは **Vision-Language-Action Model**、つまり画像・言語を入力し、ロボットの行動まで出力するモデルです。","quote_start":0,"quote_end":132,"text_sha256":"ceef6143bb3fa36ec4a1a298dcb0652dd54ff1063af999b0b3528843f1d08a88","block_sha256":"ceef6143bb3fa36ec4a1a298dcb0652dd54ff1063af999b0b3528843f1d08a88","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_5f1b9d75-0ec5-4f0d-b4bc-c943dc21fd64"},{"id":"occ_63e41595c875f2ac4e7a4199","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_63ecb1af-0abd-4a0a-8b34-84d6a94091e8","section_id":"sec_8be67a98-017a-467c-92d9-bdd854efa78c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"このように、何度も短い推論を繰り返します。","quote_start":0,"quote_end":21,"text_sha256":"fdb828c910aa7b4d72528ec50edf7fbd0bdd88247839b21d1134961cf610b5e2","block_sha256":"fdb828c910aa7b4d72528ec50edf7fbd0bdd88247839b21d1134961cf610b5e2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_63ecb1af-0abd-4a0a-8b34-84d6a94091e8"},{"id":"occ_a9497c338950f447e5143918","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_657d6e27-18e0-49e9-921c-1d4fd13c842a","section_id":"sec_7ac6c80b-4f0d-4b8e-bd9f-733c3848d0b8","layer":"body","character_id":null,"count":3,"matched_aliases":["KV 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cache管理、GPUワーカー制御、ネットワークI/O、ストレージI/O、セキュリティ、ログ、API処理。**","quote_start":0,"quote_end":99,"text_sha256":"db0e8cd6ac5feea8dfb7f8509ddf083039436dc217e7975b1e042ed3449e8fc6","block_sha256":"db0e8cd6ac5feea8dfb7f8509ddf083039436dc217e7975b1e042ed3449e8fc6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_657d6e27-18e0-49e9-921c-1d4fd13c842a"},{"id":"occ_f5fe29e2147ca150d0f9e9c7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_6639668b-70de-4400-928a-3823ef7e5337","section_id":"sec_8a97483f-1163-43f1-a68a-c680a2c1bddf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":68,"end":70,"exact":"推論","quote":"なAI工場**  \n**TPU = Google型の巨大テンソル専用工場**  \n**LPU = 低遅延LLM推論の高速ベルトコンベア**","quote_start":13,"quote_end":82,"text_sha256":"62d3ec2529431a3daf536b39b93dea0e7c036d0bbbea0e44d296e560f4d5797e","block_sha256":"62d3ec2529431a3daf536b39b93dea0e7c036d0bbbea0e44d296e560f4d5797e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_6639668b-70de-4400-928a-3823ef7e5337"},{"id":"occ_c3a5c2e5e43f997a1e641901","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_669c6693-15a9-43b8-9e77-159a13e7c720","section_id":"sec_f3997776-540b-4cda-a4c4-c78dae0caaaa","layer":"code","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":12,"end":18,"exact":"Decode","quote":"```\nFetch → Decode → Execute → Memory → Writeback\n```","quote_start":0,"quote_end":53,"text_sha256":"c2aafb0d242df0c3c72ed08d39fe51906ceca25e13f04f22f83b6aa0a82e493d","block_sha256":"c2aafb0d242df0c3c72ed08d39fe51906ceca25e13f04f22f83b6aa0a82e493d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_669c6693-15a9-43b8-9e77-159a13e7c720"},{"id":"occ_b0df4354a2f5b374e27dd462","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_67f75fa4-a2f9-460c-b5dc-12beed4161e9","section_id":"sec_97ed292d-c8db-40f1-911e-bd6bc12e7cee","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"oEは可能ですが、実装・環境・コンパイラ・通信設計に強く依存します。  \nLPUはこの種の汎用的なMoE学習・推論には向きにくいです。","quote_start":6,"quote_end":73,"text_sha256":"5e438fb2380289b4b356e5bc8db43f9c8476e4de5a3167aa73c74808f4faa201","block_sha256":"5e438fb2380289b4b356e5bc8db43f9c8476e4de5a3167aa73c74808f4faa201","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_67f75fa4-a2f9-460c-b5dc-12beed4161e9"},{"id":"occ_414ce78007be171c2a04f5f9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_682afb3b-6352-49cf-a6f8-67426a52895a","section_id":"sec_89358da1-fc0c-4957-a883-96ccfe3bfb3d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論では、GPUはモデル本体の計算を担当します。","quote_start":0,"quote_end":26,"text_sha256":"6d1a1d5ded4fbc0d18af731b03a479fd0f5d7eb188ac07f835e17ff20894a65c","block_sha256":"6d1a1d5ded4fbc0d18af731b03a479fd0f5d7eb188ac07f835e17ff20894a65c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_682afb3b-6352-49cf-a6f8-67426a52895a"},{"id":"occ_e9b1288ab100f63d722ea0c4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_68426ced-aac3-45b7-b36b-e3dadb27ace6","section_id":"sec_96593a97-1091-44e5-a238-83d7805e4de7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"この「交通整理」こそが、AI推論基盤の新しい競争軸になる。","quote_start":0,"quote_end":29,"text_sha256":"1f79cd917000c912e7322ecfdeddce3b95d3d459558e81cd02efee58a183f88b","block_sha256":"1f79cd917000c912e7322ecfdeddce3b95d3d459558e81cd02efee58a183f88b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_68426ced-aac3-45b7-b36b-e3dadb27ace6"},{"id":"occ_2983c0a3367a33767574eec2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_6ad77bb8-6326-424f-a874-6dce07b29b73","section_id":"sec_0536cbe1-635c-4c53-8177-029476181d49","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":82,"end":90,"exact":"KV cache","quote":"ュレーション\nCPU：ロボット制御、I/O、OS、安全、エージェント制御\nメモリ：動画・センサー・軌道データ・KV cache\nストレージ：ロボットデータ、動画データ、シミュレーションデータ\nネットワーク：クラウド学習、ロボット群管理\nTPU：大規模定型学習・推論\nLPU：低遅延の言語インターフェース\n```","quote_start":27,"quote_end":182,"text_sha256":"1b4db92b716cf2e5e831824eb8e70a547ea31bcbdf9c4ab0e9b7dff0285bce10","block_sha256":"1b4db92b716cf2e5e831824eb8e70a547ea31bcbdf9c4ab0e9b7dff0285bce10","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_6ad77bb8-6326-424f-a874-6dce07b29b73"},{"id":"occ_0016c941a114e10c74d0befe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_6f852019-375c-404e-8a27-c74c18023c51","section_id":"sec_d3f834b4-f2a6-461c-bdf9-d2b6f664ed81","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":75,"end":77,"exact":"推論","quote":"生成、世界モデル、シミュレーション\nCPU：データ管理、ジョブ制御、シミュレーション管理\nLPU：低遅延の言語推論・エージェントループ\n```","quote_start":20,"quote_end":91,"text_sha256":"465a824332acfbc2acc44bba1d2748f0eb182b88a1970e63b1d530ea5689c77c","block_sha256":"465a824332acfbc2acc44bba1d2748f0eb182b88a1970e63b1d530ea5689c77c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_6f852019-375c-404e-8a27-c74c18023c51"},{"id":"occ_d0906c59a443afceb9153b57","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_711070d5-52cd-4fee-a36a-14179cf69fdd","section_id":"sec_ac9ca8d9-aa0d-48ac-a5c1-8f63e31c299d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"### \\1. 低遅延推論に強い","quote_start":0,"quote_end":16,"text_sha256":"1adf6078a7c909e7cda65ebe93f5d883dee14593bf4fed0f67560bd3d2824a32","block_sha256":"1adf6078a7c909e7cda65ebe93f5d883dee14593bf4fed0f67560bd3d2824a32","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_711070d5-52cd-4fee-a36a-14179cf69fdd"},{"id":"occ_4182a52dabb963f0b79a6b38","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_712fd9ac-d191-4b41-a0a8-f153f9c20e94","section_id":"sec_f0bef2d5-8d93-4afd-9496-9acc15ff3d48","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":44,"end":46,"exact":"推論","quote":"生成AIブームは、これまでGPUを中心に語られてきた。大規模言語モデルの学習、画像生成、推論処理の多くは巨大な行列演算であり、そこではNVIDIAを中心とするGPUが圧倒的な存在感を持ってきた。だが、AIの使われ方が「質問に答えるチャットボット」から「外部ツールを使って仕事を進めるAIエージ","quote_start":0,"quote_end":146,"text_sha256":"b6f0ca52f74ce0cad5810b5056a0be6906a06258713165a6be02ca731a1fc756","block_sha256":"b6f0ca52f74ce0cad5810b5056a0be6906a06258713165a6be02ca731a1fc756","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_712fd9ac-d191-4b41-a0a8-f153f9c20e94"},{"id":"occ_38608119e2fddcae0e203576","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_71a7f283-59a6-477a-bfd7-11b6b20ae669","section_id":"sec_7dd33398-5283-46c4-a786-e187e63ed76a","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"```\nGPU：変化に強い万能AI基盤\nTPU：定型化した巨大テンソル処理\nLPU：低遅延LLM推論\nCPU：制御・I/O・エージェント実行\n```","quote_start":0,"quote_end":74,"text_sha256":"3502ebb28eeb45cb43f6e5ec843cec420bfa883e817e8375a72eb5635c705aab","block_sha256":"3502ebb28eeb45cb43f6e5ec843cec420bfa883e817e8375a72eb5635c705aab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_71a7f283-59a6-477a-bfd7-11b6b20ae669"},{"id":"occ_786bb2320d52f9d77cc84544","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_71fe81c8-7d71-4ea8-89a0-4e261039b2fc","section_id":"sec_b25924f8-b6f7-407d-b7d1-b2c30ddb56b3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"普通のLLM推論は、","quote_start":0,"quote_end":10,"text_sha256":"09ddf61debc54487e3eb8704b37cf98b6ca29a73993f29c96e6df51b7396a4bc","block_sha256":"09ddf61debc54487e3eb8704b37cf98b6ca29a73993f29c96e6df51b7396a4bc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_71fe81c8-7d71-4ea8-89a0-4e261039b2fc"},{"id":"occ_7d75c0d0db87566dae9905f5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_72238b2e-bf85-4c7a-8654-158c49569f4d","section_id":"sec_60bc1286-e5cb-4d25-b160-a7b5674c0701","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"GPUメモリは高価で限られています。  \nそのため、AI推論が大規模化すると、","quote_start":0,"quote_end":39,"text_sha256":"d771270d4e3a0675aef1fea932bff8568f75415d19b20a4b800af95a5311c250","block_sha256":"d771270d4e3a0675aef1fea932bff8568f75415d19b20a4b800af95a5311c250","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_72238b2e-bf85-4c7a-8654-158c49569f4d"},{"id":"occ_99563944a268317a17d0bbf3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7347ab30-98d0-4104-a301-62a161641f5f","section_id":"sec_c4d6d78c-8171-4c06-9259-80a17c5190ec","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":13,"end":15,"exact":"推論","quote":"AIエージェント時代には、推論が単純な行列演算だけではなくなります。","quote_start":0,"quote_end":34,"text_sha256":"00d4fab2fabb74b6f822c1a898afdecddb6beb475fda98a0f50704f59cc0c7bc","block_sha256":"00d4fab2fabb74b6f822c1a898afdecddb6beb475fda98a0f50704f59cc0c7bc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7347ab30-98d0-4104-a301-62a161641f5f"},{"id":"occ_61f5a9c99b7847978a3556df","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_73f9121f-4b45-4efc-b762-04251b505e90","section_id":"sec_06fdb9da-c6ca-40b2-8163-fbafd8afe4e4","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"```\n同じモデルを大量に回す\n同じ形の推論を何億回も行う\nレイテンシを極限まで下げる\n1トークンあたりコストを削る\n```","quote_start":0,"quote_end":62,"text_sha256":"c5780e95ae74d2e6b715a84d940b7b32455bc29d615fe78bc3ebdda8fd5d5981","block_sha256":"c5780e95ae74d2e6b715a84d940b7b32455bc29d615fe78bc3ebdda8fd5d5981","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_73f9121f-4b45-4efc-b762-04251b505e90"},{"id":"occ_e71dd653479c48b3a55b4aa2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_749b698e-09a4-4735-893c-45e03b121c2c","section_id":"sec_6acf9d59-e5ea-45c8-9531-a02ed175805a","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":67,"end":75,"exact":"KV cache","quote":"**  \n**CPU/GPU間のスケジューリング**  \n**キュー制御**  \n**バッチング**  \n**KV cache管理**  \n**I/O待ち**  \n**DB・検索・Python実行**  \n**CPUとGPUの同時利用率**","quote_start":12,"quote_end":132,"text_sha256":"c266ae2d9e8fbae1eb1b67df6394024b6da385ecf5f96c5c7eab2b0747a2e839","block_sha256":"c266ae2d9e8fbae1eb1b67df6394024b6da385ecf5f96c5c7eab2b0747a2e839","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_749b698e-09a4-4735-893c-45e03b121c2c"},{"id":"occ_3be662edcef20ad7937f3e7d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_765b9831-fefb-4de0-a207-a8d2aa8b0271","section_id":"sec_1af1f13f-a1e0-4df6-87f0-94d11ad79606","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"```\nAttention\nMLP\n行列積\nテンソル演算\n画像生成\n大量バッチ推論\n```","quote_start":0,"quote_end":45,"text_sha256":"e1c885d2388cea54036d527d301721f7743b74d442b958e9402fde82d0618c85","block_sha256":"e1c885d2388cea54036d527d301721f7743b74d442b958e9402fde82d0618c85","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_765b9831-fefb-4de0-a207-a8d2aa8b0271"},{"id":"occ_faf2e6f9e17cf0163926ad9e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_77c7c93c-2927-44c9-9333-78bd6e15d548","section_id":"sec_a145f732-392b-49c2-bcbd-fd9ff502d3e9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"理由は、GPUよりさらに用途を絞って、**行列演算・テンソル演算・LLM推論の流れ作業に特化しているから**です。","quote_start":0,"quote_end":57,"text_sha256":"62c8eb38bd598d1032925ffa6733a793277cd08b4d14aa81003cfebb1e4a8494","block_sha256":"62c8eb38bd598d1032925ffa6733a793277cd08b4d14aa81003cfebb1e4a8494","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_77c7c93c-2927-44c9-9333-78bd6e15d548"},{"id":"occ_10be34f67f899ae139cf9269","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_78bb4868-3c35-4b1a-b178-5782a273decc","section_id":"sec_ac4d8870-8a91-469f-9618-98d6b795fd19","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":16,"end":18,"exact":"推論","quote":"CPUが制御し、TPU/LPUが推論する場合、処理のたびにデータ移動や同期が発生します。","quote_start":0,"quote_end":44,"text_sha256":"5fc8b88ba16a5a63bcc6557a9c75b8b37d809ee09c61a2b47f19a6a7778a26bb","block_sha256":"5fc8b88ba16a5a63bcc6557a9c75b8b37d809ee09c61a2b47f19a6a7778a26bb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_78bb4868-3c35-4b1a-b178-5782a273decc"},{"id":"occ_31941700f2d5f095747f506c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_78c9476a-c7b9-4b07-acb9-bfffa450ef31","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"## \\3. LLM推論は「prefill」と「decode」で性質が違い、CPU的なスケジューリングが重要になる","quote_start":0,"quote_end":57,"text_sha256":"bae79c4224e3cd68f857ea90e37cf25e0e446c44e79f596e107ebba3477b0f6c","block_sha256":"bae79c4224e3cd68f857ea90e37cf25e0e446c44e79f596e107ebba3477b0f6c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_78c9476a-c7b9-4b07-acb9-bfffa450ef31"},{"id":"occ_f5419457d4bb5bc2499b3540","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_78fabec6-1e2e-4b3e-b5ac-10c95863181d","section_id":"sec_14f77511-f485-4c1a-a2eb-1fa31f400343","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"TPUやLPUが苦手なのは、AIモデルの推論そのものではありません。  \nむしろ、そこには非常に強いです。","quote_start":0,"quote_end":53,"text_sha256":"95bab4af5c73961d062b59e116801b1fba11bd04fbd6c8c66e8ac614b6cb44b2","block_sha256":"95bab4af5c73961d062b59e116801b1fba11bd04fbd6c8c66e8ac614b6cb44b2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_78fabec6-1e2e-4b3e-b5ac-10c95863181d"},{"id":"occ_19ce192acb026f9b843554ee","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7ccdff72-a689-497d-98d3-6cc203731388","section_id":"sec_e3f664ff-c753-4350-90a8-727fb8ca2b6a","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":181,"end":189,"exact":"KV cache","quote":"PU、SmartNIC、MarvellやAstera Labsのような接続レイヤー。  \n第四に、SSD、分散KV cache、RAG基盤、データベース、オブジェクトストレージ。  \n第五に、Cloudflareのような通信・認証・防衛・課金レイヤー。","quote_start":126,"quote_end":252,"text_sha256":"6b85d44a4a8771a6dfcd159331aa459e49a709117a275c2687f6f9b441ff9213","block_sha256":"6b85d44a4a8771a6dfcd159331aa459e49a709117a275c2687f6f9b441ff9213","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7ccdff72-a689-497d-98d3-6cc203731388"},{"id":"occ_02eff775b4ead39b902d12ac","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7cef39e7-292d-4e53-b4dd-d4738122b662","section_id":"sec_fb1996a9-7c62-4745-b530-69bb1b7b1c72","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"Groqは、自社のLPUを2016年に開発した推論専用チップとして説明し、設計選択のすべてを「高速で手頃な推論」に向けていると述べています。またGroqCloudは、LPUベースのスタックを世界中のデータセンターで動かし、低レイテンシ応答を提供すると","quote_start":0,"quote_end":125,"text_sha256":"9ba5442d1ca68a3de8568fa4852035b40aaf7471b82671b4901e2ce64484da47","block_sha256":"9ba5442d1ca68a3de8568fa4852035b40aaf7471b82671b4901e2ce64484da47","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7cef39e7-292d-4e53-b4dd-d4738122b662"},{"id":"occ_fc7935158852902504b4d91b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7db9af14-a92a-4837-87c0-03eac103071f","section_id":"sec_3666ba64-35d9-47b3-bc35-956eceae8da9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"このような用途では、CPUが周辺制御を行い、TPU/LPUが推論を処理する形でGPUを代替しやすいです。","quote_start":0,"quote_end":52,"text_sha256":"6e947203a0eee55b7d4ca4911487512efc3d219a3d80b19f8c652c01d20690b0","block_sha256":"6e947203a0eee55b7d4ca4911487512efc3d219a3d80b19f8c652c01d20690b0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7db9af14-a92a-4837-87c0-03eac103071f"},{"id":"occ_3afd2d2f26a85e1a0b861126","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7ec0dd08-1bc8-4e8a-87bb-0f7d77feb7f7","section_id":"sec_a6b0787b-2203-488c-9016-cdea435706ea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"そして、モデル本体の推論だけをTPU/LPUへ投げます。","quote_start":0,"quote_end":28,"text_sha256":"3c368168edf111515f7f2bf5e89b3697c32eb783f87e4ef36d7bf361d9d2790a","block_sha256":"3c368168edf111515f7f2bf5e89b3697c32eb783f87e4ef36d7bf361d9d2790a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7ec0dd08-1bc8-4e8a-87bb-0f7d77feb7f7"},{"id":"occ_5e7b740bb3f351863cddf7e6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_7f41ee47-6746-4441-8774-a8b4944be1eb","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論には大きく2段階あります。","quote_start":0,"quote_end":18,"text_sha256":"c56d2b8da38eeda76dbaab1a33c3cdeee107fb6a5ef469ae652eb4697f4d555c","block_sha256":"c56d2b8da38eeda76dbaab1a33c3cdeee107fb6a5ef469ae652eb4697f4d555c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_7f41ee47-6746-4441-8774-a8b4944be1eb"},{"id":"occ_e250754c2fdeffcbe42cb84f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_835c930d-5add-4710-9bd8-3e3d10679545","section_id":"sec_6fdcb6d7-61ba-4469-b76b-2d288dd7e005","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"この段階ではLPUは主役ではありません。  \nLPUは基本的に推論側です。","quote_start":0,"quote_end":37,"text_sha256":"3efb65fed436f96e40ebee8e6b0149a6fd36bb65f0e564e2c944c80a9280e8b2","block_sha256":"3efb65fed436f96e40ebee8e6b0149a6fd36bb65f0e564e2c944c80a9280e8b2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_835c930d-5add-4710-9bd8-3e3d10679545"},{"id":"occ_d1d9748873a9783be20bb0bd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_83770f35-e41f-43c9-8e5e-4099f4601fd3","section_id":"sec_1cd762ba-80a0-472e-9983-0964840d2c3e","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":47,"end":49,"exact":"推論","quote":"```\nGPU：大量の計算を動的にさばく\nTPU：行列計算を専用配列で流す\nLPU：言語モデル推論を決定論的なベルトコンベアで流す\n```","quote_start":0,"quote_end":69,"text_sha256":"037b8f0dc195ed8036fdb382973beca5860bca1747add4b829ad56ae78987f77","block_sha256":"037b8f0dc195ed8036fdb382973beca5860bca1747add4b829ad56ae78987f77","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_83770f35-e41f-43c9-8e5e-4099f4601fd3"},{"id":"occ_b4e92de2e9415b415f228b87","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_848a14d4-d7cd-41f9-b1d2-3562c1f57a49","section_id":"sec_96593a97-1091-44e5-a238-83d7805e4de7","layer":"body","character_id":null,"count":9,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論には、大きく分けて **prefill** と **decode** がある。prefillは入力全体を処理して最初のトークンを出す段階で、GPUを高効率に使いやすい。一方、decodeは1トークンず","quote_start":0,"quote_end":105,"text_sha256":"98a36387fd454c37ac14b3208524f83c23d6f29be7c368c7a128930b1a066393","block_sha256":"98a36387fd454c37ac14b3208524f83c23d6f29be7c368c7a128930b1a066393","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_848a14d4-d7cd-41f9-b1d2-3562c1f57a49"},{"id":"occ_bb78f5cee0aa76882b750f3c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_856cee94-911f-475f-bff9-ad00f93613aa","section_id":"sec_eeb195b5-bdb9-4c3c-84e5-7e4ec367e5ac","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"```\nGoogle Cloud上の大量VLA推論\n巨大VLM/VLAの学習\n定型化したテンソル計算\n```","quote_start":0,"quote_end":54,"text_sha256":"b68e94351543d486fae0367e52105c8aef62a4089834a3972a787e93eca396e0","block_sha256":"b68e94351543d486fae0367e52105c8aef62a4089834a3972a787e93eca396e0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_856cee94-911f-475f-bff9-ad00f93613aa"},{"id":"occ_1c62b43567054d6cf4674da2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_862fb93c-6aa1-4818-9781-357b369a9664","section_id":"sec_60bc1286-e5cb-4d25-b160-a7b5674c0701","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"en** は、GPUメモリが限られている環境で、GPU・CPU・ディスクのメモリと計算資源を組み合わせてLLM推論を行う研究です。論文では、FlexGenはGPU、CPU、ディスクからメモリと計算を集約し、テンソルの保存・アクセスパターンを最適化することで、16GB GPU上でOPT-175Bの推論を可能にしたと","quote_start":7,"quote_end":164,"text_sha256":"1609681aa995259f3dd6bf3f1d7f2ba84613e93a9ae1805cec6c690876f1a8aa","block_sha256":"1609681aa995259f3dd6bf3f1d7f2ba84613e93a9ae1805cec6c690876f1a8aa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_862fb93c-6aa1-4818-9781-357b369a9664"},{"id":"occ_86e0669e3b6121ce5244af3a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_87c9a3ec-af4a-43d0-9387-79686d653f61","section_id":"sec_e55e644a-5ce1-449c-9c0c-aab7e7170dba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"**1つ目のマイクロバッチがCPU処理を終えたら、GPU推論へ流す。  \nその間に、2つ目のマイクロバッチはCPU処理を進める。**","quote_start":0,"quote_end":66,"text_sha256":"873aed054bd0234f0f83aa42bef1add408742323e26d86b56c64bd17fb80641b","block_sha256":"873aed054bd0234f0f83aa42bef1add408742323e26d86b56c64bd17fb80641b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_87c9a3ec-af4a-43d0-9387-79686d653f61"},{"id":"occ_f3eb01de00550393912ea482","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_890e3078-2ec8-4a6d-a11d-dcb45cec1a3d","section_id":"sec_33a7815c-4a5b-42c7-80f0-c5b08a2f1551","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"GPUは学習にも推論にも画像生成にもHPCにも使えます。  \nその分、汎用性のための余地があります。","quote_start":0,"quote_end":50,"text_sha256":"55bbbf147fff0aa723434c5bd3b0d4a302fce447c027ee976dcccd2563c437d2","block_sha256":"55bbbf147fff0aa723434c5bd3b0d4a302fce447c027ee976dcccd2563c437d2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_890e3078-2ec8-4a6d-a11d-dcb45cec1a3d"},{"id":"occ_215c6fba160088c215e6603b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_8987c123-d00f-4c3e-81d7-9f0848667781","section_id":"sec_06fdb9da-c6ca-40b2-8163-fbafd8afe4e4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":63,"end":65,"exact":"推論","quote":"最先端モデル = GPU優位**  \n**定型化した巨大テンソル処理 = TPU優位**  \n**低遅延LLM推論 = LPU優位**","quote_start":8,"quote_end":75,"text_sha256":"7cd07f9eb34d72402a3f3919683d49248c7810eef549fba809ff927d53a5ac9b","block_sha256":"7cd07f9eb34d72402a3f3919683d49248c7810eef549fba809ff927d53a5ac9b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_8987c123-d00f-4c3e-81d7-9f0848667781"},{"id":"occ_23671b6df50d04c34889aedc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_8e63f0fe-bef0-462a-bdf8-963960f9c829","section_id":"sec_58e85375-d53a-4052-9dcc-3e885bf11c81","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"つまり、AI推論ではGPUだけを強くしても、CPU・ホスト側が遅いとGPUを十分に使い切れません。","quote_start":0,"quote_end":49,"text_sha256":"c03539941082723acc0d795b4536ce72b92be201fe982ffa06ad2e2dfe3aef05","block_sha256":"c03539941082723acc0d795b4536ce72b92be201fe982ffa06ad2e2dfe3aef05","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_8e63f0fe-bef0-462a-bdf8-963960f9c829"},{"id":"occ_c66f8932c067eddd518eef55","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9014cdce-16ee-46af-81e5-503b33c7cdc2","section_id":"sec_1d659e9b-c444-4464-bc56-f4c9ea399799","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"**AI全体の主役**というより、  \n**LLM推論の一部、特に低遅延応答の専用エンジン**","quote_start":0,"quote_end":47,"text_sha256":"2d2d7c1f689456502f5e1e9876c3467fe66dbc291c927df4aaf9bc98caf8e08e","block_sha256":"2d2d7c1f689456502f5e1e9876c3467fe66dbc291c927df4aaf9bc98caf8e08e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9014cdce-16ee-46af-81e5-503b33c7cdc2"},{"id":"occ_1a29eb0afb2e8c13239e9ee4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_90d516aa-dfce-4b8e-97a0-98090c2dd9d7","section_id":"sec_05b8709c-00a4-4ff3-a0fd-502fd1080c73","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":55,"end":63,"exact":"KV cache","quote":"```\n独自Attention\nFlashAttention系\n独自MoEルーティング\n量子化カーネル\n特殊なKV cache管理\nSparse演算\n低精度演算\n推論最適化カーネル\nカスタムオペレーター\n```","quote_start":0,"quote_end":105,"text_sha256":"fc00bc3b441108ecdaa2149dbdc22e795addb73f376b32685dbf3c7433f05052","block_sha256":"fc00bc3b441108ecdaa2149dbdc22e795addb73f376b32685dbf3c7433f05052","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_90d516aa-dfce-4b8e-97a0-98090c2dd9d7"},{"id":"occ_51a01875aaead51a512fd04d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_90d78e86-064d-4b0b-88bc-be41a0933dc1","section_id":"sec_33a7815c-4a5b-42c7-80f0-c5b08a2f1551","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"### \\2. 推論専用なので無駄を削れる","quote_start":0,"quote_end":21,"text_sha256":"22426e8a8223eafa9f805d5b600d74dd8a720793cf3f251c06db92dcc36ef180","block_sha256":"22426e8a8223eafa9f805d5b600d74dd8a720793cf3f251c06db92dcc36ef180","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_90d78e86-064d-4b0b-88bc-be41a0933dc1"},{"id":"occ_0f7e0a21bb5529c88e437811","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_920f1871-3d9d-4722-b57e-11eb401e7239","section_id":"sec_3bb93f54-fe5c-4b6e-bcfd-3a542b01767b","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":6,"end":14,"exact":"KV cache","quote":"**第4層：KV cache / RAG / ストレージ層**  \nSSD、分散キャッシュ、オブジェクトストレージ、高速ネットワーク、データベース。","quote_start":0,"quote_end":74,"text_sha256":"117f20cc12493eed50a4519696c900381965a2bb9b016c9a9c628578e35962eb","block_sha256":"117f20cc12493eed50a4519696c900381965a2bb9b016c9a9c628578e35962eb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_920f1871-3d9d-4722-b57e-11eb401e7239"},{"id":"occ_200b326cef85292818868e86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_950efb09-c31b-4c1f-9a8d-4da070c43b7c","section_id":"sec_72fc0180-5c45-4d8d-90d4-e71e855ca82a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"こうした処理は、単純なLLM推論とは違います。","quote_start":0,"quote_end":23,"text_sha256":"a118f26e8d571c13fd03ebdd7c5a84f6aed09d7ab557094b482b581f616045e7","block_sha256":"a118f26e8d571c13fd03ebdd7c5a84f6aed09d7ab557094b482b581f616045e7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_950efb09-c31b-4c1f-9a8d-4da070c43b7c"},{"id":"occ_9a666bf1955e3b26aeca6819","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_960ff950-7947-4a24-9a4c-2daa0912b90d","section_id":"sec_d2c25cb1-1262-4e66-9128-a39dd33e4104","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"## AIエージェント推論","quote_start":0,"quote_end":13,"text_sha256":"604df2a28c299cf60df3f7c50ea3ae5b0ea6c058434709cc7a455e7f4b58fc5d","block_sha256":"604df2a28c299cf60df3f7c50ea3ae5b0ea6c058434709cc7a455e7f4b58fc5d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_960ff950-7947-4a24-9a4c-2daa0912b90d"},{"id":"occ_d6733a91d6385d6251e7b166","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_989245e8-a6c6-45c1-b464-064edaeb8bc8","section_id":"sec_f62de756-b7cf-48ae-93c1-d612f2184b62","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":114,"end":116,"exact":"推論","quote":"\n**TPUは、テンソル計算を「専用工場化」したGoogle系AIアクセラレータ。**  \n**LPUは、言語推論を「低遅延ベルトコンベア化」した専用アクセラレータ。**","quote_start":59,"quote_end":144,"text_sha256":"74a8b34b305f47f918ff6d979b3721806d1b3fa3e765f62328934bad40eb9a60","block_sha256":"74a8b34b305f47f918ff6d979b3721806d1b3fa3e765f62328934bad40eb9a60","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_989245e8-a6c6-45c1-b464-064edaeb8bc8"},{"id":"occ_8b1bcdaa1ed4c57ff5b99ff8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_99d673e5-8f2e-46b4-be76-b336986cfae3","section_id":"sec_ac4d8870-8a91-469f-9618-98d6b795fd19","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"AIエージェントのように短い推論とツール処理を何度も繰り返すと、この往復がボトルネックになることがあります。","quote_start":0,"quote_end":54,"text_sha256":"9939f09a352ddcd7e0061dcab519da1f0d7938605e6f4a09f40fc342afdf8171","block_sha256":"9939f09a352ddcd7e0061dcab519da1f0d7938605e6f4a09f40fc342afdf8171","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_99d673e5-8f2e-46b4-be76-b336986cfae3"},{"id":"occ_715660e850b9f20f9b0e47d5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9a7bc379-c46e-4381-89d5-1a94bb1c257e","section_id":"sec_e4043154-47b4-46ab-aebe-9462b2a82359","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":97,"end":105,"exact":"KV cache","quote":" Large Language Model Serving with PagedAttention”** は、KV cacheメモリがリクエストごとに大きく、動的に増減し、非効率に管理すると断片化や重複でバッチサイズが制限されると指摘しています。PagedAttentionはOSの仮想メモリ/ページングのようにKV cach","quote_start":42,"quote_end":205,"text_sha256":"a68a650585bf6fbcf6259957f9d905e93497ea34f6d320431026ceed1f081989","block_sha256":"a68a650585bf6fbcf6259957f9d905e93497ea34f6d320431026ceed1f081989","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9a7bc379-c46e-4381-89d5-1a94bb1c257e"},{"id":"occ_af6ee6a7a369bb38a6ff47de","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9baffaea-bfb0-4b86-9e14-3aafb4ff9c68","section_id":"sec_6acf9d59-e5ea-45c8-9531-a02ed175805a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"この意味でCOMBとMASは、単なる論文上のスケジューリング技術ではなく、**AI推論インフラがGPU中心からCPU/GPU協調型へ移る兆候**として読むべきだと思います。","quote_start":0,"quote_end":86,"text_sha256":"6d8acb0d1ee84260ce9f41ad0152726eb09ebed6fe31cda9322497b177eed6a8","block_sha256":"6d8acb0d1ee84260ce9f41ad0152726eb09ebed6fe31cda9322497b177eed6a8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9baffaea-bfb0-4b86-9e14-3aafb4ff9c68"},{"id":"occ_5325da6253678b504fca6e89","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9c5f7894-3e32-44a4-9ae3-e49100603023","section_id":"sec_33a7815c-4a5b-42c7-80f0-c5b08a2f1551","layer":"code","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"```\n学習より推論\n汎用AIよりLLM推論\n最大柔軟性より低遅延\n大量機能より決まった推論パス\n```","quote_start":0,"quote_end":52,"text_sha256":"48af3afcaa79efb51a79d5326ad3218cc5d50119c34970bcdfaac3487789fe8a","block_sha256":"48af3afcaa79efb51a79d5326ad3218cc5d50119c34970bcdfaac3487789fe8a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9c5f7894-3e32-44a4-9ae3-e49100603023"},{"id":"occ_c25f348cc2009c74e3a3d138","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9d14f88e-b626-4b66-afca-190c0fd345f6","section_id":"sec_f5fb003a-62fe-48c2-a63d-107ee26859cb","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"ここにもCPU的な世界がある。  \nAI推論は、単なる行列計算ではなく、巨大なメモリ管理システムになっている。KV cacheをどこに置くか、どのリクエストに割り当てるか、いつ解放するか、どこまで共有するか。これはOS、メモリ管理、スケジューリ","quote_start":0,"quote_end":122,"text_sha256":"ea06e4c770daf3398e252df9ddad0c25679c28579f83d40728d9b1a049c0c769","block_sha256":"ea06e4c770daf3398e252df9ddad0c25679c28579f83d40728d9b1a049c0c769","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9d14f88e-b626-4b66-afca-190c0fd345f6"},{"id":"occ_8e8a879af44360925ff1faf6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9d69e0a9-e937-4863-a07c-3d67c92eafe0","section_id":"sec_14f77511-f485-4c1a-a2eb-1fa31f400343","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":45,"end":47,"exact":"推論","quote":"```\nCPU：行動管理・OS・I/O・ツール・分岐\nGPU/TPU/LPU：モデル本体の推論\n```","quote_start":0,"quote_end":51,"text_sha256":"493400a8efa8bdcd5df29d304a7dfa1aa590115f0ba01727c57c91cd1202270e","block_sha256":"493400a8efa8bdcd5df29d304a7dfa1aa590115f0ba01727c57c91cd1202270e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9d69e0a9-e937-4863-a07c-3d67c92eafe0"},{"id":"occ_25762ac7c2758079938d1220","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_9e951b37-452a-4da6-a5d9-5785c2f52e9c","section_id":"sec_5760d85c-4b90-434d-a30a-fe951b607da9","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":45,"end":47,"exact":"推論","quote":"```\n巨大なTransformer\n大規模VLM学習\n大規模VLA学習\n定型的なテンソル推論\nGoogle Cloud上の訓練\n大量バッチ推論\n```","quote_start":0,"quote_end":76,"text_sha256":"5e175d43bcd5473e0a6717163fb98377bb2c9bc3cfcd252385ca16241b98271f","block_sha256":"5e175d43bcd5473e0a6717163fb98377bb2c9bc3cfcd252385ca16241b98271f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_9e951b37-452a-4da6-a5d9-5785c2f52e9c"},{"id":"occ_f1ff432b7e15e7ba11757d61","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a04b3224-37e4-4faa-a361-f7794c5126e2","section_id":"sec_06fdb9da-c6ca-40b2-8163-fbafd8afe4e4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"一方、TPUやLPUの優位性は、**推論が産業化・定型化・大量化したとき**に強くなります。","quote_start":0,"quote_end":46,"text_sha256":"2afbb41f82082b0a2ad89c32815e17ce3fd549817cd6a8789b9ed4b43d3582a0","block_sha256":"2afbb41f82082b0a2ad89c32815e17ce3fd549817cd6a8789b9ed4b43d3582a0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a04b3224-37e4-4faa-a361-f7794c5126e2"},{"id":"occ_ea934264fe218192afc49c86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a073d8fe-b2a4-440d-84c6-b38ca5968dcc","section_id":"sec_60bc1286-e5cb-4d25-b160-a7b5674c0701","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":48,"end":56,"exact":"KV cache","quote":"**LMCache** はさらに現代的です。2025年の論文で、vLLMやSGLangが生成したKV cacheをGPU外に抽出・保存し、エンジンやクエリ間で共有する仕組みです。cache offloading、prefix reuse、prefill-decode disaggregationに対応するとされ","quote_start":0,"quote_end":156,"text_sha256":"7a4dca4aa8f3bc4cb33d03d4e2fbf4fcdda5da3cdf85c82c587691d2fbec902d","block_sha256":"7a4dca4aa8f3bc4cb33d03d4e2fbf4fcdda5da3cdf85c82c587691d2fbec902d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a073d8fe-b2a4-440d-84c6-b38ca5968dcc"},{"id":"occ_5ea04089fb501d7ef5e5321d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a0e69dd9-0043-4fc1-b6a7-a5cacca9bd6b","section_id":"sec_1cd762ba-80a0-472e-9983-0964840d2c3e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":77,"end":79,"exact":"推論","quote":"ん。  \n特に大規模学習、広範なAIモデル、画像生成、汎用HPCではGPUの方が柔軟です。LPUは **LLM推論、特に低遅延・高速トークン生成** に寄せた専用機と見るのが自然です。","quote_start":22,"quote_end":114,"text_sha256":"44e144e67f082dd36bb6ef3bc823e9bdcf27bf5ba48197ddeb740e1a4456aae6","block_sha256":"44e144e67f082dd36bb6ef3bc823e9bdcf27bf5ba48197ddeb740e1a4456aae6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a0e69dd9-0043-4fc1-b6a7-a5cacca9bd6b"},{"id":"occ_8c154174cba13201804f1ea6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a85f9f94-52a6-41d8-be9d-4e8c8935c67f","section_id":"sec_a1caf1d1-d45f-4c70-b24f-2f29acd7181d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":107,"end":109,"exact":"推論","quote":"の主力**  \n**TPU：定型化した巨大テンソル計算を高効率に回す専用工場**  \n**LPU：低遅延の言語推論・計画ループを回す補助エンジン**","quote_start":52,"quote_end":126,"text_sha256":"abb559d12ecbccefb6e5919c091a8d36b50183886a593f4b805dd8b892bb271a","block_sha256":"abb559d12ecbccefb6e5919c091a8d36b50183886a593f4b805dd8b892bb271a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a85f9f94-52a6-41d8-be9d-4e8c8935c67f"},{"id":"occ_3652f3e9a3607d80c0f18250","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a8e64b1e-5923-4eaa-9305-503f07ddac28","section_id":"sec_6f5049f4-398b-47d8-be63-701f19f1f387","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"```\n学習\n推論\n画像\n動画\n音声\n3D\nHPC\nロボティクス\nシミュレーション\n```","quote_start":0,"quote_end":45,"text_sha256":"e1a815848eebc96d1cbc8848f7a727d482343c0ff8a256552ad3159efef8e9c8","block_sha256":"e1a815848eebc96d1cbc8848f7a727d482343c0ff8a256552ad3159efef8e9c8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a8e64b1e-5923-4eaa-9305-503f07ddac28"},{"id":"occ_d57395167b99fde5c0ff0810","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_a973d27c-6b2f-48e4-b7b5-40c6c9f3dd3a","section_id":"sec_58e85375-d53a-4052-9dcc-3e885bf11c81","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"この論文は、PCIe接続のA100/H100と、CPU-GPUが密結合されたGH200のようなシステムでLLM推論を分析しています。結果として、GH200のような密結合システムは大きなバッチサイズでは高速になる一方、**低〜中バッチ領域ではCPU側の性能やカーネル起動・キューイング時間が推論レイテンシに影響する*","quote_start":0,"quote_end":157,"text_sha256":"014ad146345f824ace936ec24ea09cf09f42815de50de33bcaba9613fae889b5","block_sha256":"014ad146345f824ace936ec24ea09cf09f42815de50de33bcaba9613fae889b5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_a973d27c-6b2f-48e4-b7b5-40c6c9f3dd3a"},{"id":"occ_e4d557a5672569b1956daa04","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_abdc012d-f825-4697-b8d7-93e1fa053e21","section_id":"sec_2b70b9dc-f533-4aae-9151-6d5d17fac4ba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"### \\1. CPUで直接推論する需要","quote_start":0,"quote_end":20,"text_sha256":"b8a7e8eecd9dd39d678379446a5f5ca462b0e4b07b870f7cca5177bd4100ed14","block_sha256":"b8a7e8eecd9dd39d678379446a5f5ca462b0e4b07b870f7cca5177bd4100ed14","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_abdc012d-f825-4697-b8d7-93e1fa053e21"},{"id":"occ_2facef7d021e1dd9d2a70e4a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_adc98911-24d7-4cd3-9165-644b52dbe2c4","section_id":"sec_946dedd1-8094-428b-99e9-a309a87cc3fd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":9,"end":11,"exact":"推論","quote":"## 通常のLLM推論","quote_start":0,"quote_end":11,"text_sha256":"1ede2796f38cac787856cf741a29da65c5adf3541a41108950f111d7c81ab3fa","block_sha256":"1ede2796f38cac787856cf741a29da65c5adf3541a41108950f111d7c81ab3fa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_adc98911-24d7-4cd3-9165-644b52dbe2c4"},{"id":"occ_6bc79de7b144f4a9fb7440d6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ae5730fd-d93f-4b21-b5f3-5ba282bcd939","section_id":"sec_217a77bd-aa6e-42cf-820a-bb48d5cd649b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":35,"end":37,"exact":"推論","quote":"**「LLMの行列演算をCPUがGPUから奪う」ではなく、  \n「AI推論がサービス化・エージェント化するほど、GPUの外側にあるCPU制御・メモリ管理・ツール実行・I/O処理が巨大化する」**","quote_start":0,"quote_end":97,"text_sha256":"4f15e4b0ea96b6e0eab9c2a1047de275984000b4b834df491f8584fd1c634eee","block_sha256":"4f15e4b0ea96b6e0eab9c2a1047de275984000b4b834df491f8584fd1c634eee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ae5730fd-d93f-4b21-b5f3-5ba282bcd939"},{"id":"occ_cc5dab90fc50ed32af321fc2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_aef43064-3091-416a-9447-4de008b99191","section_id":"sec_eeb195b5-bdb9-4c3c-84e5-7e4ec367e5ac","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"庫ロボットの定型ピッキング\n工場内の検査ロボット\n家庭内の定型タスク\n音声対話付きロボット\n特定業務向けVLA推論\n```","quote_start":5,"quote_end":66,"text_sha256":"86afa95d30b166e1067da71d7f7e86839edccef4fc5b8e7cc70bb4c0ccf2a229","block_sha256":"86afa95d30b166e1067da71d7f7e86839edccef4fc5b8e7cc70bb4c0ccf2a229","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_aef43064-3091-416a-9447-4de008b99191"},{"id":"occ_5b25c408afacaf493d07e444","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_b37b9897-625b-4cf6-ac71-d4cc37346a2f","section_id":"sec_decb4039-9a16-4343-928a-9469098196f4","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"```\n形が安定した大規模学習\n大規模推論\n推薦モデル\n検索・広告・ランキング\nGoogle Cloud上のAI\n```","quote_start":0,"quote_end":60,"text_sha256":"dd1f675a76d7321b72c92447bfff731380f6401cbb81531155606644e80b63d3","block_sha256":"dd1f675a76d7321b72c92447bfff731380f6401cbb81531155606644e80b63d3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_b37b9897-625b-4cf6-ac71-d4cc37346a2f"},{"id":"occ_d36af25a5f07ae42980f94da","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_b3cdec30-b940-441c-909a-2738a0172df6","section_id":"sec_0ffdfacd-1560-429f-aa48-3ea037faae70","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"どのモデル構造が主流になるか、どの推論方式が勝つか、どのデータ型が標準になるか、まだ変化しています。","quote_start":0,"quote_end":50,"text_sha256":"3974a0165c18ab1f69c14c52dbda17d9eff1be8c4c0929432dca50e255ea37ab","block_sha256":"3974a0165c18ab1f69c14c52dbda17d9eff1be8c4c0929432dca50e255ea37ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_b3cdec30-b940-441c-909a-2738a0172df6"},{"id":"occ_3b06e63d14671edf0d7dbed3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_b472c846-a8c8-4635-8ffb-8eb102b62c38","section_id":"sec_08785f72-2569-42f3-a55a-aaecced92b31","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"## \\11. AI推論で見るCPUとGPUの役割","quote_start":0,"quote_end":25,"text_sha256":"4fae0d51f6d82989c29d7dc650f5e72e42c222d696f2898c421e251171aca49f","block_sha256":"4fae0d51f6d82989c29d7dc650f5e72e42c222d696f2898c421e251171aca49f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_b472c846-a8c8-4635-8ffb-8eb102b62c38"},{"id":"occ_9f3c964fb01b5795a6526c47","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_b6d17814-2f65-4db9-8d17-72788c7f63f2","section_id":"sec_6c21cd07-3fda-4c7d-8a28-b122b32abee4","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":46,"end":55,"exact":"Inference","quote":"2025年の論文 **“Characterizing and Optimizing LLM Inference Workloads on CPU-GPU Coupled Architectures”** は、PCIe接続のA100/H100と、密結合型のGH200を比較し、GH200は大きなバッチでは高速だが","quote_start":0,"quote_end":155,"text_sha256":"d7ab0ddd9334ecd94196763e87443f39fbeece6c9b54c0a27b23dda8c43d9e65","block_sha256":"d7ab0ddd9334ecd94196763e87443f39fbeece6c9b54c0a27b23dda8c43d9e65","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_b6d17814-2f65-4db9-8d17-72788c7f63f2"},{"id":"occ_2d8f3665fd8b242a43602b01","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_b6fff29f-521a-4e79-826d-a122fa941afd","section_id":"sec_e4043154-47b4-46ab-aebe-9462b2a82359","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":29,"end":37,"exact":"KV cache","quote":"## \\4. vLLM / PagedAttention：KV cache管理は推論の中心問題","quote_start":0,"quote_end":47,"text_sha256":"23add5051b53312817c054567c1f86e871bf5a1fedabe4c92afcb5112d39e768","block_sha256":"23add5051b53312817c054567c1f86e871bf5a1fedabe4c92afcb5112d39e768","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_b6fff29f-521a-4e79-826d-a122fa941afd"},{"id":"occ_514db1fba25cc47d3ede7f1d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ba5cd6fa-d08a-4312-bbab-8ebef0f8cba0","section_id":"sec_08bb8dd1-0363-42f6-88fc-21ce5f968155","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":47,"end":49,"exact":"推論","quote":"```\nCPU：司令塔\nGPU：万能AI工場\nTPU：巨大テンソル専用工場\nLPU：低遅延言語推論エンジン\n```","quote_start":0,"quote_end":57,"text_sha256":"5fe5f8e0035cdca922ba3f03f237b8d9dae1341cee9fe338dec0bfa14465e02a","block_sha256":"5fe5f8e0035cdca922ba3f03f237b8d9dae1341cee9fe338dec0bfa14465e02a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ba5cd6fa-d08a-4312-bbab-8ebef0f8cba0"},{"id":"occ_21f7603c382fd0d9e991ac6e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ba9d55c1-9f11-4fe5-b24c-863ffa0ff674","section_id":"sec_d912a132-d2e4-4a4e-8c9e-50cea819d242","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論では、両方とも重要です。","quote_start":0,"quote_end":16,"text_sha256":"1ece49864f8bce3053e7390e180877f777648f88dc11e94d70f2410c04f2ca06","block_sha256":"1ece49864f8bce3053e7390e180877f777648f88dc11e94d70f2410c04f2ca06","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ba9d55c1-9f11-4fe5-b24c-863ffa0ff674"},{"id":"occ_e450c365596e9c317abcbe4f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_bea1866b-60e3-42a9-8fd6-4ec2c1651994","section_id":"sec_e4d89d65-63e2-47fd-8f00-443bd9c791af","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"なぜなら、エージェントは単純なLLM推論だけでなく、ツール呼び出し、検索、DB、API、Python実行、ファイル操作などを含むからです。","quote_start":0,"quote_end":69,"text_sha256":"bd633e67b386d63f208c01babfa4b451d03190f9e8c8f8d095df5b32a8500584","block_sha256":"bd633e67b386d63f208c01babfa4b451d03190f9e8c8f8d095df5b32a8500584","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_bea1866b-60e3-42a9-8fd6-4ec2c1651994"},{"id":"occ_c043de4afc3972c00c0e7e24","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_bef6b692-46a8-4f3a-b41b-9d63d7d520b0","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"ここでのポイントは、**AI推論の性能はGPU FLOPSだけではなく、リクエストをどう並べるか、どのタイミングでGPUに流すか、KV cacheをどう扱うかで大きく変わる**ということです。  \nこの「並べる・切る・待たせない・詰","quote_start":0,"quote_end":116,"text_sha256":"3b663a0d8930cae0b468475f9ae682a2e52116cbef7ad5c61950bef93c40669d","block_sha256":"3b663a0d8930cae0b468475f9ae682a2e52116cbef7ad5c61950bef93c40669d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_bef6b692-46a8-4f3a-b41b-9d63d7d520b0"},{"id":"occ_54a2e96483f0369472eba14b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_c037b0f6-efe8-40c6-9207-26b67a3b1f15","section_id":"sec_8a97483f-1163-43f1-a68a-c680a2c1bddf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"一方TPUは、systolic arrayを中心に行列演算を規則的に流す設計です。  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 \nLPUは「言語推論の高速ベルトコンベア」であり、フィジカルAIの全体を置き換えるものではありません。","quote_start":2,"quote_end":100,"text_sha256":"2bb0b1a8785c6f307bbdf31d146eee83ba6a9a01be735008a8e703989a5748b6","block_sha256":"2bb0b1a8785c6f307bbdf31d146eee83ba6a9a01be735008a8e703989a5748b6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_c56b3986-dd38-4525-8673-161c8dfd5c3c"},{"id":"occ_100dd060ee0aeaba37ba5876","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_c75a3097-878e-4cf8-8c04-64c1bfb5491d","section_id":"sec_f6dc8e3a-bd25-4918-897c-5432256d92c4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":40,"end":42,"exact":"推論","quote":"これは「AIエージェント時代のCPU需要」の理論的な中核です。  \n従来のLLM推論ではGPUが主役ですが、AIエージェントでは以下が増えます。","quote_start":0,"quote_end":72,"text_sha256":"1482bd506060d82aedbe35b7a626bd462b4ce77fe6ebe8358ff57a093635d9ee","block_sha256":"1482bd506060d82aedbe35b7a626bd462b4ce77fe6ebe8358ff57a093635d9ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_c75a3097-878e-4cf8-8c04-64c1bfb5491d"},{"id":"occ_f0e9dd8adbfa3cdae0ccd23a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_c850caab-7ff9-46be-89a9-feb15ab5e31f","section_id":"sec_f5fb003a-62fe-48c2-a63d-107ee26859cb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"FlexGenのような研究も、GPUメモリだけでは足りない状況で、GPU・CPU・ディスクを組み合わせてLLM推論を実行する方向性を示している。これは、GPU HBMだけで全てを抱える時代から、CPU DRAMやストレージも含めた階層型メモリの時代へ進むことを意味する。([arXiv](https://arxiv","quote_start":0,"quote_end":157,"text_sha256":"ec23472332d33a4a3121430cbf6396f049ee7c7547b8057b7387c9fce612bb85","block_sha256":"ec23472332d33a4a3121430cbf6396f049ee7c7547b8057b7387c9fce612bb85","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_c850caab-7ff9-46be-89a9-feb15ab5e31f"},{"id":"occ_8a3108dc44be2de11c251243","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_c9ef8297-48d5-4660-9b92-9364843c453a","section_id":"sec_8be67a98-017a-467c-92d9-bdd854efa78c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"このとき、1回ごとの推論レイテンシが小さいと、エージェント全体が速くなります。","quote_start":0,"quote_end":39,"text_sha256":"e525970d1bf6f0379dc19b5c621ee295e80d84c8adbd0b6557f42c2a3a0578f1","block_sha256":"e525970d1bf6f0379dc19b5c621ee295e80d84c8adbd0b6557f42c2a3a0578f1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_c9ef8297-48d5-4660-9b92-9364843c453a"},{"id":"occ_36a5b996c3364e10e5d59462","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ca80a607-e4c3-4be8-87a3-5ca6435af13b","section_id":"sec_e4043154-47b4-46ab-aebe-9462b2a82359","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":26,"end":34,"exact":"KV cache","quote":"これはCPU需要に直接つながります。  \nなぜなら、KV cache管理は単純な行列計算ではなく、","quote_start":0,"quote_end":49,"text_sha256":"aacf5c33986139365c8c9a6680e322333a1ba678b29eaa344c953035a6cade95","block_sha256":"aacf5c33986139365c8c9a6680e322333a1ba678b29eaa344c953035a6cade95","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ca80a607-e4c3-4be8-87a3-5ca6435af13b"},{"id":"occ_5f2959ca392d3868c9bf09d3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ca9b5b79-83e6-4341-b9dc-f47c1f8a4f5a","section_id":"sec_17a23976-3f69-4cb5-83f7-34584d9112fb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"LPUは、LLM推論のトークン生成を高速・安定・低遅延に流すことに寄っています。","quote_start":0,"quote_end":40,"text_sha256":"749051d74c38c3536a0c1d3d75fec082f29518e5d30d0c8a828b40ce3cff1ac7","block_sha256":"749051d74c38c3536a0c1d3d75fec082f29518e5d30d0c8a828b40ce3cff1ac7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ca9b5b79-83e6-4341-b9dc-f47c1f8a4f5a"},{"id":"occ_e6936c9c15cb929178b8be36","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cad63319-86ca-421a-b083-d04631ca0ae4","section_id":"sec_ab5faba9-8f69-4a16-b5cc-1dee74938a95","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"つまり、**軽い推論リクエストと、重いエージェント処理リクエストを同じキューで潰し合わないようにする**仕組みです。","quote_start":0,"quote_end":58,"text_sha256":"1afc7a5a9648b493582afed028b987365e3a9ad98d662d37e152f700e79652cc","block_sha256":"1afc7a5a9648b493582afed028b987365e3a9ad98d662d37e152f700e79652cc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cad63319-86ca-421a-b083-d04631ca0ae4"},{"id":"occ_ea1d1b0257b4a68f67221b18","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cb8aa81b-506f-4d48-9693-28ab6a1a807f","section_id":"sec_06fdb9da-c6ca-40b2-8163-fbafd8afe4e4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"なぜなら、AIの最先端はまだ変化が激しく、学習・推論・マルチモーダル・ロボティクス・シミュレーションまで全部を支える必要があるからです。  \nこの「何にでも対応できるAI工場」というポジションは、GPUの最大の強みです。","quote_start":0,"quote_end":110,"text_sha256":"bfa44f44e37aba25312b76f5de407585c4e81fede6bf9e7e8fc305dea82542c5","block_sha256":"bfa44f44e37aba25312b76f5de407585c4e81fede6bf9e7e8fc305dea82542c5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cb8aa81b-506f-4d48-9693-28ab6a1a807f"},{"id":"occ_39efeaf10090e52b2289e254","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cbc94266-c7d8-42e0-94c0-fabfc098e166","section_id":"sec_37862236-9acb-43b9-a9e5-b7a32d79825b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"GPUは、大規模学習、大規模推論、画像生成、動画生成、音声生成、科学計算、ロボティクス、シミュレーションまで広く使えます。","quote_start":0,"quote_end":61,"text_sha256":"4e0685c3d9b968d428c6a35d2cd30e71442b53ab972772a6656edcfaff33ad82","block_sha256":"4e0685c3d9b968d428c6a35d2cd30e71442b53ab972772a6656edcfaff33ad82","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cbc94266-c7d8-42e0-94c0-fabfc098e166"},{"id":"occ_f99177262f75a888b5784c9a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cc0d527c-d707-4cc4-900b-1514ccafe9d8","section_id":"sec_1016b643-3fd0-4365-8d8c-6f235dd5ff01","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"```\n次のトークンを速く出す\nリアルタイム会話を滑らかにする\n音声AIの応答遅延を減らす\nエージェントの短い推論ループを速くする\n```","quote_start":0,"quote_end":69,"text_sha256":"4855781b36ce9f2ca036ee9220eaf3b4b2c3266cb63596437bd7c54e5757dd2c","block_sha256":"4855781b36ce9f2ca036ee9220eaf3b4b2c3266cb63596437bd7c54e5757dd2c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cc0d527c-d707-4cc4-900b-1514ccafe9d8"},{"id":"occ_caca03847e14e0622d6afcaa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ccf39eb1-93d6-40c7-a611-bda4302548b2","section_id":"sec_b25924f8-b6f7-407d-b7d1-b2c30ddb56b3","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"**入力 → LLM推論 → ツール呼び出し → 検索 → Python実行 → DB照会 → またLLM推論 → 判断 → 次のツール**","quote_start":0,"quote_end":70,"text_sha256":"3927d8e20ac1ff8676eff9f899f94657cf7161c8f61f15e14f1d4de039515a13","block_sha256":"3927d8e20ac1ff8676eff9f899f94657cf7161c8f61f15e14f1d4de039515a13","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ccf39eb1-93d6-40c7-a611-bda4302548b2"},{"id":"occ_fd5ac5efe43274d68f23d324","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cd6089e1-96e0-4834-bc0a-dca80b391f0c","section_id":"sec_652ee95e-1b6c-4ff4-9e88-79a69a291e7e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":38,"end":40,"exact":"推論","quote":"Google CloudではTPUをスライスやPodとして束ね、大規模学習や推論に使えます。またXLAコンパイラがMLフレームワークから出たグラフをTPU用機械語へコンパイルし、プログラムの残りはTPUホストマシン上で動きます。([Google Cloud Documentati","quote_start":0,"quote_end":140,"text_sha256":"fc5c7233eabb66f183fd566a1823c767c72ad3c5783336275129180adf102a3c","block_sha256":"fc5c7233eabb66f183fd566a1823c767c72ad3c5783336275129180adf102a3c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cd6089e1-96e0-4834-bc0a-dca80b391f0c"},{"id":"occ_3a04eecdcbdd34b12035bd06","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_cf5402f2-1d46-496e-962c-1bec2469c9a4","section_id":"sec_741197d7-6d5e-434d-93c5-573a40fa9156","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"LPUは、VLA全体の主役というより、**言語推論やエージェント的な短い思考ループを低遅延で回す補助エンジン** と見るのが自然です。","quote_start":0,"quote_end":67,"text_sha256":"5c403251a52be7233d2abceb5ed33c16a769b54f75c5f2123c98dc0669abf6cd","block_sha256":"5c403251a52be7233d2abceb5ed33c16a769b54f75c5f2123c98dc0669abf6cd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_cf5402f2-1d46-496e-962c-1bec2469c9a4"},{"id":"occ_7308c969211725fce9483808","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d009cd8f-d692-4a73-b2e9-f076c9ed027c","section_id":"sec_89358da1-fc0c-4957-a883-96ccfe3bfb3d","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":55,"end":63,"exact":"KV cache","quote":"```\nAPI受付\nユーザー認証\nセッション管理\nトークナイズ\nリクエストキュー\nバッチング\nGPUへの投入\nKV cache管理\nRAG検索\nDB照会\nツール呼び出し\nログ保存\n課金\nセキュリティ\n```","quote_start":0,"quote_end":103,"text_sha256":"cedfcc7f0d6033f3b04cb363017c18dd3bba1cf5b1f193c5417c0a2e04ac47f5","block_sha256":"cedfcc7f0d6033f3b04cb363017c18dd3bba1cf5b1f193c5417c0a2e04ac47f5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d009cd8f-d692-4a73-b2e9-f076c9ed027c"},{"id":"occ_01ee0614c772a811d36440be","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d18d75bf-bfec-46f7-b6d1-9e6e196c99de","section_id":"sec_05b8709c-00a4-4ff3-a0fd-502fd1080c73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"AI研究や高度な推論基盤では、標準レイヤーだけでは足りません。","quote_start":0,"quote_end":31,"text_sha256":"c281d11e37a12c36dc4ec1d804fa9f2db0f9b3b8e4592960f8762f3f8f09f67f","block_sha256":"c281d11e37a12c36dc4ec1d804fa9f2db0f9b3b8e4592960f8762f3f8f09f67f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d18d75bf-bfec-46f7-b6d1-9e6e196c99de"},{"id":"occ_13395528b82a924f2d41e4ed","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d1c727b8-5ce3-4ea0-bf92-7d25047356f9","section_id":"sec_5760d85c-4b90-434d-a30a-fe951b607da9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"TPUは、VLAにおいては主にクラウド側の大規模学習・大規模推論で強みを持ちます。","quote_start":0,"quote_end":41,"text_sha256":"9ff354e44036df7902b38bf8909e30cca6e6678c22e99297c6a54403b8320f72","block_sha256":"9ff354e44036df7902b38bf8909e30cca6e6678c22e99297c6a54403b8320f72","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d1c727b8-5ce3-4ea0-bf92-7d25047356f9"},{"id":"occ_d3b8b19d6d679d565ac0f564","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d6079af4-cf6f-4b01-870c-f52284bd26aa","section_id":"sec_4702fa77-c51d-4de1-a6b8-7ffad0dcbd89","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"という比較的規則的な推論パイプラインでは強いです。","quote_start":0,"quote_end":25,"text_sha256":"9ed2f27bb2eea96a75960a24e22618cbe1bbeb23b91c031b6fc0852aac18d196","block_sha256":"9ed2f27bb2eea96a75960a24e22618cbe1bbeb23b91c031b6fc0852aac18d196","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d6079af4-cf6f-4b01-870c-f52284bd26aa"},{"id":"occ_098ad64833d49180dbf8db70","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d6dc77a5-ecd2-4b1f-a8a9-7e69fef289ad","section_id":"sec_368badcc-9f77-4be6-9b74-b52df8d896c2","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":97,"end":99,"exact":"推論","quote":"Model\nMamba系\nマルチモーダルモデル\n動画生成モデル\nロボティクス基盤モデル\n世界モデル\n強化学習\n推論モデル\n```","quote_start":42,"quote_end":106,"text_sha256":"bf5dad784cdaa38dd1078fef5e3f4afe2165450f36cefb247640303fd18ec737","block_sha256":"bf5dad784cdaa38dd1078fef5e3f4afe2165450f36cefb247640303fd18ec737","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d6dc77a5-ecd2-4b1f-a8a9-7e69fef289ad"},{"id":"occ_0460f4256642a3046eda147b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d8eec3c0-6515-45d4-becd-4bc7b92eb0e6","section_id":"sec_a6b0787b-2203-488c-9016-cdea435706ea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"特に、**チャット、音声AI、リアルタイム翻訳、定型的なLLM推論、社内AIエージェント**では、この構成は強いです。","quote_start":0,"quote_end":59,"text_sha256":"ae88210d3268e6d90b5129a5a51312c3814e151086c29eb58c07d63b2f766dbf","block_sha256":"ae88210d3268e6d90b5129a5a51312c3814e151086c29eb58c07d63b2f766dbf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d8eec3c0-6515-45d4-becd-4bc7b92eb0e6"},{"id":"occ_bddddf6a8137ade77adae582","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_d984c86b-37ca-4c8d-b300-af6bf0535731","section_id":"sec_b25924f8-b6f7-407d-b7d1-b2c30ddb56b3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":148,"end":150,"exact":"推論","quote":"はCPU上のツール処理がE2Eレイテンシの大部分を占めると報告しています。さらに、高性能GPUを使うほどLLM推論が速くなるため、ボトルネックがCPU側へ移りやすいとも述べています。([arXiv](https://arxiv.org/html/2511.00739v3))","quote_start":93,"quote_end":230,"text_sha256":"aadae0e7a138e4e1303ff4fbe5a2523ec699c023673a87c9385bb9454b69c6c6","block_sha256":"aadae0e7a138e4e1303ff4fbe5a2523ec699c023673a87c9385bb9454b69c6c6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_d984c86b-37ca-4c8d-b300-af6bf0535731"},{"id":"occ_ee6358a7e4d4236b283be9e2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_db2e3b40-eddf-4203-9d06-9f564afba3ae","section_id":"sec_fb1996a9-7c62-4745-b530-69bb1b7b1c72","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":93,"end":95,"exact":"推論","quote":"g Unit** という用語です。  \nこれはGPUやTPUのような汎用AIアクセラレータではなく、**LLM推論、特に低遅延のトークン生成**に寄せた専用アクセラレータです。","quote_start":38,"quote_end":126,"text_sha256":"562f074a911117f130ece7480998999efebb363df85e5812a2b540e3c87ec64a","block_sha256":"562f074a911117f130ece7480998999efebb363df85e5812a2b540e3c87ec64a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_db2e3b40-eddf-4203-9d06-9f564afba3ae"},{"id":"occ_e2f99697f8b8a9b04e693179","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_db5a1dcb-663c-41cd-93dd-2d1e07b1bf81","section_id":"sec_69049162-737f-40ac-a3f5-3578216266a2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":140,"end":142,"exact":"推論","quote":"ecution: A CPU-Centric Perspective”** で提案された、**AIエージェント推論をCPU/GPU混在環境で効率よく動かすためのスケジューリング手法**です。  \n目的は共通していて、**CPU側のツール実行が詰まってGPUを遊ばせる問題を減らす**ことです。論文では、エージェント","quote_start":85,"quote_end":242,"text_sha256":"853f5c12959ea94dd090cf3c020d9c87dce81d4a84fcc1411ff9ebbdfc85ab61","block_sha256":"853f5c12959ea94dd090cf3c020d9c87dce81d4a84fcc1411ff9ebbdfc85ab61","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_db5a1dcb-663c-41cd-93dd-2d1e07b1bf81"},{"id":"occ_976532f1815e2674307057f3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_de98706b-53f7-4164-8c86-e8a503d1c3ee","section_id":"sec_d3f834b4-f2a6-461c-bdf9-d2b6f664ed81","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"・センサー\n↓\nCPU：入力管理、OS、ROS、I/O、安全制御\n↓\nGPU/TPU：視覚理解、VLM/VLA推論\n↓\nCPU：行動計画、制御、安全確認\n↓\nロボット制御器・モーター\n```","quote_start":7,"quote_end":102,"text_sha256":"a8085c19af83220162370d16e6d84c4ff7e14dcf64b9acdfe307a43abf78eb0f","block_sha256":"a8085c19af83220162370d16e6d84c4ff7e14dcf64b9acdfe307a43abf78eb0f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_de98706b-53f7-4164-8c86-e8a503d1c3ee"},{"id":"occ_21a6e887341a9874290a626b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_df5b86bc-3d3a-4138-af95-e46fdc0262b9","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":4,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":14,"end":21,"exact":"prefill","quote":"DistServeはさらに、prefillとdecodeを別GPUに分けることで干渉を減らす方式です。論文では、既存方式はprefillとdecodeを同じ場所で混在させるため干渉が起きるとし、DistServeはTTFTとTPOTの要件に応","quote_start":0,"quote_end":121,"text_sha256":"c7e5b0cb6b24fb8ebcc1d186733c709a9fb6d4259989851e82016431a486fd71","block_sha256":"c7e5b0cb6b24fb8ebcc1d186733c709a9fb6d4259989851e82016431a486fd71","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_df5b86bc-3d3a-4138-af95-e46fdc0262b9"},{"id":"occ_733c0f35fd9f52c483205b62","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_df603698-2e09-4544-9a26-090b4aaccee5","section_id":"sec_c4d6d78c-8171-4c06-9259-80a17c5190ec","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":42,"end":44,"exact":"推論","quote":"```\n長文入力\nRAG\n検索\nツール呼び出し\nマルチモーダル\nMoEルーティング\n推論ステップの変化\n動的バッチング\nKV cache管理\n```","quote_start":0,"quote_end":74,"text_sha256":"5eed7c94431066a201fb65a90874bff38fa8e4fcb167dd01db574c577bb97107","block_sha256":"5eed7c94431066a201fb65a90874bff38fa8e4fcb167dd01db574c577bb97107","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_df603698-2e09-4544-9a26-090b4aaccee5"},{"id":"occ_1d46f060474719dedb8719e6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_df679d1a-d939-4f12-b70e-db8cdd287de2","section_id":"sec_e4043154-47b4-46ab-aebe-9462b2a82359","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"つまり、AI推論は「GPUで行列演算するだけ」ではなく、**巨大な動的メモリ管理システム**になっています。","quote_start":0,"quote_end":54,"text_sha256":"acd3c0fb399b1170fae65b91e73598f3a85510f9c6b9c1295ae324451eebaa27","block_sha256":"acd3c0fb399b1170fae65b91e73598f3a85510f9c6b9c1295ae324451eebaa27","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_df679d1a-d939-4f12-b70e-db8cdd287de2"},{"id":"occ_bcc71c8dc22c36ffe41f6f6c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e02e0e3e-aacb-4da5-9367-3226f7c28512","section_id":"sec_37862236-9acb-43b9-a9e5-b7a32d79825b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"### \\1. 学習にも推論にも使える","quote_start":0,"quote_end":19,"text_sha256":"f66b564c2ea9c3ad1e0e29eee3784852175f64fdeaac8147094910488649a26c","block_sha256":"f66b564c2ea9c3ad1e0e29eee3784852175f64fdeaac8147094910488649a26c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e02e0e3e-aacb-4da5-9367-3226f7c28512"},{"id":"occ_87173ae37cc07046f0f7c4e6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e0c4dd9c-7952-4ee7-9b02-400f2d4eee3d","section_id":"sec_f5fb003a-62fe-48c2-a63d-107ee26859cb","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":3,"end":11,"exact":"KV cache","quote":"## KV cacheとメモリ管理が、CPU需要を押し上げる","quote_start":0,"quote_end":30,"text_sha256":"4492704a869e0aa5ac16f009c9a818768501899f51e4d36d09923513a53eaf10","block_sha256":"4492704a869e0aa5ac16f009c9a818768501899f51e4d36d09923513a53eaf10","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e0c4dd9c-7952-4ee7-9b02-400f2d4eee3d"},{"id":"occ_d86c2f64b365d8ce670d607c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e190e0a2-498d-4018-b739-a1ddcb277258","section_id":"sec_50759b4f-7e29-4ba7-b376-9aafe0ef3db0","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"```\n画像を見る\n↓\n言語命令を理解する\n↓\n状況を推論する\n↓\n何を掴むか、どこへ動かすかを決める\n↓\nロボットの行動に変換する\n```","quote_start":0,"quote_end":70,"text_sha256":"6124528c52752ca79bc875bc9f6b4063182dd2c2e313e897168fa5b6beaeddcb","block_sha256":"6124528c52752ca79bc875bc9f6b4063182dd2c2e313e897168fa5b6beaeddcb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e190e0a2-498d-4018-b739-a1ddcb277258"},{"id":"occ_602e876217de1e0888fc38d7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e1c84e1b-3b47-432a-ad19-6b608e8117c3","section_id":"sec_2b70b9dc-f533-4aae-9151-6d5d17fac4ba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":96,"end":98,"exact":"推論","quote":"処理は、GPUなしでもCPUで回せます。  \n特にオンプレ、エッジ、企業内サーバーでは、GPUを載せないCPU推論も現実的です。","quote_start":41,"quote_end":105,"text_sha256":"71aec6b0ae84e987f504a813adb7c4d32f70a42dad87d32027056e72d37702ed","block_sha256":"71aec6b0ae84e987f504a813adb7c4d32f70a42dad87d32027056e72d37702ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e1c84e1b-3b47-432a-ad19-6b608e8117c3"},{"id":"occ_1f8cd24367ef61d4ecccb780","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e2d2234e-2bd4-459c-874f-7d8d8c12e757","section_id":"sec_f5fb003a-62fe-48c2-a63d-107ee26859cb","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":29,"end":37,"exact":"KV cache","quote":"vLLMの **PagedAttention** 論文は、KV cacheが巨大かつ動的に増減するため、非効率な管理ではメモリ断片化や重複が起き、バッチサイズを制限してしまうと指摘している。PagedAttentionはOSの仮想メモリやページングに着想を得てKV cach","quote_start":0,"quote_end":137,"text_sha256":"4f3504329ff30fe1b5e7340e35fc2074cbfebb25dfa5e0fae85c38c01f91f0e9","block_sha256":"4f3504329ff30fe1b5e7340e35fc2074cbfebb25dfa5e0fae85c38c01f91f0e9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e2d2234e-2bd4-459c-874f-7d8d8c12e757"},{"id":"occ_b527d1fd27ba8efac37ed53d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e31deb74-f401-48b4-b93c-fc9ca68fc633","section_id":"sec_ac9ca8d9-aa0d-48ac-a5c1-8f63e31c299d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":16,"end":18,"exact":"推論","quote":"この領域では、LPUのような専用推論チップが非常に強くなります。","quote_start":0,"quote_end":32,"text_sha256":"d0118c83eca23730972d80529550744e0b108ec1675b55a8f7d904bcadd93a1b","block_sha256":"d0118c83eca23730972d80529550744e0b108ec1675b55a8f7d904bcadd93a1b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e31deb74-f401-48b4-b93c-fc9ca68fc633"},{"id":"occ_e98bd36d8c8c43a325185184","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_e6bb60ba-48eb-417d-9c40-e4e59d5be17a","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":2,"end":8,"exact":"decode","quote":"**decode** は、1トークンずつ続きを生成する段階です。  \nこれは逐次性が強く、GPU利用率が低くなりやすい。","quote_start":0,"quote_end":60,"text_sha256":"2ab9142ba1c52e0bbfb4536a23eff7bbeac0cdf205534791f4e78bd856246a43","block_sha256":"2ab9142ba1c52e0bbfb4536a23eff7bbeac0cdf205534791f4e78bd856246a43","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_e6bb60ba-48eb-417d-9c40-e4e59d5be17a"},{"id":"occ_937adef80e6fe4174e7bcf90","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ea2ae8a0-8804-49d8-b58e-c7b83f7dca36","section_id":"sec_a6b0787b-2203-488c-9016-cdea435706ea","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"```\nCPU：次に何をすべきか決める\n↓\nTPU/LPU：LLM推論を高速に実行\n↓\nCPU：結果を見て次のツールを呼ぶ\n↓\nTPU/LPU：また推論する\n```","quote_start":0,"quote_end":82,"text_sha256":"7a1832dfefcd989ab13d283c8547469fe03cb8a8f9d62a569dc3acf17b247479","block_sha256":"7a1832dfefcd989ab13d283c8547469fe03cb8a8f9d62a569dc3acf17b247479","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ea2ae8a0-8804-49d8-b58e-c7b83f7dca36"},{"id":"occ_6ee4f6abe1f551d836dc9a65","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ea4d964a-3eb3-4ea9-9925-bcdfad62303b","section_id":"sec_946dedd1-8094-428b-99e9-a309a87cc3fd","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":26,"end":34,"exact":"KV cache","quote":"CPUは、リクエスト受付、トークナイズ、バッチング、KV cache管理、ネットワーク制御などを担当します。","quote_start":0,"quote_end":54,"text_sha256":"9c7517254f147b90ce78c01db9b490844b8e9f90b15eef3bb3c6bbc2335a7a14","block_sha256":"9c7517254f147b90ce78c01db9b490844b8e9f90b15eef3bb3c6bbc2335a7a14","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ea4d964a-3eb3-4ea9-9925-bcdfad62303b"},{"id":"occ_f308e7a048f64dfd925307f0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ec3021f8-6544-4e4e-ab57-ba47874df126","section_id":"sec_3aad843a-f437-4379-87a0-f87bf411ef73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"## \\4. 動的shape・不規則なバッチ・長さがバラバラの推論","quote_start":0,"quote_end":33,"text_sha256":"22de0d936f5ab5234f3ca331fc91b16754f9e75bce6dd7339cb9796fec830286","block_sha256":"22de0d936f5ab5234f3ca331fc91b16754f9e75bce6dd7339cb9796fec830286","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ec3021f8-6544-4e4e-ab57-ba47874df126"},{"id":"occ_ccf9bc92fbf095f44ab96f23","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_f4705d2f-0835-4c0b-8c9d-1150d7c5aa0c","section_id":"sec_8a97483f-1163-43f1-a68a-c680a2c1bddf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"はい。  \nこの時代、つまり **AIエージェント化・推論爆増・リアルタイムAI化** の時代では、GPU、TPU、LPUの優位性はかなり分かれてきます。","quote_start":0,"quote_end":77,"text_sha256":"3438ecdf54a8d9cd956874bf5ba6de92a04926e6d1d1864547716e87bab8cc30","block_sha256":"3438ecdf54a8d9cd956874bf5ba6de92a04926e6d1d1864547716e87bab8cc30","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_f4705d2f-0835-4c0b-8c9d-1150d7c5aa0c"},{"id":"occ_09e70419d5e5599581be6765","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_f7ab7672-34fe-4325-b60a-1f2b47cfb37f","section_id":"sec_6bcc2a43-7390-4518-87df-b6181c33d220","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"TPUは、形が安定した大規模テンソル計算では強い。  \nLPUは、低遅延の言語推論では強い。  \nしかし、フィジカルAIはまだ研究開発段階で、モデル構造もデータ形式も固まっていません。","quote_start":0,"quote_end":92,"text_sha256":"81cfd7aa64a661f47a11757086fea53c9c811ff292a8d1184c3473eb837ab91e","block_sha256":"81cfd7aa64a661f47a11757086fea53c9c811ff292a8d1184c3473eb837ab91e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_f7ab7672-34fe-4325-b60a-1f2b47cfb37f"},{"id":"occ_c2f06163b3d9797d8f05db84","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_f8bac625-3a96-42ff-9bcb-bd29df979186","section_id":"sec_23176590-b1e6-481b-a89e-03ca6370a6be","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":63,"end":70,"exact":"prefill","quote":"文が **Sarathi-Serve** と **DistServe** です。Sarathi-Serveは、prefillは高レイテンシだがGPU計算を飽和させ、decodeは低レイテンシだが1トークンずつなのでGPU利用率が低いと整理し、chunked-prefillとstall-free schedulingでスルー","quote_start":8,"quote_end":170,"text_sha256":"7b6e721df9877abd177a330359f9d8bc58e00825030c298b760155cd6891df40","block_sha256":"7b6e721df9877abd177a330359f9d8bc58e00825030c298b760155cd6891df40","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_f8bac625-3a96-42ff-9bcb-bd29df979186"},{"id":"occ_23ab01e477ce949d16d9c6bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_f95bacf6-0e67-4548-85eb-1fee466c661b","section_id":"sec_adf52b26-6132-43c0-8c99-5e891c018baf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":132,"end":134,"exact":"推論","quote":"を1つの巨大GPUのように扱う設計です。NVIDIAは、GB200 NVL72がH100比でリアルタイムLLM推論30倍、LLM学習4倍、エネルギー効率25倍を掲げています。([NVIDIA](https://www.nvidia.com/en-us/data-center/gb200-nvl72/))","quote_start":77,"quote_end":229,"text_sha256":"de14bf68cdbd228d11d93f2c21f3bce63d90e2cb88aaec4303e415e2a552a426","block_sha256":"de14bf68cdbd228d11d93f2c21f3bce63d90e2cb88aaec4303e415e2a552a426","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_f95bacf6-0e67-4548-85eb-1fee466c661b"},{"id":"occ_70d817126ebf8a8717023d53","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_fb6431a8-4337-4bd1-b589-5c0581d5a15d","section_id":"sec_eeb195b5-bdb9-4c3c-84e5-7e4ec367e5ac","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"```\n人間との会話\n指示理解\n短い計画\nエージェント的な再推論\n```","quote_start":0,"quote_end":36,"text_sha256":"1c578bb9ea216a8651c421af7d004d7886ca7f98ceab44e866eb4df6f47c87a9","block_sha256":"1c578bb9ea216a8651c421af7d004d7886ca7f98ceab44e866eb4df6f47c87a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_fb6431a8-4337-4bd1-b589-5c0581d5a15d"},{"id":"occ_169bc00e83bedaa2c6423477","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_fb9ede3d-0ff5-4196-bca6-f8edfb05a2ff","section_id":"sec_e9704f13-a11a-4040-9f60-a6a26f2b4a77","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"特にロボットでは、AIモデルの推論だけでなく、**リアルタイム制御** が必要です。","quote_start":0,"quote_end":42,"text_sha256":"a1df873e634d71e4fb67c5b6c939154dc2204026af3142fe74e8d7a0419f743d","block_sha256":"a1df873e634d71e4fb67c5b6c939154dc2204026af3142fe74e8d7a0419f743d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_fb9ede3d-0ff5-4196-bca6-f8edfb05a2ff"},{"id":"occ_dbf8241d71e86f9c4f794df4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_fbf51f44-3774-427a-829f-cabff1b699b4","section_id":"sec_3895746e-8caa-4677-a4b1-13824b60e07d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":125,"end":127,"exact":"推論","quote":"レーション、データ処理、システムレベル性能で重要だと説明しています。Google CloudはXeonをAI、推論、汎用コンピューティングで使い続け、IPUも併用してネットワーク、ストレージ、セキュリティ処理をCPUから一部オフロードするとしています。([Intel Corporation](https://ww","quote_start":70,"quote_end":227,"text_sha256":"0b1eb9e5eb3bb0bc71161d344cd03875321d40e26104f845379ec1f22a71eb32","block_sha256":"0b1eb9e5eb3bb0bc71161d344cd03875321d40e26104f845379ec1f22a71eb32","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_fbf51f44-3774-427a-829f-cabff1b699b4"},{"id":"occ_37f3691bff0efe8078cb0849","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_fc2ceaa5-c000-4294-9de3-8d9f3f74cf7a","section_id":"sec_ade72eea-582d-4e81-8d35-284b83454113","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"ここで分かる重要点は、**AI推論はGPUだけでは完結しない**ということです。","quote_start":0,"quote_end":40,"text_sha256":"fc9942aa9ba62d482e1a33955556cdd7a09158ebeb2b860382c3a564d77d7d06","block_sha256":"fc9942aa9ba62d482e1a33955556cdd7a09158ebeb2b860382c3a564d77d7d06","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_fc2ceaa5-c000-4294-9de3-8d9f3f74cf7a"},{"id":"occ_c5c6f05113409257f1ceb884","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb","work_id":"wrk_dea71d50-dc48-48c0-a352-3461dbce4132","block_id":"blk_ff9e8dc0-4be5-4fb3-9de6-f839924c6815","section_id":"sec_e4043154-47b4-46ab-aebe-9462b2a82359","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論では、生成中の各リクエストが **KV cache** を持ちます。これは長文・多数リクエスト・複数候補生成で巨大化します。","quote_start":0,"quote_end":67,"text_sha256":"7e78a1bbbad83253f3e4ba7d9be136e6ae0ada8437ed841646aabb2a1eca32ee","block_sha256":"7e78a1bbbad83253f3e4ba7d9be136e6ae0ada8437ed841646aabb2a1eca32ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2cf94359-bc71-4b68-8fd3-9c93c45ac3eb/#blk_ff9e8dc0-4be5-4fb3-9de6-f839924c6815"},{"id":"occ_c8cfd61c9aa334069335196e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_04f944df-6a82-4322-93fe-73d569fa4614","section_id":"sec_3bb49a6e-9085-49ed-b067-768c9cbb5991","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":106,"end":108,"exact":"推論","quote":"計算と環境実行を切り離す設計は実際に存在する。例えばNVIDIAのNeMo GymはCPU側で動作し、モデルの推論エンジンを通信経由で呼び出す構成になっている。([NVIDIA Docs](https://docs.nvidia.com/nemo/rl/latest/design-docs/nemo-gym-in","quote_start":51,"quote_end":208,"text_sha256":"0ab5edbfeb30dbe5ae014944ff94bd4a02cfe47947273081f941b2f341ea2f72","block_sha256":"0ab5edbfeb30dbe5ae014944ff94bd4a02cfe47947273081f941b2f341ea2f72","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_04f944df-6a82-4322-93fe-73d569fa4614"},{"id":"occ_27704d40a21a63f366624eec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_3b32110a-9474-4a3f-98e7-6d3f18153384","section_id":"sec_3ff23fe3-ec39-4f5d-8b9e-eba0c50453d3","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"また、ASICを一括りにもできない。学習対応の装置と、推論だけを担当する装置では役割が違う。推論専用の設備に重み更新まで任せることはできないが、試行生成や教師モデルの計算へ使う余地はある。","quote_start":0,"quote_end":94,"text_sha256":"b958443674db0e81067f0114cbb2cbdcf67f4be1f76b96f3ac683c6a78f6cd4a","block_sha256":"b958443674db0e81067f0114cbb2cbdcf67f4be1f76b96f3ac683c6a78f6cd4a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_3b32110a-9474-4a3f-98e7-6d3f18153384"},{"id":"occ_7ee0849ea3cfaf977bbcce1c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_53de1803-3e09-4d7b-af2e-b684273b40bb","section_id":"sec_eae0575a-ed35-40fc-8a36-9b597d6c4141","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"例から考えられる発展の方向であり、完全自律の再帰的改善や、際限のない加速が実証されたという意味ではない。また、推論向けのJalapeñoが、MiMoの学習更新を含む全工程に対応すると示されたわけでもない。","quote_start":6,"quote_end":108,"text_sha256":"688bf7f183f484863e23321e11fc911a1e9cc4f1ccb0837465979b65908317f0","block_sha256":"688bf7f183f484863e23321e11fc911a1e9cc4f1ccb0837465979b65908317f0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_53de1803-3e09-4d7b-af2e-b684273b40bb"},{"id":"occ_eae0f4ccf6d930450167d9a7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_5432bafe-aa16-4507-903a-1d07fec4f09c","section_id":"sec_14c1b25e-12ef-4f4d-b85f-c432e8bf883b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"## 13．強化学習は「学習のための推論」を大量に必要とする","quote_start":0,"quote_end":30,"text_sha256":"50ac46368c6f86b91dec860a202decf6eb9bb35fc2efafbcf90dabfe88c2ef9e","block_sha256":"50ac46368c6f86b91dec860a202decf6eb9bb35fc2efafbcf90dabfe88c2ef9e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_5432bafe-aa16-4507-903a-1d07fec4f09c"},{"id":"occ_038510e124fce68ab260d6bd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_568cc13e-1983-494d-b2ae-6011f3a6654a","section_id":"sec_7787a994-dd1b-472f-be26-6a4f9cf28fd3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":64,"end":66,"exact":"推論","quote":"、NeMo Gymのような公開実装に見られる。同実装では、環境側はモデル内部へ直接アクセスせず、HTTP経由で推論を呼び出す。ただし、公開されているこの構成はvLLMとの接続を前提としており、TPUへ接続するなら対応する実装と検証が別途必要になる。([NVIDIA Docs](https://docs.nvidi","quote_start":9,"quote_end":166,"text_sha256":"978e44c3530612ded718066a926b89297c7a2320148341cc75fe501f1e107ac3","block_sha256":"978e44c3530612ded718066a926b89297c7a2320148341cc75fe501f1e107ac3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_568cc13e-1983-494d-b2ae-6011f3a6654a"},{"id":"occ_d01044740466f4ec7b4fa4eb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_5fbc7dd7-99fe-443a-bf9e-8712c5a42c75","section_id":"sec_9401262a-c341-40a5-bafd-fc9d57de8ddd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":76,"end":78,"exact":"推論","quote":"で、事前学習、強化学習、アラインメントの研究を組み合わせたと説明している。システムカードにも、強化学習を通じて推論方法を改善し、異なる戦略を試し、誤りを認識することを学ぶという記述がある。**AstraでRLが使われていることは、公式に確認できる。**([OpenAI](https://openai.com/in","quote_start":21,"quote_end":178,"text_sha256":"d06d400e9e141ad04c62e5939a33277099a1749e5d6213e362b13bed3b2f6851","block_sha256":"d06d400e9e141ad04c62e5939a33277099a1749e5d6213e362b13bed3b2f6851","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_5fbc7dd7-99fe-443a-bf9e-8712c5a42c75"},{"id":"occ_7b9f4829052285c317886c2e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_73a3b442-c6f3-4d89-868a-43a42cc73c6c","section_id":"sec_37907dff-d62b-4d8d-8b5f-7ebdda64c4de","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"ここからは配置に関する推論だが、教師の確率計算や一部の採点をTPU・ASICへ切り出し、生徒の実験や更新をGPU側へ置く構成も候補になる。反対に、全体をTPUへ統一した方が、通信と運用を単純化できる場合も考えられる。","quote_start":0,"quote_end":108,"text_sha256":"d24377bf1a6d80a1021d9f02f3f02e9468af3f4934d7bec6bc79785f092de740","block_sha256":"d24377bf1a6d80a1021d9f02f3f02e9468af3f4934d7bec6bc79785f092de740","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_73a3b442-c6f3-4d89-868a-43a42cc73c6c"},{"id":"occ_805f66ccc2a1d88317ef4ec5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_839e198c-7a6e-4c51-8b5e-ee4eb2d4657b","section_id":"sec_7b3696db-c5d0-4756-868c-257e858a1f33","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":69,"end":71,"exact":"推論","quote":"、別の分野では不利になる場合がある。NVIDIAのNemotron-Cascade 2の研究でも、学習によって推論が短くなった結果、数学の性能に悪影響が出たり、人間の好みに合わせる学習と厳密な指示追従の間にトレードオフが生じたりすると報告されている。([arXiv](https://arxiv.org/html/","quote_start":14,"quote_end":171,"text_sha256":"70ca84fa53d83402019ac5cf0320fb15ff9eed1c49bfd54db01da2a62a676963","block_sha256":"70ca84fa53d83402019ac5cf0320fb15ff9eed1c49bfd54db01da2a62a676963","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_839e198c-7a6e-4c51-8b5e-ee4eb2d4657b"},{"id":"occ_347447f14e6618ba7057cf41","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_84c0d504-7037-49cf-8ea9-800f96afafcc","section_id":"sec_105030b4-12e3-41a1-8434-e3f44936e642","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":70,"end":72,"exact":"推論","quote":"を基礎から整理し、NVIDIAやOpenAIの公開情報との関係を確認する。そのうえで、こうした学習がなぜ大量の推論を必要とし、H100などの旧世代GPUにも仕事を残し得るのかを見ていく。","quote_start":15,"quote_end":108,"text_sha256":"4cb6082991179d2c4b15dc365b0a958dd67feaf8d7d300c04f6f59a9e015a665","block_sha256":"4cb6082991179d2c4b15dc365b0a958dd67feaf8d7d300c04f6f59a9e015a665","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_84c0d504-7037-49cf-8ea9-800f96afafcc"},{"id":"occ_8b7cb9cc01affc2bd11bc2f5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_84d359f9-ca3a-45be-8234-4bafda3bb0fe","section_id":"sec_84b87706-ec8b-4f23-ad50-893c4d0d74f4","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"2026年6月に発表されたJalapeñoは、LLMの推論を対象とする独自アクセラレータである。OpenAIがモデル、計算処理、推論サービスの要求を踏まえて設計し、Broadcomがシリコン実装やネットワーク、Celesticaがボード・ラックなどのシステム","quote_start":0,"quote_end":129,"text_sha256":"6dbcd1085fd4f76aca2d4b9fa68d86223ef5bfc6780e6890b92098f151dd23d0","block_sha256":"6dbcd1085fd4f76aca2d4b9fa68d86223ef5bfc6780e6890b92098f151dd23d0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_84d359f9-ca3a-45be-8234-4bafda3bb0fe"},{"id":"occ_c2600864e3da3d6268442b22","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_d0ab4382-38ee-4aaf-b0c8-86755c70a4bf","section_id":"sec_4c5470ed-d624-401f-90dd-f8e2a98841c2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"複数のモデルを毎回すべて動かすと、推論の費用や運用が重くなる。そこで、教師側の振る舞いを一つのモデルへ移し、使いやすくする。この考え方自体は古く、複数モデルや専門モデルの知識を一つへ移す研究も以前から存在する。([arXiv](https","quote_start":0,"quote_end":119,"text_sha256":"cac89c38e4778c3ff9516f2e29d9dee0ab663077eb7397790c9ba6a0bbcf0f47","block_sha256":"cac89c38e4778c3ff9516f2e29d9dee0ab663077eb7397790c9ba6a0bbcf0f47","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_d0ab4382-38ee-4aaf-b0c8-86755c70a4bf"},{"id":"occ_14e1e715c029262e441e6449","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc82-481f-b5ce-3dbca5dfe331","block_id":"blk_e94bfa27-a98f-486f-b2e2-d069a4c29480","section_id":"sec_0a2c3bea-3bf9-487e-a65e-7836e979d7c3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":63,"end":65,"exact":"推論","quote":"が進んでも、すべての仕事が一度に移るとは限らない。OpenAI自身も、Jalapeñoの展開と並行して、学習・推論の双方でNVIDIAなどのアクセラレータを引き続き広く導入すると説明している。ただし、これはH100など特定の旧世代を使い続けると約束したものではない。([OpenAI](https://openai","quote_start":8,"quote_end":165,"text_sha256":"483f6b966e9706af2a1a12548efec4648f61605037c5772b9c2025222fbaa55c","block_sha256":"483f6b966e9706af2a1a12548efec4648f61605037c5772b9c2025222fbaa55c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2e5b712c-b120-4b45-91dd-33e6758666be/#blk_e94bfa27-a98f-486f-b2e2-d069a4c29480"},{"id":"occ_5d6d4ba31219276bb7667465","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2e5b712c-b120-4b45-91dd-33e6758666be","work_id":"wrk_c9d1ffd9-bc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|\n| NVIDIA | AI計算プラットフォーム | GPU、CUDA、Aerial、AI推論基盤 |\n| Nokia | RANと接続インフラ | AirScale、anyRAN、RANソフト、光・IPバックホール |\n| Ericsson | RANとCloud RAN | 無線機、Clo","quote_start":72,"quote_end":229,"text_sha256":"c6e1599f3ab6438e9b9685cf83be72934a331d86f34bebd4193c638aba700581","block_sha256":"c6e1599f3ab6438e9b9685cf83be72934a331d86f34bebd4193c638aba700581","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2ed1383f-5c73-49bd-bd5b-f19deda1814c/#blk_544fd5cd-ef28-4b52-ba2c-8e6ad61e5e0f"},{"id":"occ_697045a466ac9406fc7f5564","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2ed1383f-5c73-49bd-bd5b-f19deda1814c","work_id":"wrk_209cb693-e035-4d91-b959-d2a9b4df1de3","block_id":"blk_6eb6ce61-e768-4368-aabf-9416eb61ea61","section_id":"sec_1957e7cf-7a43-4d02-a44e-b46794b3f517","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":79,"end":81,"exact":"推論","quote":"IDIA基盤上でのCloud RAN動作\n- 基地局サイトでのPhysical AI実証\n- 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AI推論\n- 開発者エコシステム","quote_start":0,"quote_end":43,"text_sha256":"e8a40cb4ce1bbf35d939941b8e14e02225452807b40aa1609d7abf7fdd3b2b6c","block_sha256":"e8a40cb4ce1bbf35d939941b8e14e02225452807b40aa1609d7abf7fdd3b2b6c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2ed1383f-5c73-49bd-bd5b-f19deda1814c/#blk_e12667e3-2f42-420a-87db-eb46d06871c5"},{"id":"occ_9f7afe252cfd71f92735bac3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2ed1383f-5c73-49bd-bd5b-f19deda1814c","work_id":"wrk_209cb693-e035-4d91-b959-d2a9b4df1de3","block_id":"blk_e6c55f1e-e71c-4f30-af71-cd34c182ccb6","section_id":"sec_3739f594-cda9-4031-a119-be3fe113ffa7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":59,"end":61,"exact":"推論","quote":"用ASICやDSPで処理していた基地局の物理層処理を、NVIDIAのアクセラレータ上へ移し、同じ計算基盤でAI推論も動かすことを狙う。","quote_start":4,"quote_end":71,"text_sha256":"36c68ddbd7a4e0059a6df603a863df5cb0fd6e07a56eb1346a288870307acbe6","block_sha256":"36c68ddbd7a4e0059a6df603a863df5cb0fd6e07a56eb1346a288870307acbe6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2ed1383f-5c73-49bd-bd5b-f19deda1814c/#blk_e6c55f1e-e71c-4f30-af71-cd34c182ccb6"},{"id":"occ_0f2d4a96416afa14fb07c6ce","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_129b843f-417f-4356-8622-43c9e53105da","section_id":"sec_94c2e0da-daaa-44ae-b69d-55604cf8a061","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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cache、ログ、チェックポイント保存などによって、NAND需要は急速に高付加価値化している。TrendForceは、2026年Q1の上位5社NAND売上が前四半期比83","quote_start":0,"quote_end":154,"text_sha256":"ce8f8b581549b0823101447d3b8cc4313dbc6beb89447479726c9dae2f9111b0","block_sha256":"ce8f8b581549b0823101447d3b8cc4313dbc6beb89447479726c9dae2f9111b0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_129b843f-417f-4356-8622-43c9e53105da"},{"id":"occ_0b1aa486a419fea8ff3840bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_179f8aa1-66b3-4822-81e3-99d1642247c6","section_id":"sec_96d3a42e-80ef-41cd-977e-4839338a9c88","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":86,"end":94,"exact":"KV cache","quote":"は演算直結の超低レイテンシDRAMである。\nHBF/XL-FLASHは、モデル重み、巨大ベクトルDB、RAG、KV cache、頻繁な小ブロック読み出しをGPU近傍に置くための「HBMの外側の高速大容量階層」である。","quote_start":31,"quote_end":139,"text_sha256":"bc17269a71c2241c8ee7592e7ced285402c5115e30e5a78626986559ba9c094f","block_sha256":"bc17269a71c2241c8ee7592e7ced285402c5115e30e5a78626986559ba9c094f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_179f8aa1-66b3-4822-81e3-99d1642247c6"},{"id":"occ_2a9cd5c777a8700896ee706a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_2e7fbf9e-2ca1-43ad-84a1-a3972fd3fa57","section_id":"sec_03db6bf6-163c-44f0-8586-d21f0bcc4bfc","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":288,"end":290,"exact":"推論","quote":"     |\n| 最大の利益源 | HBMとサーバーDRAM                     | AI推論ストレージと高付加価値NAND             |\n| 上振れ規模  | Samsung/SKは4〜5兆ドル候補              | Kioxiaは1兆ドル候補、SanDiskはやや","quote_start":233,"quote_end":390,"text_sha256":"b2265f8c4f992c549492c3435f69f30ef5902244ecbe289e427549a8b4320351","block_sha256":"b2265f8c4f992c549492c3435f69f30ef5902244ecbe289e427549a8b4320351","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_2e7fbf9e-2ca1-43ad-84a1-a3972fd3fa57"},{"id":"occ_7cbb7847687b0eb8a15c915d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_36dccc45-3c9a-4a5c-b672-2fe60fa7a34e","section_id":"sec_33726e59-03d1-4979-a87c-214084c91aa3","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":102,"end":104,"exact":"推論","quote":"\nDDR5 / MRDIMM / SOCAMM\n  ↓ 近傍ストレージ\nTLC / QLC eSSD\n  ↓ 推論高速化・巨大重み階層\nHBF / XL-FLASH\n  ↓ 制御・起動・産業機器\nNOR / SLC NAND\n```","quote_start":47,"quote_end":163,"text_sha256":"0378c9f01451ed40ee3b6224f543cf2f4883cae13158a55f62de0dce07243972","block_sha256":"0378c9f01451ed40ee3b6224f543cf2f4883cae13158a55f62de0dce07243972","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_36dccc45-3c9a-4a5c-b672-2fe60fa7a34e"},{"id":"occ_165c8e4f7298f52e76707554","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_3810a41c-d571-4bda-9ba3-2b18b70a1c54","section_id":"sec_96d3a42e-80ef-41cd-977e-4839338a9c88","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"SanDiskはHBFについて、2026年後半にHBFメモリの初期サンプル、2027年初めにHBF搭載AI推論デバイスの初期サンプルを目指している。Kioxia側では、NVIDIAと組んだ100 million IOPS級AI SSD構想が報じられており、XL-FLASHやHBF的な構造が候補として論じられて","quote_start":0,"quote_end":155,"text_sha256":"96ef34cf0e47d07bf455ba85602dd0c88a8ee43f564fdc3f41881070d940f44b","block_sha256":"96ef34cf0e47d07bf455ba85602dd0c88a8ee43f564fdc3f41881070d940f44b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_3810a41c-d571-4bda-9ba3-2b18b70a1c54"},{"id":"occ_2c22b6ef01c4f54d2f372c1b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_76859e04-c2c6-4772-bc9d-22b18838c38a","section_id":"sec_f598d189-aaf4-4aa7-a69f-d746c30db454","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":767,"end":769,"exact":"推論","quote":"量産 |  標準化なら拡大 | 容量ではなくIOPS単価  |\n| HBF              | AI推論用高速Flash階層     |  QLC比10〜30倍 |  サンプル段階 | 採用次第で急拡大 | HBM補完層        |","quote_start":712,"quote_end":836,"text_sha256":"481d80ea71f5b2210b8e324341d29a10c9ae77bb7ccc6b6ba8bd20813d55f706","block_sha256":"481d80ea71f5b2210b8e324341d29a10c9ae77bb7ccc6b6ba8bd20813d55f706","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_76859e04-c2c6-4772-bc9d-22b18838c38a"},{"id":"occ_f2d260d9ae4dfa7ea020eb12","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_806331a4-f260-4805-a8b5-594449d89786","section_id":"sec_baaebc8a-96e3-4d42-9d6d-d8ae8c54dd9c","layer":"character","character_id":"zetu_noia","count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":36,"end":44,"exact":"KV cache","quote":"GPUは目立ちます。けれど、GPUが取りに行く重み、アクティベーション、KV cache、RAGの文書、推論ログ、チェックポイントは、どこかに置かれなければいけません。置く場所がHBMであり、DDRであり、eSSDであり、HBFであり、NANDです。","quote_start":0,"quote_end":125,"text_sha256":"9baf56a0cca294ebd1aece004eda15d936d06424a5a605d50c9fd7247962f262","block_sha256":"9baf56a0cca294ebd1aece004eda15d936d06424a5a605d50c9fd7247962f262","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_806331a4-f260-4805-a8b5-594449d89786"},{"id":"occ_ebe48718b632b85789bd0847","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_80b09228-dc3a-4dae-a1ee-6a9f77413c25","section_id":"sec_6ae53732-bb0a-44c5-a5bf-e666ca4c6370","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":214,"end":216,"exact":"推論","quote":"\n- KioxiaとSanDiskの1兆ドルシナリオは、通常NANDだけでなく、HBF/XL-FLASHがAI推論階層として成立するかに左右される。\n- 低PERは必ずしも割安を意味しない。ピーク利益への疑いとして低く見えている可能性がある。\n- 2028年以降もHBM、DDR5、MRDIMM、SOCAMM、eS","quote_start":159,"quote_end":316,"text_sha256":"f8215c4aef76825cb6d537d150d7f2d1cf4cfd3e62cfccdc9ad3bc0225f89c6d","block_sha256":"f8215c4aef76825cb6d537d150d7f2d1cf4cfd3e62cfccdc9ad3bc0225f89c6d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_80b09228-dc3a-4dae-a1ee-6a9f77413c25"},{"id":"occ_27b5c4d67a2ae3313198b991","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_84ea7bab-e86f-4df7-8737-d410477712ca","section_id":"sec_f598d189-aaf4-4aa7-a69f-d746c30db454","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":166,"end":168,"exact":"推論","quote":"帯域、GPU近傍配置、消費電力削減によって価格が決まる。そのため、一ビット単価は通常NANDより高くても、AI推論システム全体の費用対効果が合えば採用される。","quote_start":111,"quote_end":190,"text_sha256":"2014bc099202d8e8d2b415552884705a8a8b5d18642ec92459f36ec234aabddd","block_sha256":"2014bc099202d8e8d2b415552884705a8a8b5d18642ec92459f36ec234aabddd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_84ea7bab-e86f-4df7-8737-d410477712ca"},{"id":"occ_429e53ed3a4cd50afaa7fe62","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_92b9c674-91b1-49a6-b0e4-4d1b0a4ef069","section_id":"sec_50df44e7-3846-410b-9bbb-d577435f5e35","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"端DRAMウェハを大量に消費する。\nAIサーバーDRAMはGPU/ASIC増加とともに伸びる。\neSSDはAI推論、RAG、KV cache、HDD代替で伸びる。\nNOR/SLC NANDは成熟品撤退により構造不足化している。\nHBF/XL-FLASHは、HBMの外側に新しい高速大容量メモリ階層を作る可能性がある","quote_start":5,"quote_end":162,"text_sha256":"ccda25410ecec8be0a6fd74d78ae7561ea8dc0daa13edba5253bbf7cd900e643","block_sha256":"ccda25410ecec8be0a6fd74d78ae7561ea8dc0daa13edba5253bbf7cd900e643","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_92b9c674-91b1-49a6-b0e4-4d1b0a4ef069"},{"id":"occ_bdec725fe5bf1b1de4e4ab6b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_b193f436-b420-4e37-9aa2-b9a280f4e7a7","section_id":"sec_0ad8d54f-23c6-4b9a-86dd-1816f4cadd14","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":181,"end":183,"exact":"推論","quote":"む | HBM、積層、先端パッケージ、電力が同時に制約になる |\n| 需要 | 顧客在庫積み増しで止まる | 推論、RAG、KV cache、主権AIクラウドが常時稼働需要になる |","quote_start":126,"quote_end":217,"text_sha256":"e0bfc08c7334d651377af4abaf5bf7ffba402b8cb8bff31fee651974f5ee1766","block_sha256":"e0bfc08c7334d651377af4abaf5bf7ffba402b8cb8bff31fee651974f5ee1766","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_b193f436-b420-4e37-9aa2-b9a280f4e7a7"},{"id":"occ_637d1003407b8dd6622ce4df","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_b978944f-7c8d-4427-8425-c5f2752ac4db","section_id":"sec_94c2e0da-daaa-44ae-b69d-55604cf8a061","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":234,"end":236,"exact":"推論","quote":"------ |\n| データセンターNAND  | TLC / QLC eSSD           | AI推論、RAG、KV cache、HDD代替 | AI需要、eSSD優先、長期契約      |\n| 組み込み・高信頼NAND | SLC / MLC NAND、NOR Flash | 車載、産業、医療、防","quote_start":179,"quote_end":336,"text_sha256":"52f0de742763b802aa81ee282e5a7e68f25214ab0d585efeb21eff5e2f1a7b3c","block_sha256":"52f0de742763b802aa81ee282e5a7e68f25214ab0d585efeb21eff5e2f1a7b3c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7/#blk_b978944f-7c8d-4427-8425-c5f2752ac4db"},{"id":"occ_f42fde206407de88db05967d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_2f5cc921-59ef-4e07-ba11-82e4656c1db7","work_id":"wrk_22580832-91eb-41f5-879d-0ee23fcf4e6f","block_id":"blk_c89844af-1e65-482d-a5fe-fe173c8dc9c6","section_id":"sec_d38d2dc1-4c9c-49f3-a96a-084f2d489db5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"QLC 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データ収集、ローカル判断、アップデート、フィードバック\n```","quote_start":0,"quote_end":116,"text_sha256":"598cf7e900b543fa7f1590f5c41454918775e296127c5e2b6b4ce6ac258ed74c","block_sha256":"598cf7e900b543fa7f1590f5c41454918775e296127c5e2b6b4ce6ac258ed74c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_385079a7-c5a3-4a8e-9c9a-7b84af62dd7e/#blk_5cd89cec-80f9-4725-a33e-e634f51035d4"},{"id":"occ_cc34ad0d7765dc56dac78999","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_385079a7-c5a3-4a8e-9c9a-7b84af62dd7e","work_id":"wrk_c607c873-652b-451e-89e4-a9043a064d80","block_id":"blk_a0b6887f-335e-4b84-82ef-b0b7d79f278f","section_id":"sec_0bfbf833-e9a5-44d1-b94b-e70ca2b9b907","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":127,"end":129,"exact":"推論","quote":"テクチャである。これは、カメラ映像を高画質に処理しながら、物体認識、人物検出、車両認識、センサー融合などのAI推論を端末側で行うための技術だ。","quote_start":72,"quote_end":143,"text_sha256":"b32301db5762b743f90a774acb97a315f1e8cc763e3ce0bb197881a7598873b4","block_sha256":"b32301db5762b743f90a774acb97a315f1e8cc763e3ce0bb197881a7598873b4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_385079a7-c5a3-4a8e-9c9a-7b84af62dd7e/#blk_a0b6887f-335e-4b84-82ef-b0b7d79f278f"},{"id":"occ_77e5ece33f0ba9a85fe1bc2d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_385079a7-c5a3-4a8e-9c9a-7b84af62dd7e","work_id":"wrk_c607c873-652b-451e-89e4-a9043a064d80","block_id":"blk_a59c8611-641f-4bfd-a9bd-3328327ec266","section_id":"sec_d4b0c41e-d6d6-4e6b-b59d-1d1a00738eed","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"Ambarellaは、カメラ映像を処理しながらAI推論を行うエッジAI 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も似た手法を採用し、高速かつ省メモリな推論・学習を実現**","quote_start":117,"quote_end":182,"text_sha256":"4d6d23adcfe343d813dce30efcafe11a587e110d5a8f9d4128790db6278422b8","block_sha256":"4d6d23adcfe343d813dce30efcafe11a587e110d5a8f9d4128790db6278422b8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_3a0c2381-6516-49a0-8369-32f5729cb14f/#blk_1e9bbe62-c44b-479b-a8b3-d288ad163a37"},{"id":"occ_e4e47b7794d20f7def3240b8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_3a0c2381-6516-49a0-8369-32f5729cb14f","work_id":"wrk_e6d329cc-bf01-4e80-ba58-758785fb93b8","block_id":"blk_41e55f6e-2071-478f-8004-c0ff69aecdda","section_id":"sec_95a1d289-72eb-449a-9d78-c67c14e61a3b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":143,"end":145,"exact":"推論","quote":"）量子化**の2つの種類がある。  \n✅ **NVIDIA Blackwellでは FP4 量子化が採用され、推論・学習効率が向上**。  \n✅ メリットは「メモリ削減・高速化・消費電力低減」、デメリットは「精度低下・学習の難しさ」。","quote_start":88,"quote_end":205,"text_sha256":"9372ee585cf58c301c72f72b40607bcc491f5c8c55c42fc1761349a969b2aa17","block_sha256":"9372ee585cf58c301c72f72b40607bcc491f5c8c55c42fc1761349a969b2aa17","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_3a0c2381-6516-49a0-8369-32f5729cb14f/#blk_41e55f6e-2071-478f-8004-c0ff69aecdda"},{"id":"occ_ea40a1fb8fa2c0bbd56d6054","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_3a0c2381-6516-49a0-8369-32f5729cb14f","work_id":"wrk_e6d329cc-bf01-4e80-ba58-758785fb93b8","block_id":"blk_43adc1f5-99d9-4bde-aed2-04ad66efc6d1","section_id":"sec_32389ce9-654f-4429-b24c-73cacbba7db6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":91,"end":93,"exact":"推論","quote":"く、**FP4（浮動小数点4ビット）を採用したことが 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GPUとHPEのインフラで構成され、カナダ企業、研究機関、公的機関向けに、学習・ファインチューニング・推論を提供するものです。([TELUS][1])","quote_start":133,"quote_end":212,"text_sha256":"0db50e5b524b1b3383bea787da89af47fed4306275ff0beaa09b629feb63764f","block_sha256":"0db50e5b524b1b3383bea787da89af47fed4306275ff0beaa09b629feb63764f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_45e2a710-51c3-42ae-a56c-b21f7fd55a5e/#blk_e407c21e-e32c-40cb-bd26-22a853314257"},{"id":"occ_9c5d7940564c3a393040adc5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_48a46dd8-5089-4a60-987b-dcda2b2ff37b","work_id":"wrk_e946941d-2715-4fb6-a635-d393a4db813b","block_id":"blk_57445abc-71f3-4c7b-89c5-ae68262629a7","section_id":"sec_36affbb4-3124-4eb0-8706-1b3d6e94759f","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":19,"end":28,"exact":"Inference","quote":"Model Size、Context、Inferenceが増える。","quote_start":0,"quote_end":33,"text_sha256":"7e282028b0cdbe4d24b36655c896c5809e9b9f2988ac5ecc37ddbd2560d766c3","block_sha256":"7e282028b0cdbe4d24b36655c896c5809e9b9f2988ac5ecc37ddbd2560d766c3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_48a46dd8-5089-4a60-987b-dcda2b2ff37b/#blk_57445abc-71f3-4c7b-89c5-ae68262629a7"},{"id":"occ_d69067d65a338ae8836905e3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_269b1c26-1e8e-43ee-a029-62c4d5f18a79","section_id":"sec_022f7872-7378-4e09-880e-843283c9d19e","layer":"code","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":67,"end":69,"exact":"推論","quote":"\n・NANDを高並列化する\n・積層構造で帯域を増やす\n・GPU近傍に配置する\n・HBMより大容量にする\n・AI推論のKVキャッシュや巨大モデル向けに使う\n```","quote_start":12,"quote_end":92,"text_sha256":"dd7db7008e355970cb7ad74a120b6e9f255df835cecd169c7e65b4150e2496c2","block_sha256":"dd7db7008e355970cb7ad74a120b6e9f255df835cecd169c7e65b4150e2496c2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_269b1c26-1e8e-43ee-a029-62c4d5f18a79"},{"id":"occ_5a87fa5d17b2f3de5f41eb73","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_276d847a-07d2-445d-9825-4d7ccee3512b","section_id":"sec_8ab76917-d81d-4ff7-9055-f4d59cd6e5e2","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"特に長文推論やエージェントでは、KVキャッシュ、検索インデックス、過去文脈、ツール実行ログなどが巨大化する。  \nこれをすべてHBMに置くのは非現実的である。","quote_start":0,"quote_end":79,"text_sha256":"e70724edac05e51561fb308129c44a374cc613c127bf20bced3896592064be70","block_sha256":"e70724edac05e51561fb308129c44a374cc613c127bf20bced3896592064be70","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_276d847a-07d2-445d-9825-4d7ccee3512b"},{"id":"occ_2b4cebbc36752907a1785240","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_2c7ff5ec-6766-4fac-9d93-c25ec419fb60","section_id":"sec_9dd8cd28-26e8-4757-814f-cc7bd673fc71","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"HBFやXL-FLASHは、NANDを単なるストレージから、AI推論の記憶階層へ押し上げる技術である。  \nCPOやCOUPEは、光I/Oをボード端からパッケージ近傍へ移し、AIクラスタの接続限界を突破しようとする技術である。  \nSRAMは、依然としてチップ内最速の","quote_start":0,"quote_end":134,"text_sha256":"2813576e0fc934781a50a725b9e9fa29fa6eb03e07e930f6c29fadadd59f03d4","block_sha256":"2813576e0fc934781a50a725b9e9fa29fa6eb03e07e930f6c29fadadd59f03d4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_2c7ff5ec-6766-4fac-9d93-c25ec419fb60"},{"id":"occ_a572cc3a4aa92336f0bbff30","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_2f99ac9a-9bce-44c2-a01d-7236b521134b","section_id":"sec_b096ef5b-f66e-4fc5-9e83-e096ae58fc36","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":76,"end":83,"exact":"KVキャッシュ","quote":"  \nGPUの演算性能は伸びるが、HBMは高価で、容量も物理的・熱的・コスト的に増やしにくい。巨大モデル、長いKVキャッシュ、大量の推論リクエストを扱うには、HBMだけでは足りない。","quote_start":21,"quote_end":112,"text_sha256":"eb5022e5f8cd8789c18d62112ee19b730882d931ab1fe351bc59658e2f483cb0","block_sha256":"eb5022e5f8cd8789c18d62112ee19b730882d931ab1fe351bc59658e2f483cb0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_2f99ac9a-9bce-44c2-a01d-7236b521134b"},{"id":"occ_661bfc85e6aeada939a5f0b9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_40a67780-a891-4753-89e2-2411665bc284","section_id":"sec_022f7872-7378-4e09-880e-843283c9d19e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":95,"end":97,"exact":"推論","quote":"を土台に、シリコン技術、設計技術、3Dスタックパッケージを組み合わせ、高帯域・高耐久・エネルギー効率を狙うAI推論向けメモリとして説明している。([SanDisk Documents](https://documents.sandisk.com/content/dam/asset-library/en_us/as","quote_start":40,"quote_end":197,"text_sha256":"e14ba5fefede90138bc1d18871cacd71de1db7ebd3562c73a1677042032a0f84","block_sha256":"e14ba5fefede90138bc1d18871cacd71de1db7ebd3562c73a1677042032a0f84","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_40a67780-a891-4753-89e2-2411665bc284"},{"id":"occ_3ea25adc03e061508132948d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_571c5be6-02c5-4555-803c-3b8879055bca","section_id":"sec_b096ef5b-f66e-4fc5-9e83-e096ae58fc36","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"しかし今後、AI推論、長文コンテキスト、AIエージェント、マルチラックGPUクラスタが拡大すると、これだけでは足りなくなる。","quote_start":0,"quote_end":62,"text_sha256":"8997785c1a4d332bbb9418cb586d2c66e30eb8f8aa29e1ba442e694fb5da1e3e","block_sha256":"8997785c1a4d332bbb9418cb586d2c66e30eb8f8aa29e1ba442e694fb5da1e3e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_571c5be6-02c5-4555-803c-3b8879055bca"},{"id":"occ_4f557cf06c63c8c0a1ed9d75","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955","work_id":"wrk_cb25df93-468d-4abf-9350-ba1bcf0d3b49","block_id":"blk_5c700c0f-38d0-49ff-8212-5640a5919386","section_id":"sec_8ab76917-d81d-4ff7-9055-f4d59cd6e5e2","layer":"code","character_id":null,"count":2,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":13,"end":20,"exact":"KVキャッシュ","quote":"```\nHBM：今すぐ使うKVキャッシュ\nHBF：大容量の近接KVキャッシュ\nXL-FLASH：GPUアクセス可能な低遅延ストレージ\n光I/O：それらをラック/マルチラックでつなぐ\n```","quote_start":0,"quote_end":94,"text_sha256":"229c6005ddcaf2dc7acfa1314bb02bb00a98658bbc3c6dc6a5e5986e88c361c3","block_sha256":"229c6005ddcaf2dc7acfa1314bb02bb00a98658bbc3c6dc6a5e5986e88c361c3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_49a8bdbd-8ee8-4c28-8af4-da1787c47955/#blk_5c700c0f-38d0-49ff-8212-5640a5919386"},{"id":"occ_c2b20abb84b0c421153e3a5e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_0198dd9c-a9d6-4824-bbe3-90afa8500bc2","section_id":"sec_8f5e98b8-36f6-4deb-98a5-ee7140d75f18","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":182,"end":184,"exact":"推論","quote":"のUXで勝ちやすく、Anthropicは裏の採用・統合・監査で勝ちやすいからです。これは現時点の公開戦略からの推論です。 ([Reuters](https://www.reuters.com/business/openai-developing-ai-devices-including-smart-speaker","quote_start":127,"quote_end":284,"text_sha256":"f6089e1ab39382a2516994effb3f9e0816b6b5977af77dd323a7a12d022a70fa","block_sha256":"f6089e1ab39382a2516994effb3f9e0816b6b5977af77dd323a7a12d022a70fa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_0198dd9c-a9d6-4824-bbe3-90afa8500bc2"},{"id":"occ_ba3c25601d48add73f9be719","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_08f56881-e45b-4964-bb63-95e0a644c0db","section_id":"sec_7b02aa57-4b84-4a39-a0d4-0dde875087dd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":200,"end":202,"exact":"推論","quote":"辺倒でもない。むしろ、人が使う端末と企業が使う実行基盤の両方をつなぐ“表側の知能OSに近づくと思われる。これは推論だが、現在の買収・提携・製品構成からはかなり自然だ。 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Facebook](https://about.fb.com/news/2026/03/meta-ai-glasses-built-fo","quote_start":149,"quote_end":306,"text_sha256":"e71a360aa09c866ca50bf10b5aa2b4ca07ca42de0a9da4fa2383b414e15885bc","block_sha256":"e71a360aa09c866ca50bf10b5aa2b4ca07ca42de0a9da4fa2383b414e15885bc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_37b04a63-f3b0-4580-9c5d-b5fad5b427a4"},{"id":"occ_9a4813bce8473bf54f27c99b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_4f85357f-f388-4972-b8a4-2a603f55925f","section_id":"sec_3dc3ab31-c20f-4352-bdf0-8ec71028e191","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":67,"end":69,"exact":"推論","quote":"ユーザー体験と収益化がぶつかりにくい**ことです。  \n良い生成物を作る人ほど課金されやすく、OpenAI側も推論課金、生成課金、分配手数料を取れます。  \n私なら、OpenAIがSNSを持つなら **初期本命はこれ** 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Facebook](https://about.fb.com/news/2025/09/meta-ray-b","quote_start":192,"quote_end":349,"text_sha256":"cc8d63e70bec57fc683e0c9198bd59bf71ee449a3acdc1c1fb98d1fdea5f8911","block_sha256":"cc8d63e70bec57fc683e0c9198bd59bf71ee449a3acdc1c1fb98d1fdea5f8911","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_5c3e228f-30d7-48e5-8d3b-8a06913bf0f4"},{"id":"occ_af8705e217452dcefe4d2918","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_6bc5b137-86c8-4e3a-990f-98f7970ce59c","section_id":"sec_f6c9bcb8-90b7-4d6f-8771-10eb96aea863","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":355,"end":357,"exact":"推論","quote":"bution と 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DeepMind](https://deepmind.google/m","quote_start":139,"quote_end":296,"text_sha256":"15fe8ae413bb499222e6fd486f31a30e7ae137c1f4e5a4d62e8c75014ebfd25d","block_sha256":"15fe8ae413bb499222e6fd486f31a30e7ae137c1f4e5a4d62e8c75014ebfd25d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_7cc57504-333b-48b3-9132-15addeed55e4"},{"id":"occ_163a6f930d644a743cb2a54c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_808a0a87-6004-4a0b-8dde-17d4a6ca7fd6","section_id":"sec_69cb3877-2d91-412f-a0a4-9dc62f999af0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":187,"end":189,"exact":"推論","quote":"boticsは物理空間を理解して多段タスクを自律的にこなし、Gemini 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News](https://www.aboutamazon.com/news/operations/amazon-mi","quote_start":153,"quote_end":310,"text_sha256":"98d49d727ce911a69aa70dd9a93ff57db5645a0501508e905556dd10ce2c9530","block_sha256":"98d49d727ce911a69aa70dd9a93ff57db5645a0501508e905556dd10ce2c9530","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_bbb4ad9b-9e15-49fc-8d8e-157069de798e"},{"id":"occ_59334eca3e0fcffe4a3d51a6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6","work_id":"wrk_897da4b4-3324-4db0-b370-04a77562fa7c","block_id":"blk_bfde8126-f553-45c1-a940-801260494a7b","section_id":"sec_dcc6a454-612d-41a2-984a-20116fc98b58","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":256,"end":258,"exact":"推論","quote":"AI時代の広告は、**注意の奪い合いから、推薦システムへの接続料・送客料・成約料へと比重を移す**。これは私の推論だが、Amazonの現行機能はその方向をかなりはっきり示している。 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MCP をオープン化しているぶん、規格が広がっても利益の大半を直接取れないリスクもあります。これは推論ですが、Anthropicの弱点は“深く刺さるが、面で取りにくい”ことです。 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**“AIが動いた結果に対する課金”** だと思います。これは推論ですが、もっとも OpenAI らしい勝ち筋です。 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Google、個人装着の本命は Meta**  \nという整理がいちばん使いやすいです。これは推論ですが、現時点の事業配置から見るとかなり自然な結論です。 ([Amazon 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News](https://www.aboutamazon.co","quote_start":108,"quote_end":265,"text_sha256":"0f94922397459d0442abe4582a218a9bda6aa353172997f238e48da0f55e8d2f","block_sha256":"0f94922397459d0442abe4582a218a9bda6aa353172997f238e48da0f55e8d2f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_513cc5f0-bfa3-418a-9ecc-cd67eca8bae6/#blk_f588cf88-995c-4c66-86e4-754d278897d2"},{"id":"occ_a70c58dd9d1ccaa2d04b8927","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_53605de4-06cc-4e0b-9bc9-21f393408c0a","work_id":"wrk_006833f6-f225-48d3-8754-46fe28d6bd8e","block_id":"blk_2f04f916-dc49-43bd-901e-77dd4c856d52","section_id":"sec_7c91db57-1adf-4ae2-ad6f-5ced1a9ebb9b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"AI OS化、オンデバイスAI、AI PC、スマホ内AI、ローカル推論、個人エージェント。  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 \n一方で低価格帯の端末は、最低限のアプリ、クラウドAI、軽量処理だけを担う。  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SKU\n・大型モデルをローカルに置きやすい\n・KVキャッシュを増やせる\n・GPU間通信を減らせる\n・AMDや独自ASICとの性能競争に有利","quote_start":0,"quote_end":75,"text_sha256":"92dc6a92819843652a0f7134b8642291fe8af358a2f6e0d839f5281153efb19b","block_sha256":"92dc6a92819843652a0f7134b8642291fe8af358a2f6e0d839f5281153efb19b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5387a515-478b-41ca-82a3-5b54af988fd1/#blk_c2bf9ddd-0a87-43d1-b05a-54d5065f94fd"},{"id":"occ_d70cb5e4481223e877e8b4f6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5387a515-478b-41ca-82a3-5b54af988fd1","work_id":"wrk_41cc4908-f3de-420c-9823-faa2e85483cd","block_id":"blk_c89791c1-014b-4cb2-8c3f-3efafd0efa7f","section_id":"sec_d3201fcd-8747-496c-afde-61e805088464","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論単価下落を利用量増加が補えない","quote_start":0,"quote_end":17,"text_sha256":"ad6a25098d0de2249992a0afeaa8777e875249915881dd0060f8a54bf6fe0d3e","block_sha256":"ad6a25098d0de2249992a0afeaa8777e875249915881dd0060f8a54bf6fe0d3e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5387a515-478b-41ca-82a3-5b54af988fd1/#blk_c89791c1-014b-4cb2-8c3f-3efafd0efa7f"},{"id":"occ_3adea356d20cdd25bc3b15dc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b","work_id":"wrk_52a8c79a-5c56-4f34-a515-1b1dcb4c0477","block_id":"blk_04ab5249-921a-4989-9621-34a278731976","section_id":"sec_1b0e96e4-f15c-450b-b532-32951cc180b7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"むしろ現実的なのは、Meta側がGPUクラスタを管理し、顧客にはAPIや推論エンドポイントとして提供する形である。","quote_start":0,"quote_end":57,"text_sha256":"cae5636b8106e9f7e808053d9a8d0a394ea3a9f45305c7d9af919c67b3cc1feb","block_sha256":"cae5636b8106e9f7e808053d9a8d0a394ea3a9f45305c7d9af919c67b3cc1feb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b/#blk_04ab5249-921a-4989-9621-34a278731976"},{"id":"occ_13ec5f40d34a444142c77c9e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b","work_id":"wrk_52a8c79a-5c56-4f34-a515-1b1dcb4c0477","block_id":"blk_11feb81b-dafa-4135-80ff-19ca42e67f50","section_id":"sec_9bd26956-bf91-4de5-bd2e-7de08a23629f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"GPUクラウドでも、司令塔はCPUに依存する。特にエージェントAIや長文推論では、GPUだけでは処理は完結しない。","quote_start":0,"quote_end":57,"text_sha256":"35a4504ac02e67b998da45370959131695f75d9c7681cad63c61358a95e50177","block_sha256":"35a4504ac02e67b998da45370959131695f75d9c7681cad63c61358a95e50177","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b/#blk_11feb81b-dafa-4135-80ff-19ca42e67f50"},{"id":"occ_744b4ca8e06a59372fd0307d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b","work_id":"wrk_52a8c79a-5c56-4f34-a515-1b1dcb4c0477","block_id":"blk_254a632b-ddb3-4101-a603-0f23c8fbce37","section_id":"sec_f7dac3a0-aed8-4bb9-bc2d-a5a46c8e1050","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":68,"end":70,"exact":"推論","quote":"なのは「GPUをどれだけ保有しているか」ではない。重要なのは、**同じGPUからどれだけ多くの売上、トークン、推論、SLA、顧客価値を生み出せるか**である。","quote_start":13,"quote_end":92,"text_sha256":"3fc924132c58477ec7c9c125de8eee064f1a0d4c96d590efa262fe24f1fa3d0b","block_sha256":"3fc924132c58477ec7c9c125de8eee064f1a0d4c96d590efa262fe24f1fa3d0b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b/#blk_254a632b-ddb3-4101-a603-0f23c8fbce37"},{"id":"occ_599f49792f441f5e28338e02","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b","work_id":"wrk_52a8c79a-5c56-4f34-a515-1b1dcb4c0477","block_id":"blk_353fe60b-1222-463c-9ea6-6a05e7d0c0a3","section_id":"sec_d282086a-2fa0-45ca-a745-87a33d833700","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":88,"end":95,"exact":"KVキャッシュ","quote":"GPUは大量の並列演算をまとめて走らせる装置であり、HBM、CUDAコンテキスト、カー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ASICはさらに特定ワークロードに寄るため、「誰でも好きな処理を安全に細切れで動かす」には向きにくい。","quote_start":33,"quote_end":171,"text_sha256":"d8602cb46282be96e73d14b85fd83482894182f29176bae4260b74d80e27e072","block_sha256":"d8602cb46282be96e73d14b85fd83482894182f29176bae4260b74d80e27e072","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b/#blk_353fe60b-1222-463c-9ea6-6a05e7d0c0a3"},{"id":"occ_1804dae721f7747207846841","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_54b154f3-ea9f-45e9-b38b-a16c54ca3e2b","work_id":"wrk_52a8c79a-5c56-4f34-a515-1b1dcb4c0477","block_id":"blk_3da1e324-7288-4725-bbea-1b9f2f0877ad","section_id":"sec_3445f6ee-9f13-4933-90eb-d8a42d7b1601","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":168,"end":170,"exact":"推論","quote":"呼び出せるサーバーレスAI基盤として説明されている。 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Meta 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\nMeta","quote_start":0,"quote_end":127,"text_sha256":"4060c86e3ef3a54fe0617f9ea15c0cb7d03ce95cc6041550e34593e90aef8ebf","block_sha256":"4060c86e3ef3a54fe0617f9ea15c0cb7d03ce95cc6041550e34593e90aef8ebf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5603783d-df89-4ab1-ba77-c8a24ace4a7b/#blk_da504abd-abe2-4dc4-ae2e-b4da0b1ba0b7"},{"id":"occ_ce72ddb0488f2185b0804e26","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5603783d-df89-4ab1-ba77-c8a24ace4a7b","work_id":"wrk_bbe3569f-7530-42b2-8b5f-75a35e4b1cba","block_id":"blk_eb76e95e-bd5d-441e-9948-c501fe9602ac","section_id":"sec_6fa0c532-d060-4efd-bb15-29528157db56","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"新しい学習レシピはスケールするか。  \n推論時スケーリングは効くか。  \n複数エージェントを並列に動かす設計は有効か。  \ntool useは安定するか。  \nマルチモーダル推論はMeta AIやAIグラスに載るか。  \n安全性リスクはどこに出","quote_start":0,"quote_end":122,"text_sha256":"cf6ca8746e1c1f25512f08ee826c583286ac6ef3617cb729868ee1037c3afadb","block_sha256":"cf6ca8746e1c1f25512f08ee826c583286ac6ef3617cb729868ee1037c3afadb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5603783d-df89-4ab1-ba77-c8a24ace4a7b/#blk_eb76e95e-bd5d-441e-9948-c501fe9602ac"},{"id":"occ_32c2f6b22308d3321443a55d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_1d87a7cc-daaf-4182-8112-c9fd4725c2a9","section_id":"sec_92b0e1ca-d927-4d8b-b6f0-930e129eeac6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":343,"end":345,"exact":"推論","quote":"(https://www.arm.com/products))\n- **Ethos NPUs**  \n  AI推論向け NPU IP。 ([arm.com](https://www.arm.com/products))\n- **System IP**  \n  CoreLink interconnect、memor","quote_start":288,"quote_end":445,"text_sha256":"6ac57152dd88e8183c6f729e08333a429f53c18d4ec55e5ca4d41f56c4fd591e","block_sha256":"6ac57152dd88e8183c6f729e08333a429f53c18d4ec55e5ca4d41f56c4fd591e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_1d87a7cc-daaf-4182-8112-c9fd4725c2a9"},{"id":"occ_bd3d45bfe47962e02e1e07eb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_26887ccd-fd5d-4e49-948f-bdbf4ee81931","section_id":"sec_184cd75f-8414-43bf-a348-c029dcf63ead","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"## \\10. 学習と推論で少し違う","quote_start":0,"quote_end":18,"text_sha256":"139b40ab7b6c845f4ab2035d1835c99496eaf265005aeef5386ea05cce2d83f2","block_sha256":"139b40ab7b6c845f4ab2035d1835c99496eaf265005aeef5386ea05cce2d83f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_26887ccd-fd5d-4e49-948f-bdbf4ee81931"},{"id":"occ_16eafe3d9730914ed4f8b289","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_29c32106-6dea-4f9e-9d63-014f5371a66b","section_id":"sec_2b4ee7b7-df45-49a6-aab0-fcf9521879c5","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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していると説明しています","quote_start":98,"quote_end":255,"text_sha256":"0727889f1d69119a70ff337c4faca3ed281d876253765e6976018d24949f6964","block_sha256":"0727889f1d69119a70ff337c4faca3ed281d876253765e6976018d24949f6964","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_29c32106-6dea-4f9e-9d63-014f5371a66b"},{"id":"occ_6f487212d33396c7d714ea0e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_46a71c94-a1f1-4778-9778-e17c43cec750","section_id":"sec_4eba16b0-44c9-4ccd-adf0-955f7b8ee26f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":109,"end":111,"exact":"推論","quote":"証機の償却またはリース\n- IP の個別契約  \n  を別々に管理しているはずです。これは各社の収益構造からの推論です。 ([SEC](https://www.sec.gov/Archives/edgar/data/883241/000088324125000024/snps-20250731.htm))","quote_start":54,"quote_end":207,"text_sha256":"a7543ee9d0dee5f1a05a91642cee34aa4ae9bebb2c429147dc184df397816460","block_sha256":"a7543ee9d0dee5f1a05a91642cee34aa4ae9bebb2c429147dc184df397816460","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_46a71c94-a1f1-4778-9778-e17c43cec750"},{"id":"occ_460a05a95dd09d28863c638c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_4c7dcc7a-458e-4938-9f39-20e3a74e435b","section_id":"sec_efba70c3-f60e-433f-9af4-497137267211","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":203,"end":205,"exact":"推論","quote":"す。これは Synopsys/Cadence の time-based enterprise 寄りモデルからの推論です。 ([SEC](https://www.sec.gov/Archives/edgar/data/883241/000088324125000024/snps-20250731.htm))","quote_start":148,"quote_end":301,"text_sha256":"2eab1abb0199ce50e90c3ab909277b88877c7b18c2297f0c64fefd00702a723e","block_sha256":"2eab1abb0199ce50e90c3ab909277b88877c7b18c2297f0c64fefd00702a723e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_4c7dcc7a-458e-4938-9f39-20e3a74e435b"},{"id":"occ_e026caa4aad7caf170b7a10f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_6daacd41-3d53-40a6-9241-37c9d15418a0","section_id":"sec_1dda1140-e42d-480e-857a-55eb537720bb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":274,"end":276,"exact":"推論","quote":" の認識期間が複数年にまたがることを見ると、大口顧客契約は相当大きいはずだと推測できます。これは公開財務からの推論です。 ([investor.cadence.com](https://investor.cadence.com/news/news-details/2026/Cadence-Reports-Four","quote_start":219,"quote_end":376,"text_sha256":"b3bd38be1c1fa75f28ffcc8b81a2ba31c0cb2364acc61096589af86adbcaf16e","block_sha256":"b3bd38be1c1fa75f28ffcc8b81a2ba31c0cb2364acc61096589af86adbcaf16e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_6daacd41-3d53-40a6-9241-37c9d15418a0"},{"id":"occ_6ecd7bfaaa3a8c340103941d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_6e166e4a-69b9-4ff6-b0c3-b719f2fd3b0f","section_id":"sec_a46a91f6-6a18-4769-8cc8-367486b8fd20","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":38,"end":40,"exact":"推論","quote":"- スマホ向けなのか\n- 自動運転向けなのか\n- SSD向けなのか\n- AI推論向けなのか","quote_start":0,"quote_end":45,"text_sha256":"c38e63345c06f712ea901a2cbd1636e14d2f8787e408bcc9a8201a337fc682a6","block_sha256":"c38e63345c06f712ea901a2cbd1636e14d2f8787e408bcc9a8201a337fc682a6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_6e166e4a-69b9-4ff6-b0c3-b719f2fd3b0f"},{"id":"occ_5748b98221035cb9e7b6b395","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_8ebb9443-dabc-4a36-81d3-f328221ec355","section_id":"sec_2e196580-e3da-4a60-9d94-1393aa8007da","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":126,"end":128,"exact":"推論","quote":"級顧客”** を考えると、もう少し下のレンジで見る方が自然です。これは公開されている売上規模と顧客集中度からの推論です。 ([SEC](https://www.sec.gov/Archives/edgar/data/883241/000088324125000028/snps-20251031.htm))","quote_start":71,"quote_end":224,"text_sha256":"0b9e3119ece362ba868a8f67725f29c77fc1bc3261c5185024674c7125307046","block_sha256":"0b9e3119ece362ba868a8f67725f29c77fc1bc3261c5185024674c7125307046","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_8ebb9443-dabc-4a36-81d3-f328221ec355"},{"id":"occ_128a27f99b9e649ac301c568","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_9c71fbed-c9a3-42aa-a303-3eafd8b3c682","section_id":"sec_b408517f-f7ef-419c-9415-2e882e191059","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":96,"end":98,"exact":"推論","quote":"材**は **HBM**\n- **クラスタが巨大になるほど伸びる**のは **NIC とスイッチ**\n- **推論/RAGが広がるほど効く**のは **SSD** です。 ([Micron Technology](https://investors.micron.com/static-files/5fb98d73","quote_start":41,"quote_end":198,"text_sha256":"0401bfa343e42305679a3f82d680245f3a9115ae1328ea4b103c5c7eaebf43f2","block_sha256":"0401bfa343e42305679a3f82d680245f3a9115ae1328ea4b103c5c7eaebf43f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_9c71fbed-c9a3-42aa-a303-3eafd8b3c682"},{"id":"occ_24962697be859561b689ebd7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_a3641e44-351f-4f2c-8222-a41800aa445e","section_id":"sec_61e7b84e-5e65-43f7-b3d7-d46e3fa39132","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":73,"end":75,"exact":"推論","quote":"では、締切前の検証や PPA 探索が急増しやすいので、この使い方はかなりありそうです。これは公開モデルに基づく推論です。 ([Cadence](https://www.cadence.com/en_US/home/explore/eda-in-the-cloud.html?utm_source=chatgpt.c","quote_start":18,"quote_end":175,"text_sha256":"97b4d07d04b64564d7c569d38b31889ca06b553533c7b6b9d57949de6aea16c5","block_sha256":"97b4d07d04b64564d7c569d38b31889ca06b553533c7b6b9d57949de6aea16c5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_a3641e44-351f-4f2c-8222-a41800aa445e"},{"id":"occ_bfecaab9486b0ad30814a035","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_ac90e480-305f-4009-8068-25de5a5a8b77","section_id":"sec_bdf0f83a-3199-4b84-9c90-2e76181d4891","layer":"body","character_id":null,"count":3,"matched_aliases":["KV 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tier","quote_start":0,"quote_end":104,"text_sha256":"2133a9d04202ff7f347d75bd7a6e938e040cb8389afe30d0590bf511044a89c0","block_sha256":"2133a9d04202ff7f347d75bd7a6e938e040cb8389afe30d0590bf511044a89c0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_ac90e480-305f-4009-8068-25de5a5a8b77"},{"id":"occ_e3c471f3a0874f9d57c694e1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_bd6d1aed-8928-4537-8c3f-92456dc86c87","section_id":"sec_19e5b639-214f-4105-b1a6-d9a4ac5a588b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":289,"end":291,"exact":"推論","quote":"**Ansys を飲み込んで本当に platform company になれるか** が主論点です。これは私の推論ですが、ソースが示す TAM 拡大と統合ロードマップから見ると、4社の中でいちばん“上振れの幅”が大きいのは Synopsys です。 ([investor.synopsys.com](https:/","quote_start":234,"quote_end":391,"text_sha256":"f3f60ce5d86aed5195ef00bce3b13e42ebcae4c062bae7498ee0e0d904b9c223","block_sha256":"f3f60ce5d86aed5195ef00bce3b13e42ebcae4c062bae7498ee0e0d904b9c223","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_bd6d1aed-8928-4537-8c3f-92456dc86c87"},{"id":"occ_d73252f4bcc63754816b363e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_c175039d-57c6-4a5f-a49f-c5eeb4857833","section_id":"sec_88ead90f-a526-4010-8169-eb728bb1a6e0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":267,"end":269,"exact":"推論","quote":"ずGPU、その次がHBMで、NICは重要だが主役の次、SSDは必要でも相対的には脇役寄りです。これは構成からの推論です。 ([NVIDIA](https://www.nvidia.com/en-us/data-center/dgx-b200/))","quote_start":212,"quote_end":335,"text_sha256":"bb45d5fd4cb8315ef8b1acb455f1b7432dcc010a8db184ceb8ed3f8896eebb0a","block_sha256":"bb45d5fd4cb8315ef8b1acb455f1b7432dcc010a8db184ceb8ed3f8896eebb0a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_c175039d-57c6-4a5f-a49f-c5eeb4857833"},{"id":"occ_8dcb4342ee04262e3ab67ed6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_c446d4d6-4cff-420a-985a-c64dd4480ee5","section_id":"sec_c3abfd56-efb5-4fb1-941b-24f8126efd74","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論でも学習でも、  \n実際に重い計算をしているのは主にここです。","quote_start":0,"quote_end":35,"text_sha256":"c31b57d150750bb59b10dc200d531fe165eff1ce9ee0944dbcad9350aa65a86c","block_sha256":"c31b57d150750bb59b10dc200d531fe165eff1ce9ee0944dbcad9350aa65a86c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_c446d4d6-4cff-420a-985a-c64dd4480ee5"},{"id":"occ_9dab8402255049326ff88a37","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_d83425cd-e843-4331-998d-f6f4bf643fd5","section_id":"sec_0fdefcfb-d97d-456e-a29d-1252eeb63fbc","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"### AI推論","quote_start":0,"quote_end":8,"text_sha256":"c92687708d83f1c23deccf7bf7b71299b69ee4de9fc66d896ae705bba3bc6561","block_sha256":"c92687708d83f1c23deccf7bf7b71299b69ee4de9fc66d896ae705bba3bc6561","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_d83425cd-e843-4331-998d-f6f4bf643fd5"},{"id":"occ_e93e1acde66e868fd719dc46","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_e9f5758d-1b11-4915-8eba-80908cbd6f9a","section_id":"sec_88ead90f-a526-4010-8169-eb728bb1a6e0","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":113,"end":115,"exact":"推論","quote":"\\> SSD/ストレージ**  \nが基本です。  \nただし、**学習中心**ならネットワーク比重が上がり、**推論・RAG・ベクトルDB中心**ならSSD比重が上がります。Micronは、AI推論での **KV cache tiering** や **vector database 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([SEC](https://www.sec.gov/Archives/edgar/data/883241/000088324125000024","quote_start":74,"quote_end":231,"text_sha256":"dcd9319a5e3855d01f9edca9df121eba87133bd699913d4ba8919623ece7709c","block_sha256":"dcd9319a5e3855d01f9edca9df121eba87133bd699913d4ba8919623ece7709c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_f546e1e9-dd47-447d-ad6c-48b94f694d39"},{"id":"occ_7b925823f6c38516637d0d0c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_f5cf3f98-d7fe-4d9a-8fa7-114801582edc","section_id":"sec_15857941-6775-4501-b9eb-a82f49214eb8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":89,"end":91,"exact":"推論","quote":"は個別プロジェクト単位  \n  という形で、契約の箱が分かれているはずです。これは公開されている販売構造からの推論です。 ([SEC](https://www.sec.gov/Archives/edgar/data/883241/000088324125000024/snps-20250731.htm))","quote_start":34,"quote_end":187,"text_sha256":"ef7259077dbcc21e41b2a7d969d4a344612af4bfda597c84a8838a7dd5c63736","block_sha256":"ef7259077dbcc21e41b2a7d969d4a344612af4bfda597c84a8838a7dd5c63736","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_f5cf3f98-d7fe-4d9a-8fa7-114801582edc"},{"id":"occ_dc3a7f8ae7a352974b07f4e4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57810a5f-315e-49a7-ac85-72cc27e35eff","work_id":"wrk_fc529d17-dbc8-4405-b2ca-a48accef7253","block_id":"blk_fac020a8-b663-43c5-8c0c-b9ac056931e6","section_id":"sec_19e5b639-214f-4105-b1a6-d9a4ac5a588b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":174,"end":176,"exact":"推論","quote":"単独ではなく Synopsys の統合価値として見る。**  \nこの順です。これは企業側の公開資料に基づく私の推論ですが、**“AI設計ツールの王道”は Cadence、“AI時代の工学プラットフォーム化”は Synopsys、“AIチップ普及の取り分”は Arm** と分けるとかなり見やすいです。 ([Cade","quote_start":119,"quote_end":276,"text_sha256":"6503b06e67497f795dfb78cbd50c0b440d2d00414df0c1a0e210f53563debfa0","block_sha256":"6503b06e67497f795dfb78cbd50c0b440d2d00414df0c1a0e210f53563debfa0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57810a5f-315e-49a7-ac85-72cc27e35eff/#blk_fac020a8-b663-43c5-8c0c-b9ac056931e6"},{"id":"occ_e5a02b3eac55875e30ffa0f0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57995821-c471-42b5-ae3a-f989b7bd06f5","work_id":"wrk_ffa6d69b-69a7-4979-a51d-6b89aa7b0ff5","block_id":"blk_02eb402d-4d5e-48e5-bd38-d01750dd5a0a","section_id":"sec_749fd69f-a80c-498f-893a-b30710e5e8b8","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":37,"end":44,"exact":"KVキャッシュ","quote":"大規模モデルにはHBMが必要であり、AIエージェントには長いコンテキストとKVキャッシュが必要になる。AIスマートフォン、AI PC、自動車、ロボット、監視装置にも、従来より大容量のDRAMとストレージが搭載されるだろう。","quote_start":0,"quote_end":111,"text_sha256":"94aab205e069a4fa1e94319ab28de6987ba8a410050262c8fa8262e336280dcb","block_sha256":"94aab205e069a4fa1e94319ab28de6987ba8a410050262c8fa8262e336280dcb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57995821-c471-42b5-ae3a-f989b7bd06f5/#blk_02eb402d-4d5e-48e5-bd38-d01750dd5a0a"},{"id":"occ_27329954e50d86ed2aef16ee","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57995821-c471-42b5-ae3a-f989b7bd06f5","work_id":"wrk_ffa6d69b-69a7-4979-a51d-6b89aa7b0ff5","block_id":"blk_11bc3e62-9ff9-4bec-accd-1f5fbd172ee0","section_id":"sec_bad096f2-e560-4dce-9847-9dc60a60edf9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"> 待てよ。  \n> 数十億人を受け止められる推論基盤が完成しても、計算資源の性能が上がり続けるなら、ハイパースケーラーは結局、次の半導体を買い続けなければならないのではないか。","quote_start":0,"quote_end":89,"text_sha256":"b558153048aacdd56fde2177ba209cbb24e9540bd2648da468bf517cb76e3ca0","block_sha256":"b558153048aacdd56fde2177ba209cbb24e9540bd2648da468bf517cb76e3ca0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57995821-c471-42b5-ae3a-f989b7bd06f5/#blk_11bc3e62-9ff9-4bec-accd-1f5fbd172ee0"},{"id":"occ_4ff67f3563536bec6cc9a10c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57995821-c471-42b5-ae3a-f989b7bd06f5","work_id":"wrk_ffa6d69b-69a7-4979-a51d-6b89aa7b0ff5","block_id":"blk_3098d258-107d-4515-9dc7-5ffc1ede0dd6","section_id":"sec_d471f69a-d3ee-4391-a0b0-219f8dd61f6e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"- 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Google TPU\n- Microsoft Maia\n- Broadcom系XPU\n- 新しい推論アクセラレータ","quote_start":33,"quote_end":97,"text_sha256":"35f2122c0c16dac10c6102b099641142cd60d0d6da34293b0bb70aa9f9f3c434","block_sha256":"35f2122c0c16dac10c6102b099641142cd60d0d6da34293b0bb70aa9f9f3c434","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57995821-c471-42b5-ae3a-f989b7bd06f5/#blk_d445a394-0d9a-4208-895e-5fbc77af8046"},{"id":"occ_ee3cd47090c176341b606494","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_57995821-c471-42b5-ae3a-f989b7bd06f5","work_id":"wrk_ffa6d69b-69a7-4979-a51d-6b89aa7b0ff5","block_id":"blk_efc92fea-52d3-401e-b9a9-02ad09159a59","section_id":"sec_cc5c929a-df3c-4c08-8dfe-b437f7f4dc7b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"モデルが入れ替わるたびに、それをホストし、推論し、保存し、接続することで収益を得られる。","quote_start":0,"quote_end":44,"text_sha256":"5507135cd98b7dae336ee46090de90943c790cac5c86c28655c3948851973122","block_sha256":"5507135cd98b7dae336ee46090de90943c790cac5c86c28655c3948851973122","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_57995821-c471-42b5-ae3a-f989b7bd06f5/#blk_efc92fea-52d3-401e-b9a9-02ad09159a59"},{"id":"occ_f03745298967b5bf42c3e548","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_04994c51-7c20-43c2-a1e6-6268d76dd656","section_id":"sec_0289410f-2a3a-4d53-a244-efa6407f153a","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"提言としては、(a) 量子化・圧縮を“ハード命令＋コンパイラ＋ランタイム”で共同設計し、(b) MoEや推論の精度要求に合わせて“層別に精度を変える/圧縮率を変える”ことを高速に行い、(c) 推論中に発生するKVやactivationsの圧縮/転送も含めて最適化することです。これはGroq側のTruePoi","quote_start":0,"quote_end":154,"text_sha256":"edb86216b7c021411d8b324f16b8afe63f8040dcc2a791da0ba6761181d98ea9","block_sha256":"edb86216b7c021411d8b324f16b8afe63f8040dcc2a791da0ba6761181d98ea9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_04994c51-7c20-43c2-a1e6-6268d76dd656"},{"id":"occ_24accb08215892c5aa6706f6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_04fdc412-2b1c-452a-aeaa-70355b55d5c8","section_id":"sec_409e6cb8-dd7b-4ea8-9210-ad8f1d0a1609","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":31,"end":39,"exact":"KV cache","quote":"## じゃあ、なぜ PagedAttention や FP8 KV cache が出てくるのか","quote_start":0,"quote_end":47,"text_sha256":"c25ea21d0007742a3dfda88e5213426e5bbdbc5f33f7ec0db2d61f7b64d584ed","block_sha256":"c25ea21d0007742a3dfda88e5213426e5bbdbc5f33f7ec0db2d61f7b64d584ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_04fdc412-2b1c-452a-aeaa-70355b55d5c8"},{"id":"occ_64fcb3cb62a7f5de722ef196","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_0642a48c-1b7d-49bf-832e-cd704471de34","section_id":"sec_28fea2c9-9a14-427f-a012-b845145f6909","layer":"body","character_id":null,"count":5,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"prefill","quote":"- **prefill** = 入力文を一気に読んで **KV cache を作る前半戦**\n- **ITL** = 出力中の **1トークンごとの待ち時間**\n- **PagedAttention** = 増え続ける *","quote_start":0,"quote_end":111,"text_sha256":"a595c46143b4ee651cbd60ed4886ec12418a948d46c879ce5e553772c3cf30af","block_sha256":"a595c46143b4ee651cbd60ed4886ec12418a948d46c879ce5e553772c3cf30af","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_0642a48c-1b7d-49bf-832e-cd704471de34"},{"id":"occ_de70e8246393611d735d0198","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_078e07b0-21b2-4ce8-bc5b-60e537ba1189","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":7,"end":15,"exact":"KV cache","quote":"```\n通常のKV cache\n[重い]\n[重い]\n[重い]\n[重い]\n\nFP8 KV cache\n[軽い]\n[軽い]\n[軽い]\n[軽い]\n```","quote_start":0,"quote_end":73,"text_sha256":"227aa5ffc6b5163b8089b5bdd11a89fc761a83fb890ba5539348f393f5d6efe3","block_sha256":"227aa5ffc6b5163b8089b5bdd11a89fc761a83fb890ba5539348f393f5d6efe3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_078e07b0-21b2-4ce8-bc5b-60e537ba1189"},{"id":"occ_6195b946cddd394a7c9e8b5f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_0954d7f8-146c-493a-9645-8545364959ef","section_id":"sec_3b51abe6-0b27-4425-8551-dea67c06ab4d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"## \\6. 学習と推論を一枚で重ねると","quote_start":0,"quote_end":20,"text_sha256":"10e93448eaddab60d69df5fec4248a3496feb9719d43f045f938088da54497ab","block_sha256":"10e93448eaddab60d69df5fec4248a3496feb9719d43f045f938088da54497ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_0954d7f8-146c-493a-9645-8545364959ef"},{"id":"occ_ac7393ea7fa845c41f2780ac","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_0ed9a61d-3062-49f0-abf8-bb5cdb197c6d","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":78,"end":85,"exact":"prefill","quote":"**、**ITL は「話し始めてからの滑らかさ」**、TPS は「サーバ全体の仕事量」です。  \nそして、**prefill は大きな計算をまとめて回しやすいので compute-bound 寄り、decode は増え続ける KV cache を読み続けるので memory-bound 寄り**、というのが定番の見方です。","quote_start":23,"quote_end":185,"text_sha256":"1630fe55ba6904e36da799f14f62a465faf6bd4daa36e22f8198a6a768789cf2","block_sha256":"1630fe55ba6904e36da799f14f62a465faf6bd4daa36e22f8198a6a768789cf2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_0ed9a61d-3062-49f0-abf8-bb5cdb197c6d"},{"id":"occ_1f6508955a610e37a4a6d46e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_0f8076be-2ce3-44dc-84b2-546a1027cf62","section_id":"sec_de4fb33b-0f5c-4c32-8abe-aabde45ca240","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":67,"end":69,"exact":"推論","quote":"は今後も最重要です。RubinがHBM4で帯域2.8倍級（8→22TB/s）を公表したように、長文脈・MoE・推論経済性では帯域が直撃します。ただし、KV cacheは「容量も帯域も食う上に動的で断片化しやすい」という性質を持つため、HBM拡張だけでは十分でありません。PagedAttentionのようなソフト管","quote_start":12,"quote_end":169,"text_sha256":"6a977c5a47c824227519bf71a266860d4579a5ff297955f6817dd08d430ba89b","block_sha256":"6a977c5a47c824227519bf71a266860d4579a5ff297955f6817dd08d430ba89b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_0f8076be-2ce3-44dc-84b2-546a1027cf62"},{"id":"occ_56fa79b8e21f62f2c6f5cfa4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_0fa7a211-5e70-4ed5-b5d9-610e2407c698","section_id":"sec_49f9d313-6d91-41a3-a73e-91edd278bb12","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"Rubinは、公式のNVL72ページと技術ブログで、単体性能（例：Rubin GPUでNVFP4推論50PFLOPS、学習35PFLOPS）を示しつつも、より前面に**コスト/トークン**や**MoE学習の必要GPU数**といった経済性指標を置いています。これは、推論が「長文脈＋対話＋多段推論（ag","quote_start":0,"quote_end":150,"text_sha256":"2ba43ba708828b799d908eed7008ce313c2cfdf11ca9130e03afd357778127c4","block_sha256":"2ba43ba708828b799d908eed7008ce313c2cfdf11ca9130e03afd357778127c4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_0fa7a211-5e70-4ed5-b5d9-610e2407c698"},{"id":"occ_13a5ed2af24ad531f82fd293","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_10484ff0-d9e9-4420-80fc-aa7d30d21e07","section_id":"sec_a06a5f2d-3519-415c-b895-a0b56001c9b6","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":135,"end":142,"exact":"prefill","quote":"「システム全体が1秒あたりに出した総トークン数」と定義しています。さらに TTFT には通常、**キュー待ち・prefill・ネットワーク遅延** が含まれ、ITL は decode 部分だけを見るために **最初の1トークンを除いて**計算されます。 ([NVIDIA Docs](https://docs.nvidia.","quote_start":80,"quote_end":242,"text_sha256":"e012212a976585fd60bbb31baf842757e4d1801583487eb560f9bf6967b4cc98","block_sha256":"e012212a976585fd60bbb31baf842757e4d1801583487eb560f9bf6967b4cc98","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_10484ff0-d9e9-4420-80fc-aa7d30d21e07"},{"id":"occ_b28b9624b83cb52c58f890ba","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_10e5ae31-a1e0-494f-898f-7a7a76ef5a6a","section_id":"sec_d5c475ce-74da-4a9f-b52e-73170b5458c1","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":37,"end":43,"exact":"decode","quote":"すると、3往復目の返答生成では、**最初からかなり大きい履歴を読みながら decode する**ことになります。vLLM は、マルチラウンド会話では過去チャット履歴の KV cache を再利用できるので、**将来のラウンドで latency を下げられる**と説明しています。ただしこ","quote_start":0,"quote_end":143,"text_sha256":"4ea548e25aafd7beaaa974411ad966fd9dfa4bc2f9151c7d20e2635dd4bb6ff6","block_sha256":"4ea548e25aafd7beaaa974411ad966fd9dfa4bc2f9151c7d20e2635dd4bb6ff6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_10e5ae31-a1e0-494f-898f-7a7a76ef5a6a"},{"id":"occ_ce4b8e9ce36626e1ae83859e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1152da3e-385d-4fb2-9a64-3011c3713454","section_id":"sec_d0d71363-7341-4fdf-b004-4b64a7694513","layer":"body","character_id":null,"count":5,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"prefill","quote":"- **prefill**  \n  入力文を読んで、返答の準備をする段階。**最初の返答の遅さ**に効く。 ([NVIDIA Docs](https://docs.nvidia.com/dynamo/dev/resourc","quote_start":0,"quote_end":111,"text_sha256":"619c2c4e1b2f873d26290046cdd5eccae52451ef3dd3e133baee96a9144ecaa6","block_sha256":"619c2c4e1b2f873d26290046cdd5eccae52451ef3dd3e133baee96a9144ecaa6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_1152da3e-385d-4fb2-9a64-3011c3713454"},{"id":"occ_2fa42bd2021b0611fca1c3d4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_116261c6-0f4d-4f03-913e-aa8446e0e352","section_id":"sec_4901810f-ce1a-4bdb-9336-2c20928c23ed","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":19,"end":27,"exact":"KV cache","quote":"```\n1トークンずつ返す段階\n\n前のKV cacheを読む\n      ↓\n今回の1トークンを計算\n      ↓\n新しいK/Vをcacheへ追記\n      ↓\n次の1トークンへ\n      ↓\nまたKV cacheを読む\n```","quote_start":0,"quote_end":117,"text_sha256":"03a1497b7b75b9f95bfdcdb08e307e2d666902cc98bd7e150080c3827f8a4f4b","block_sha256":"03a1497b7b75b9f95bfdcdb08e307e2d666902cc98bd7e150080c3827f8a4f4b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_116261c6-0f4d-4f03-913e-aa8446e0e352"},{"id":"occ_b7fd98e3ea43b8ecef8cb9b7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1390477f-0e16-4ff8-8bbe-6014d66c4ea1","section_id":"sec_2d061dfb-a4aa-4643-a2cc-4e1897124ae9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":103,"end":105,"exact":"推論","quote":"分散学習では勾配の all-reduce、モデル並列や MoE では GPU 間の活性値やトークンのやり取り、推論サーバでも KV キャッシュや中間結果の受け渡しが発生します。しかも H100 SXM でも、NVLink は GPUメモリ帯域の約 **3.7分の1**、PCIe Gen5 は約 **26分の1**","quote_start":48,"quote_end":205,"text_sha256":"48b735a44d4e0e3094d724cd2170a3e8ba5b0f43df195fd0b78feb01a257ec17","block_sha256":"48b735a44d4e0e3094d724cd2170a3e8ba5b0f43df195fd0b78feb01a257ec17","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_1390477f-0e16-4ff8-8bbe-6014d66c4ea1"},{"id":"occ_e80b1fa9cf6868fd13ba43ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_156b7a0a-7781-4c14-b88d-c7b08b643653","section_id":"sec_4901810f-ce1a-4bdb-9336-2c20928c23ed","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":9,"end":15,"exact":"Decode","quote":"### 4-2. 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**decode** に分けて考えると見えやすいです。NVIDIA NIM のベンチ資料でも、**長い入力列は TTFT を増やし、長い出力列は generati","quote_start":0,"quote_end":105,"text_sha256":"66b9c9b2e53aaf210cc82f492ea36b30f77e68a902c1b2a7ec8435001216e28e","block_sha256":"66b9c9b2e53aaf210cc82f492ea36b30f77e68a902c1b2a7ec8435001216e28e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_15931e0b-7118-4c38-96a3-5dac3efb0a3a"},{"id":"occ_e4cf2fb8cd3a2cc5ac929dfe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1664d260-34a7-408e-9c20-4cee9942d191","section_id":"sec_0a8cf111-965a-43ae-93c0-9991072df106","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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も、長い入力は prefill のメモリ要求を増やして TTFT を押し上げ、長い出力は generation のメモリ要求を増やして ITL を押し上げると説明しています。 ([vLLM](https://docs.vllm.a","quote_start":6,"quote_end":168,"text_sha256":"defc6751d6f12c10678f721ca052adb2e64187746ebd74ba6abb963e90d51cf1","block_sha256":"defc6751d6f12c10678f721ca052adb2e64187746ebd74ba6abb963e90d51cf1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_19111cf7-9335-4d73-b82d-20cf96c5e021"},{"id":"occ_9432aa9a422456cd17439eb2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1a53a3e5-49cf-47da-acd5-52855a6aa751","section_id":"sec_5d8a58ae-53a9-495c-b88b-15cfc651e808","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":74,"end":81,"exact":"prefill","quote":"_num\\_batched\\_tokens を大きくすると TTFT が良くなりやすい**、つまりより多くの prefill トークンを一度にバッチできると説明しています。 ([vLLM](https://docs.vllm.ai/en/stable/configuration/optimization/))","quote_start":19,"quote_end":174,"text_sha256":"0736c64d6494eee89abb99ff3331ca2a65b47e58ddcae6fed6412fd49ac7021f","block_sha256":"0736c64d6494eee89abb99ff3331ca2a65b47e58ddcae6fed6412fd49ac7021f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_1a53a3e5-49cf-47da-acd5-52855a6aa751"},{"id":"occ_69f41fc5ec0d22fe607b4c35","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1d50002c-4ab7-41df-9af8-4f0b06a4b136","section_id":"sec_ae441d93-02e2-4de4-9342-68c94664f2cd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"AWSの定義では、jitter 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Tens","quote_start":0,"quote_end":143,"text_sha256":"58091053b2adf1faf355fde44f3fcbb80de95b7af545d17082250684e6469e49","block_sha256":"58091053b2adf1faf355fde44f3fcbb80de95b7af545d17082250684e6469e49","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_1d50002c-4ab7-41df-9af8-4f0b06a4b136"},{"id":"occ_8d53c66459f5253f429b5a08","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_1e0bdadc-ac5b-462f-92d2-bc70d0db36f8","section_id":"sec_6da6773d-a2dc-48cd-9e5a-5f12a68ef37a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":186,"end":188,"exact":"推論","quote":"ムがpost-training（後学習）も対象として強調するのは、コア顧客のワークロードが事前学習から後学習＋推論運用へ比率移動していることを示唆します。","quote_start":131,"quote_end":208,"text_sha256":"233ba5c5cc85d889acaa07c0a5eb7c48bfcdc096b39b9bf617454a1ae156fce9","block_sha256":"233ba5c5cc85d889acaa07c0a5eb7c48bfcdc096b39b9bf617454a1ae156fce9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_1e0bdadc-ac5b-462f-92d2-bc70d0db36f8"},{"id":"occ_de8575b3577d167aea4c8dc5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_2028e705-fc37-435a-85e6-eaf42b82ebdd","section_id":"sec_32063747-1e88-45ef-a3d2-d800210f8413","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":18,"end":25,"exact":"prefill","quote":"これらは、単一リクエストが「(1) prefill → (2) decode → (3) ツール → (4) 追加文脈 → (5) decode…」を繰り返し、**レイテンシがステップ数に比例して累積**しやすいのが特徴です。NVIDIAのGroq ","quote_start":0,"quote_end":125,"text_sha256":"a30079266ebbb0b6f6b71efab6d07cd86da0931509ab7422acd4e2c786837b2e","block_sha256":"a30079266ebbb0b6f6b71efab6d07cd86da0931509ab7422acd4e2c786837b2e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_2028e705-fc37-435a-85e6-eaf42b82ebdd"},{"id":"occ_737fe3950ec6b633670d3720","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_238e892a-aa98-4f46-b821-13519d31e597","section_id":"sec_5d8a58ae-53a9-495c-b88b-15cfc651e808","layer":"body","character_id":null,"count":5,"matched_aliases":["KV 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I","quote_start":0,"quote_end":122,"text_sha256":"c4f24e817a026c760a1d70d684a3b3b41e33e0b66ad83d7c1cacb0d6d26b669f","block_sha256":"c4f24e817a026c760a1d70d684a3b3b41e33e0b66ad83d7c1cacb0d6d26b669f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_33655888-0fb1-4ec7-9ff0-ae1db7600706"},{"id":"occ_3b7278d20b42ee59b9bbc094","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_33b13fdc-0e37-4ad6-8084-05934503fa4a","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":48,"end":56,"exact":"KV cache","quote":"もちろん万能ではなく、量子化なので**精度とのトレードオフ**があります。vLLM の FP8 KV cache 文書では、量子化スケールの決め方として、デフォルト値、ランダムトークンによる推定、データセットを使った校正などが説明されています。つまり「ただ小さくすればよい」ではなく、**どの程度の精度劣化でどれ","quote_start":0,"quote_end":156,"text_sha256":"7923bee51e32a2d84a72581d83c27b3847b86a1ca100a85b4baac050097ed274","block_sha256":"7923bee51e32a2d84a72581d83c27b3847b86a1ca100a85b4baac050097ed274","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_33b13fdc-0e37-4ad6-8084-05934503fa4a"},{"id":"occ_6a58ead65350b9d5ef9f7c58","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_3c3fecab-d511-4b4b-bcf1-ab9f2a43d63b","section_id":"sec_788a1c87-b25f-4fbe-bc03-93c4ac198054","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":16,"end":23,"exact":"prefill","quote":"vLLM の最適化説明では、**prefill は compute-bound、decode は memory-bound** と対比されています。chunked prefill は、この両者をうまく混ぜて GPU 利用率とレイテンシを改善するた","quote_start":0,"quote_end":123,"text_sha256":"ce2ec7753b7fad6305a2aeebb0915bf144e63ef2520722f42243db062b67778f","block_sha256":"ce2ec7753b7fad6305a2aeebb0915bf144e63ef2520722f42243db062b67778f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_3c3fecab-d511-4b4b-bcf1-ab9f2a43d63b"},{"id":"occ_ed4fd210f5006f3df940d3da","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_3dbd44b6-8db6-4f00-b3d5-bf4bd9b2c01d","section_id":"sec_28fea2c9-9a14-427f-a012-b845145f6909","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"はい。これらは全部、\\*\\*LLM推論の「どこに時間とメモリが使われているか」\\*\\*を説明するための用語です。  \nまず一言で並べると、","quote_start":0,"quote_end":69,"text_sha256":"5783dc7e5d5f07541f3a56e4a55b3f79ee4338e99eeb0cd5414ae84127513b0e","block_sha256":"5783dc7e5d5f07541f3a56e4a55b3f79ee4338e99eeb0cd5414ae84127513b0e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_3dbd44b6-8db6-4f00-b3d5-bf4bd9b2c01d"},{"id":"occ_41c8fa067a2a462aab6fc467","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_3e43bf2d-5c5f-4f9a-a862-73106ff0ccb6","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":2,"end":10,"exact":"KV cache","quote":"- KV cache がメモリを圧迫する\n- だから軽くしたい\n- 軽くすると長文や高同時接続に強くなりやすい","quote_start":0,"quote_end":55,"text_sha256":"211b3ed21534e72d3bee7076c062518ea7f7e341ea8c1af0c4c355c8e9677a99","block_sha256":"211b3ed21534e72d3bee7076c062518ea7f7e341ea8c1af0c4c355c8e9677a99","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_3e43bf2d-5c5f-4f9a-a862-73106ff0ccb6"},{"id":"occ_72d89652dcc9f2f96c0ec199","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_4780b850-25cc-4599-afaa-995d597885da","section_id":"sec_3b51abe6-0b27-4425-8551-dea67c06ab4d","layer":"code","character_id":null,"count":3,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":118,"end":120,"exact":"推論","quote":"                       ↑\n                    通信・同期が重い\n\n推論\n[prefillでKV作成] -> [decodeでKVを毎回読む/足す] -> [1 token出力]\n                          ↑\n                 帯","quote_start":63,"quote_end":220,"text_sha256":"966afae7fe78cdda6f5b386b7ecd30d696bd43bcaaab3c3e26b3575bd1d68050","block_sha256":"966afae7fe78cdda6f5b386b7ecd30d696bd43bcaaab3c3e26b3575bd1d68050","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_4780b850-25cc-4599-afaa-995d597885da"},{"id":"occ_0ad10dfec08195419bc2c00d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_47ac20d2-3698-4b4d-9504-3c663ad74be2","section_id":"sec_b950962c-5326-403d-8a60-33ed3c81fd75","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":31,"end":39,"exact":"KV cache","quote":"同時に複数リクエストを処理すると、GPU は複数シーケンスの KV cache を抱えます。NVIDIA は、concurrency が高いほどシステム全体の TPS は増える一方、**レイテンシは悪化しやすい**と説明しています。出力が長いケースでは、各リクエストの KV c","quote_start":0,"quote_end":139,"text_sha256":"541bf233629e2b70a142c6125fc6ebe753a414827d9459f76fb470073500aa3d","block_sha256":"541bf233629e2b70a142c6125fc6ebe753a414827d9459f76fb470073500aa3d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_47ac20d2-3698-4b4d-9504-3c663ad74be2"},{"id":"occ_91130960022e199b2c9ce5a4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_47c52812-d768-465f-bae0-bff141f2fa2d","section_id":"sec_03e3214c-ecb2-4e76-be2b-1a35f2ce53c0","layer":"body","character_id":null,"count":2,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"decode","quote":"つまり decode は、**毎回の計算は小さめなのに、毎回読む履歴は少しずつ増える**構造です。だから長文になるほど ITL が悪化しやすく、vLLM も decode を memory-bound と扱っています。N","quote_start":0,"quote_end":110,"text_sha256":"987cb2b1abf6acdc4b7bee56e93dd355d1de55025a1fa1bce1736c76db3b9dee","block_sha256":"987cb2b1abf6acdc4b7bee56e93dd355d1de55025a1fa1bce1736c76db3b9dee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_47c52812-d768-465f-bae0-bff141f2fa2d"},{"id":"occ_41f6eee9dd66c46db77872bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_498a630f-09d6-424c-87dd-6ffc04962418","section_id":"sec_914b2a87-a792-4119-894e-0af29a1acb8b","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":32,"end":40,"exact":"KV cache","quote":"なぜ必要かというと、LLM は出力を1トークンずつ伸ばすたびに KV cache も増えていくからです。","quote_start":0,"quote_end":52,"text_sha256":"93fdaed326e53b99b8e889e6fc378eb7ad71ab7312ea76c0f75f79b4acee4060","block_sha256":"93fdaed326e53b99b8e889e6fc378eb7ad71ab7312ea76c0f75f79b4acee4060","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_498a630f-09d6-424c-87dd-6ffc04962418"},{"id":"occ_627620313de650662777d7e1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_4fa1bc1a-0089-4ef0-a42f-35194f373346","section_id":"sec_cf2db1c1-6854-4415-bc6b-f69a13c6dd26","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"重要なのは、RLHF/RL系は「環境ロールアウト＝大量推論」を伴い、(a) 低TTFT、(b) 低TPOT（time per output token）、(c) 高スループットの両立が難しくなる点です。Rubin PODがVera CPUラックで多数のRL/","quote_start":0,"quote_end":129,"text_sha256":"3e4e0527fea4c0767a81a99f421f217544ec8524d1e67723914ca1fec7ebf765","block_sha256":"3e4e0527fea4c0767a81a99f421f217544ec8524d1e67723914ca1fec7ebf765","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_4fa1bc1a-0089-4ef0-a42f-35194f373346"},{"id":"occ_a20a5e2797fb8df97df522d6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5048ef72-b9ad-40d0-9140-1982731cb30d","section_id":"sec_32063747-1e88-45ef-a3d2-d800210f8413","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"さらに長文脈化が進むと、推論はKV cache（過去トークンのKey/Value）に支配されます。PagedAttention/vLLMはKV cacheをOSのページングに似せて管理し、断片化を抑え、同一レイテンシでスループッ","quote_start":0,"quote_end":114,"text_sha256":"de02e39feb0a09c410033331300ec3587a5ffc390a98cb02d89512cf10f3f0c1","block_sha256":"de02e39feb0a09c410033331300ec3587a5ffc390a98cb02d89512cf10f3f0c1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5048ef72-b9ad-40d0-9140-1982731cb30d"},{"id":"occ_3328600e3379139701ceb8e5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_50c7afb1-ce5a-4ff6-87b2-6868caa2f4e4","section_id":"sec_409e6cb8-dd7b-4ea8-9210-ad8f1d0a1609","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":11,"end":19,"exact":"KV cache","quote":"```\n何もしない:\nKV cache がどんどん太っていく\n↓\nメモリ容量・帯域が苦しい\n↓\nITLが悪化しやすい\n\n改善策:\n- cacheを上手く分割管理する\n- cacheを圧縮する\n- 共有prefixは再利用する\n```","quote_start":0,"quote_end":116,"text_sha256":"870a4fbf0e85d5621987e7b84dd31e96e8726de7ece08d8c1c2656d965bf2c6b","block_sha256":"870a4fbf0e85d5621987e7b84dd31e96e8726de7ece08d8c1c2656d965bf2c6b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_50c7afb1-ce5a-4ff6-87b2-6868caa2f4e4"},{"id":"occ_c37f869f58edbaf568253407","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5330200e-48a7-4547-b688-ba8c2b7b7ef4","section_id":"sec_07887d78-c99e-4994-a3a1-bc0a877a40b1","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":16,"end":18,"exact":"推論","quote":"### 学習ではなぜ通信が重く、推論ではなぜジッタとKVキャッシュが効くのか？","quote_start":0,"quote_end":39,"text_sha256":"f811ea5daa5b0dd865fb019a260f8b3bbb40f4b7715c86e03b98d9024cb0b12a","block_sha256":"f811ea5daa5b0dd865fb019a260f8b3bbb40f4b7715c86e03b98d9024cb0b12a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5330200e-48a7-4547-b688-ba8c2b7b7ef4"},{"id":"occ_f4b70ad35faca205dcdbed86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5335afc9-0838-4ce7-9528-2a6027122941","section_id":"sec_c0489759-32ff-45c9-a437-7a06d2f49d8c","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":28,"end":35,"exact":"prefill","quote":"```\n入力:\n「N町の将来性ある仕事を3つ教えて」\n\nprefill:\n[入力を全部読む]\n      ↓\n[内部表現を作る]\n      ↓\n[KV cacheを作る]\n      ↓\n最初の1トークンを出せる状態になる\n```","quote_start":0,"quote_end":116,"text_sha256":"18de8b5af09c9403cf3f999a5b40cf5d7a59bb889f946cb5de60555300f8267e","block_sha256":"18de8b5af09c9403cf3f999a5b40cf5d7a59bb889f946cb5de60555300f8267e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5335afc9-0838-4ce7-9528-2a6027122941"},{"id":"occ_798ccd22a72e458bcd14ac4a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_540617d4-edfc-4eb0-89dc-35c073539fe8","section_id":"sec_0289410f-2a3a-4d53-a244-efa6407f153a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"### スパース対応：推論では構造化スパースより適応圧縮/量子化＋ソフト協調が本命になりやすい","quote_start":0,"quote_end":47,"text_sha256":"3e63c7d48d1af17d9fe5f82c40b17791ff9d39776fa7d877ebaa4e6c970be0d0","block_sha256":"3e63c7d48d1af17d9fe5f82c40b17791ff9d39776fa7d877ebaa4e6c970be0d0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_540617d4-edfc-4eb0-89dc-35c073539fe8"},{"id":"occ_c35abdd62fe3ff65a2f86599","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_550259b6-6b6f-42cd-b2ec-81d97eb534df","section_id":"sec_4901810f-ce1a-4bdb-9336-2c20928c23ed","layer":"code","character_id":null,"count":2,"matched_aliases":["decode","推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"```\n推論のdecode\n\ntoken 1:\n  cache読む -> 少し計算 -> cache書く\n\ntoken 2:\n  もっと大きいcache読む -> 少し計算 -> cache書く\n\ntoken 3","quote_start":0,"quote_end":106,"text_sha256":"c94384573f0ab7fc8fcf5aab46fed96f3493a07ad6467891b661ded6156ab612","block_sha256":"c94384573f0ab7fc8fcf5aab46fed96f3493a07ad6467891b661ded6156ab612","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_550259b6-6b6f-42cd-b2ec-81d97eb534df"},{"id":"occ_b604dd5e0087d065f8fcf06d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5770caab-ca9f-4f52-b1c7-95250946b2e1","section_id":"sec_ba224cfe-ee8e-40a2-8872-b1795a0d8975","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":216,"end":218,"exact":"推論","quote":"デルサイズとトークン数を同程度に増やすのが計算最適になり得る」ことを実験から示し、学習だけでなく下流の微調整や推論の計算量にも波及すると論じています。","quote_start":161,"quote_end":236,"text_sha256":"20ad4d689bb18949172f1ef3d943d46bc2201d486c03cd93caad805a8cc7029d","block_sha256":"20ad4d689bb18949172f1ef3d943d46bc2201d486c03cd93caad805a8cc7029d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5770caab-ca9f-4f52-b1c7-95250946b2e1"},{"id":"occ_5f8c759045d1681144105906","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_58d991b3-21e4-49dc-b619-337782e3942a","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"code","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":17,"end":24,"exact":"prefill","quote":"```\nTTFTを良くしたい\n→ prefillを速くしたい\n→ compute-bound側の効率が重要\n\nITLを良くしたい\n→ decodeを滑らかにしたい\n→ memory-bound側の効率が重要\n\nTPSを良くしたい\n→ 全体の詰まりを","quote_start":0,"quote_end":124,"text_sha256":"49f9b5dd047af790a3e4cd7d23b601a82ff6526e53ba5a673430e35cf0b0e660","block_sha256":"49f9b5dd047af790a3e4cd7d23b601a82ff6526e53ba5a673430e35cf0b0e660","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_58d991b3-21e4-49dc-b619-337782e3942a"},{"id":"occ_00a4cca3c76742e4221098f2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5b8056b3-638e-4307-acd0-ca582b396cc4","section_id":"sec_3f5d34cd-20f0-40e9-a069-3e355f5474b5","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"Groq 3 LPXは「推論市場（特にエージェント）の価値軸＝低TTFT・低テイル・高tokens/s per user」を前面に出し、Rubinとの組み合わせで推論パレート境界を押し広げる戦略です。 一方で、SRAM容量の小さ","quote_start":0,"quote_end":114,"text_sha256":"5fca06425dd4c78f1693c9fcc9f8bef369e72c5ede9ca7720d0bd0d8e90a9950","block_sha256":"5fca06425dd4c78f1693c9fcc9f8bef369e72c5ede9ca7720d0bd0d8e90a9950","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5b8056b3-638e-4307-acd0-ca582b396cc4"},{"id":"occ_2eef25582935da238ec25f3c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_5ec170b1-125f-4fca-b20c-ea570f38dd57","section_id":"sec_e0b291ab-9e23-4c35-87ca-eb2f861e95b7","layer":"code","character_id":null,"count":6,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":17,"end":24,"exact":"prefill","quote":"```\nユーザー入力\n   ↓\n[prefill]\n   - 入力を全部読む\n   - KV cacheを作る\n   - 長い入力だとTTFTが重くなる\n   ↓\n[decode]\n   - 1トークンずつ出す\n   - そのたびにKV cache","quote_start":0,"quote_end":124,"text_sha256":"26f4e9ee369eb5e5a27d0717db3a528fcc70208186e15b99fae598e5a6967159","block_sha256":"26f4e9ee369eb5e5a27d0717db3a528fcc70208186e15b99fae598e5a6967159","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_5ec170b1-125f-4fca-b20c-ea570f38dd57"},{"id":"occ_2ee7f5e4e46185870664fe3a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_61c937d7-37ff-4dd1-af05-fdbfaa1307a4","section_id":"sec_b3572938-d67b-4605-b557-315055a6bcd5","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"prefill","quote":"prefill は「最初の返答まで」の重さに効きます。入力が長いほど、KV cache の形成と前段のメモリ要求が増え、**TTFT** が伸びやすくなります。 ([NVIDIA Docs](https://doc","quote_start":0,"quote_end":107,"text_sha256":"e5ec3d680d006d1754b49f69ff9e793a00ae1d33b8a8afabbea2f8dcd9f5fd04","block_sha256":"e5ec3d680d006d1754b49f69ff9e793a00ae1d33b8a8afabbea2f8dcd9f5fd04","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_61c937d7-37ff-4dd1-af05-fdbfaa1307a4"},{"id":"occ_3b22c60252ee485422ae116d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_627b554b-8632-4147-b7a6-926dbadb4213","section_id":"sec_03e3214c-ecb2-4e76-be2b-1a35f2ce53c0","layer":"body","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":8,"end":14,"exact":"decode","quote":"### \\2. decode が memory-bound になりやすい理由","quote_start":0,"quote_end":38,"text_sha256":"a576b9ee7c18072df6114d71f24f5a1b2c2f87439de8cf7067087b07744a0ece","block_sha256":"a576b9ee7c18072df6114d71f24f5a1b2c2f87439de8cf7067087b07744a0ece","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_627b554b-8632-4147-b7a6-926dbadb4213"},{"id":"occ_e25b7a8e3391198567ad7bef","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_656f62cb-83d7-4d38-87fc-7ee07fad58ce","section_id":"sec_7bf0e965-fbe8-4d37-a4d6-02d7779ebdfa","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":81,"end":89,"exact":"KV cache","quote":"][ある][仕事][を][3つ][教えて][まず]\n   10個ぶんの過去を見る\n\n出力:\n[、]\n\n出力後のKV cache:\n[1][2][3][4][5][6][7][8][9][10][11]\n```","quote_start":26,"quote_end":130,"text_sha256":"52c6c2a25ecefb536e4de9b694a188856b8856433e69e6706f1b64b97c6e6cb5","block_sha256":"52c6c2a25ecefb536e4de9b694a188856b8856433e69e6706f1b64b97c6e6cb5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_656f62cb-83d7-4d38-87fc-7ee07fad58ce"},{"id":"occ_6ff6499633d5aa3ec89c262c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_69980066-6cb8-4ceb-a8a4-c4e05a6ee692","section_id":"sec_2d061dfb-a4aa-4643-a2cc-4e1897124ae9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":185,"end":187,"exact":"推論","quote":"NVIDIA自身が「高帯域・低遅延」を目標に最適化していると説明しています。裏を返すと、分散学習やマルチGPU推論では、この通信が性能の中心課題になりやすいということです。 ([NVIDIA Developer](https://developer.nvidia.com/nccl))","quote_start":130,"quote_end":272,"text_sha256":"767fee72ce4ab54cc6b8e15052d71c6b35dc9a7508b2e23286fc00680c845057","block_sha256":"767fee72ce4ab54cc6b8e15052d71c6b35dc9a7508b2e23286fc00680c845057","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_69980066-6cb8-4ceb-a8a4-c4e05a6ee692"},{"id":"occ_64afaa2597656c752e7a4247","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_6aec73eb-77f5-459f-b851-350f68720a69","section_id":"sec_b3572938-d67b-4605-b557-315055a6bcd5","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":57,"end":65,"exact":"KV cache","quote":"`\n長いプロンプトを一気に読む段階\n\n[入力トークン列]\n      ↓\n[まとめて計算]\n      ↓\n[KV cacheを作る]\n      ↓\n[最初の1トークン]\n```","quote_start":2,"quote_end":92,"text_sha256":"61d81b9f140b4e84c89463bc68259e3cb3350e5316f759fee4770c413c01fe62","block_sha256":"61d81b9f140b4e84c89463bc68259e3cb3350e5316f759fee4770c413c01fe62","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_6aec73eb-77f5-459f-b851-350f68720a69"},{"id":"occ_34e274e337917b5104dc49b1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_6b2a0ecd-1f9a-48c1-b921-e32f185aed38","section_id":"sec_8640eb7f-0b02-4fa9-97f2-6efdcd383f06","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"ellは、公式のGB200 NVL72ページやデータシートで、**H100比30倍のリアルタイム兆パラメータ級推論**、**学習4倍**などの主張を掲げています（いずれも「Projected」表記と条件明記あり）。条件の重要点として、推論比較はTTL（token-to-token）=50ms、FTL（first ","quote_start":6,"quote_end":163,"text_sha256":"b405310e82dabb0ca79a90c1814fc94027db5701f76f49cb55c9b6d2bd4d37b2","block_sha256":"b405310e82dabb0ca79a90c1814fc94027db5701f76f49cb55c9b6d2bd4d37b2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_6b2a0ecd-1f9a-48c1-b921-e32f185aed38"},{"id":"occ_0fdddc8241e238187077092d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_6d7b1cb1-7260-4e14-9e9c-c70f5eb9e8fb","section_id":"sec_28fea2c9-9a14-427f-a012-b845145f6909","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":12,"end":19,"exact":"prefill","quote":"です。NVIDIA は prefill を「入力プロンプトを処理して KV cache を生成する最初の段階」と定義し、ITL を「連続するトークン間の平均時間」と定義しています。vLLM は PagedAttention を、atten","quote_start":0,"quote_end":119,"text_sha256":"571f7576011953d7c051e3b0f0f5e5553e47a90b8ea90166000b82070d5ef346","block_sha256":"571f7576011953d7c051e3b0f0f5e5553e47a90b8ea90166000b82070d5ef346","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_6d7b1cb1-7260-4e14-9e9c-c70f5eb9e8fb"},{"id":"occ_827f6628ef7de16c2f4c9ac6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_700bb272-7571-410e-a739-eb52c4f137c1","section_id":"sec_e0b291ab-9e23-4c35-87ca-eb2f861e95b7","layer":"code","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"prefill","quote":"```\nprefill ＝ compute-bound寄り\ndecode ＝ memory-bound寄り\n```","quote_start":0,"quote_end":57,"text_sha256":"cf57820536dafbaf99c751e0eb20f7ca62d912cccf57bde89f898dfa0c53464c","block_sha256":"cf57820536dafbaf99c751e0eb20f7ca62d912cccf57bde89f898dfa0c53464c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_700bb272-7571-410e-a739-eb52c4f137c1"},{"id":"occ_78a68009d7dc2f88eeaea5ac","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_7286b44c-4137-4d2f-97fc-5f11d7373e7d","section_id":"sec_280b38d3-5262-4ad8-b96f-a2156984cc0d","layer":"code","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"decode","quote":"```\ndecodeの各ステップ\n\nstep 1:\n[過去9]  -> 1 token計算 -> cache 10\n\nstep 2:\n[過去10] -> 1 token計算 -> cache 11\n\nstep 3:\n[過","quote_start":0,"quote_end":110,"text_sha256":"bea9cf304e8671f6208da2194ab43f3e8aef422d625b28bce02dd9be8ee368d6","block_sha256":"bea9cf304e8671f6208da2194ab43f3e8aef422d625b28bce02dd9be8ee368d6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_7286b44c-4137-4d2f-97fc-5f11d7373e7d"},{"id":"occ_57e0475127caec8fb1ffc3da","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_78887c4c-df49-4b87-8c4e-fba3ac576719","section_id":"sec_03e3214c-ecb2-4e76-be2b-1a35f2ce53c0","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode"],"evidence":{"text_basis":"markdown","start":28,"end":36,"exact":"KV cache","quote":"NVIDIA は ITL の説明で、**長い出力列では KV cache が成長してメモリコストが増える**こと、さらに **各新トークンの attention コストは「これまでの入力＋出力長」に線形に増える**ことを明記しています。そのうえで、この計算は **一般に ","quote_start":0,"quote_end":136,"text_sha256":"52fc11ce7a12c2c7c9bdcdb5b3602775560ede3655f2966a61e63e61c630fa59","block_sha256":"52fc11ce7a12c2c7c9bdcdb5b3602775560ede3655f2966a61e63e61c630fa59","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_78887c4c-df49-4b87-8c4e-fba3ac576719"},{"id":"occ_42e77e8e4cb392d360d83e83","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_78f2cc3d-6326-4522-bd54-e98f4f8ca38d","section_id":"sec_280b38d3-5262-4ad8-b96f-a2156984cc0d","layer":"body","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":52,"end":58,"exact":"decode","quote":"ここでのポイントは「毎回1トークンしか出していないのに、毎回読むべき履歴は増えていく」ことです。だから decode は、派手な行列演算の“馬力勝負”というより、**大きくなり続ける過去を何度も読み返すメモリ帯域勝負**になりやすいです。NVIDIA も、この部分は一般に compute-bound ではないと書い","quote_start":0,"quote_end":158,"text_sha256":"ec5acddd238ae67af41961e3c93c7b4f799b59de915a0b77962126b078ef5341","block_sha256":"ec5acddd238ae67af41961e3c93c7b4f799b59de915a0b77962126b078ef5341","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_78f2cc3d-6326-4522-bd54-e98f4f8ca38d"},{"id":"occ_87f046d015b5219c62d5593a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_7a9977fc-3498-46e8-9326-fce5de3eb7c1","section_id":"sec_4901810f-ce1a-4bdb-9336-2c20928c23ed","layer":"body","character_id":null,"count":5,"matched_aliases":["KV cache","decode","推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"TensorRT では、LLM推論で KV cache を使うための IKVCacheUpdateLayer があり、cache テンソルの形 \\[B, N, S\\_max, H\\] を前提に、更新を書き込んでいく仕組みが用意されてい","quote_start":0,"quote_end":117,"text_sha256":"8b0c3ec32930cfffab7025a759d6c481316fc4d0cf59de4d6433e7e261a6bdb3","block_sha256":"8b0c3ec32930cfffab7025a759d6c481316fc4d0cf59de4d6433e7e261a6bdb3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_7a9977fc-3498-46e8-9326-fce5de3eb7c1"},{"id":"occ_b4d442b36ca965f3726a784a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_803b0add-4088-455f-85fc-3499f53d6cde","section_id":"sec_a06a5f2d-3519-415c-b895-a0b56001c9b6","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"prefill","quote":"ここから、なぜ **prefill は compute-bound、decode は memory-bound になりやすいか** につなげます。","quote_start":0,"quote_end":73,"text_sha256":"c69502b251c74a39a3f5c6f8414c9a3dfa58bc4a498b102f61b7f44e0f3fa5be","block_sha256":"c69502b251c74a39a3f5c6f8414c9a3dfa58bc4a498b102f61b7f44e0f3fa5be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_803b0add-4088-455f-85fc-3499f53d6cde"},{"id":"occ_3232a2cb7eacc05b510df1b2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_82a0d50b-1e1d-4f18-980e-675c022dc28d","section_id":"sec_d5c475ce-74da-4a9f-b52e-73170b5458c1","layer":"code","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":39,"end":46,"exact":"prefill","quote":"```\nprefix caching が助けるもの:\n- すでに読んだ履歴の再prefill\n\nprefix caching があまり助けないもの:\n- 今まさに長く生成している最中の decode\n```","quote_start":0,"quote_end":103,"text_sha256":"1fd745c9a5b3603a8fc7360eb6ed4847a1fe9dcf830cd55aee1452924228c7be","block_sha256":"1fd745c9a5b3603a8fc7360eb6ed4847a1fe9dcf830cd55aee1452924228c7be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_82a0d50b-1e1d-4f18-980e-675c022dc28d"},{"id":"occ_1f97ebb941d973ae991177c9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_85a5651a-477c-4746-9ceb-e8fad6fc4c00","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"body","character_id":null,"count":4,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":25,"end":32,"exact":"prefill","quote":"NVIDIA の説明では、TTFT はキュー待ち・prefill・ネットワーク遅延を含み、ITL は decode 部分だけを見る指標です。TPS は同時実行中の全リクエストを合算した総スループットです。vLLM では、decode を優先すると ITL が改善し","quote_start":0,"quote_end":132,"text_sha256":"edf7e3b60ec3692ba7a5fefd252f866e29848313e0ae7bf17d4d864a5c4402af","block_sha256":"edf7e3b60ec3692ba7a5fefd252f866e29848313e0ae7bf17d4d864a5c4402af","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_85a5651a-477c-4746-9ceb-e8fad6fc4c00"},{"id":"occ_ceb5c7f590defdd8345097cc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_879307c1-791e-4751-ac5f-3bf8a08a1816","section_id":"sec_653a6c6f-c662-4adf-bc9b-1b1802f3e19f","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":14,"end":22,"exact":"KV cache","quote":"エージェントはマルチターンでKV cacheを増やし、再利用（prefix caching等）も増える一方、コンテキストは“半一時データ”として膨張します。Rubin PODはこれを「推論コンテキストを共有データ型として扱う」方向で設計し、Bl","quote_start":0,"quote_end":122,"text_sha256":"dd048dc81271b8b1bf4cb070bde515bc91edaed51f7035516765e1b6f34f5315","block_sha256":"dd048dc81271b8b1bf4cb070bde515bc91edaed51f7035516765e1b6f34f5315","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_879307c1-791e-4751-ac5f-3bf8a08a1816"},{"id":"occ_8481f9e71a189b321ddc2252","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_887846ad-5c7f-4568-a0c1-d274127c329c","section_id":"sec_32063747-1e88-45ef-a3d2-d800210f8413","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"ReActは「推論（思考）と行動（ツール/環境操作）を交互に行う」枠組みを示し、エージェントの基本形になりました。citeturn0search19 またTree of Thoughtsは、複数の思考経路を探索・","quote_start":0,"quote_end":109,"text_sha256":"f28940dcbdf3bd95c322f06dc91daa31116e7cd492b6f2679676f58496d25d94","block_sha256":"f28940dcbdf3bd95c322f06dc91daa31116e7cd492b6f2679676f58496d25d94","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_887846ad-5c7f-4568-a0c1-d274127c329c"},{"id":"occ_3efe42d19711e8eed9db99da","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_8db08fba-416f-46a7-8565-f74c0b1fce08","section_id":"sec_0a8cf111-965a-43ae-93c0-9991072df106","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":47,"end":54,"exact":"prefill","quote":"この段階は主に **TTFT** 側に効きます。NVIDIA は、**長い入力列（ISL）は prefill のメモリ要求を増やし、TTFT を増やす**と説明しています。 ([NVIDIA Docs](https://docs.nvidia.com/nim/benchmarking/llm/latest/","quote_start":0,"quote_end":154,"text_sha256":"2ac14ab49617d72d05d240cf727e0f79794cd6acdc33aa9a8a6e283f15219dd2","block_sha256":"2ac14ab49617d72d05d240cf727e0f79794cd6acdc33aa9a8a6e283f15219dd2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_8db08fba-416f-46a7-8565-f74c0b1fce08"},{"id":"occ_68371b9c5ff880ca9243c439","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_8e94d8c3-4d2d-4e87-afe0-7bb72a6e03d5","section_id":"sec_de4fb33b-0f5c-4c32-8abe-aabde45ca240","layer":"body","character_id":null,"count":2,"matched_aliases":["prefill","推論"],"evidence":{"text_basis":"markdown","start":88,"end":90,"exact":"推論","quote":"SRAMバンク群）を設け、FlashAttention型のタイル処理と共鳴させる、(b) GPUメモリの外側に推論コンテキスト層を置く（NVIDIAのCMX/ICMSの思想を一般化）、(c) 可能ならCXL等でメモリプールを扱い、prefill/agenticで膨らむKVを安価な階層へ逃がす、という3層化です。","quote_start":33,"quote_end":189,"text_sha256":"fd15d026d2d923b7f3bd4ca45d2c2d395c67a60fc86660a01fce39ab4f17d9dd","block_sha256":"fd15d026d2d923b7f3bd4ca45d2c2d395c67a60fc86660a01fce39ab4f17d9dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_8e94d8c3-4d2d-4e87-afe0-7bb72a6e03d5"},{"id":"occ_271ec6da9d9e2d71d9ad190d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_9024a3d3-34e6-4797-a193-654418f8f319","section_id":"sec_03e3214c-ecb2-4e76-be2b-1a35f2ce53c0","layer":"code","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"Decode","quote":"```\nDecodeの各ステップ\n\nstep1: 過去 500 を読む → 1 token出す\nstep2: 過去 501 を読む → 1 token出す\nstep3: 過去 502 を読む → 1 token出す\nst","quote_start":0,"quote_end":110,"text_sha256":"e36483fa9477c24555686b7d94294c40ee0448a41513679c0a184d64ad6bc0cc","block_sha256":"e36483fa9477c24555686b7d94294c40ee0448a41513679c0a184d64ad6bc0cc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_9024a3d3-34e6-4797-a193-654418f8f319"},{"id":"occ_eab52a5bfc43e83c189a45d7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_94aba386-5536-4887-b7d1-421eb3a4aa62","section_id":"sec_03e3214c-ecb2-4e76-be2b-1a35f2ce53c0","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","KV cache"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"Decode","quote":"```\nDecode\n\n1トークン出すたびに\n[過去のKV cacheを読む]\n        ↓\n[今回の1トークンを計算]\n        ↓\n[新しいK/Vをcacheへ追加]\n        ↓\n次のトークンでは","quote_start":0,"quote_end":110,"text_sha256":"cfcabbc95593c34edc39ff5ed1ea796455e4baa67d4c32cd3f62f987142244ea","block_sha256":"cfcabbc95593c34edc39ff5ed1ea796455e4baa67d4c32cd3f62f987142244ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_94aba386-5536-4887-b7d1-421eb3a4aa62"},{"id":"occ_81143e9a9606c95851106908","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_95160faa-a448-43e9-ad49-75f85b65c279","section_id":"sec_0a8cf111-965a-43ae-93c0-9991072df106","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"prefill","quote":"```\n入力を読む（prefill）\n[N町][の][将来性][が][ある][仕事][を][3つ][教えて]\n\nKV cache:\n[1][2][3][4][5][6][7][8][9]\n```","quote_start":0,"quote_end":97,"text_sha256":"4608229b4a423885619b243a02250d3e294d6bbe49fc78f0825fbcc294785f6d","block_sha256":"4608229b4a423885619b243a02250d3e294d6bbe49fc78f0825fbcc294785f6d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_95160faa-a448-43e9-ad49-75f85b65c279"},{"id":"occ_d28dc8bfc312140942590e24","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_962e4818-108d-4841-a7c7-29b3a2ffcdfc","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"code","character_id":null,"count":4,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"prefill","quote":"```\nTTFT  ← 主に prefill 側の重さが効く\nITL   ← 主に decode 側の重さが効く\nTPS   ← prefill と decode を全部合わせた総合処理量\n```","quote_start":0,"quote_end":98,"text_sha256":"6a1441e978f28438322f3dc460f8a0ad188197fa0b7507640b3cf5693df6069d","block_sha256":"6a1441e978f28438322f3dc460f8a0ad188197fa0b7507640b3cf5693df6069d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_962e4818-108d-4841-a7c7-29b3a2ffcdfc"},{"id":"occ_7c9c55de43d9d2b3e5c548ee","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_9a04edd8-cb6b-44dc-a883-ab2ba0e10294","section_id":"sec_de4fb33b-0f5c-4c32-8abe-aabde45ca240","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":11,"end":19,"exact":"KV cache","quote":"Rubin PODが「KV cacheを高帯域ストレージ層へオフロードし、トークン/秒を最大5倍」と述べるのは、まさにKVが第一級データ型になったことの表明です。","quote_start":0,"quote_end":81,"text_sha256":"1a4e25460b56bbe955cb412f8ad3e605b5833decc7260294e16b3249be0d14c1","block_sha256":"1a4e25460b56bbe955cb412f8ad3e605b5833decc7260294e16b3249be0d14c1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_9a04edd8-cb6b-44dc-a883-ab2ba0e10294"},{"id":"occ_13a962c06d151acf5cbaae0d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_9c36dd55-2480-491c-8832-62f3cd5fe5ea","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"code","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":32,"end":39,"exact":"prefill","quote":"```\nユーザー体感と内部処理の対応\n\nリクエスト\n   ↓\n[prefill]\n- 入力をまとめて読む\n- KV cacheを作る\n- compute-bound寄り\n- 主にTTFTへ効く\n\n   ↓\n\n[decode]\n- 1トークンずつ出す\n- KV cacheを毎回読","quote_start":0,"quote_end":139,"text_sha256":"1013eb7be06e199214fc01275d2749a7b9d5adb2a200f745d741d6492fe337ad","block_sha256":"1013eb7be06e199214fc01275d2749a7b9d5adb2a200f745d741d6492fe337ad","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_9c36dd55-2480-491c-8832-62f3cd5fe5ea"},{"id":"occ_230088d644e414e0fc44a532","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_9ce6f91f-2dde-4529-85eb-d298a971777b","section_id":"sec_653a6c6f-c662-4adf-bc9b-1b1802f3e19f","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":24,"end":32,"exact":"KV cache","quote":"提言としては、(a) DPU/SmartNICでKV cacheのI/Oと暗号化/隔離をオフロード、(b) NVMe/次世代NVRAMをKV向けレイテンシ最適（小IO・高QPS・RDMA直結）にチューニング、(c) GPU側はKVの階層化APIを標準化し、vLLM","quote_start":0,"quote_end":132,"text_sha256":"cf6c61d76ead9b493beb5f238fb8588dce62a97666cabe98bfa87a14d386f3a8","block_sha256":"cf6c61d76ead9b493beb5f238fb8588dce62a97666cabe98bfa87a14d386f3a8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_9ce6f91f-2dde-4529-85eb-d298a971777b"},{"id":"occ_8f3c9683297dc652251821cc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a10ea2a4-98f7-416b-aa43-233c299087e2","section_id":"sec_b950962c-5326-403d-8a60-33ed3c81fd75","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"となります。だからサーバ推論では、**長文・高同時接続・大きいコンテキスト** が重なると ITL が崩れやすいです。NVIDIA は、ITL や TPS を見るときに concurrency と sequence length","quote_start":0,"quote_end":114,"text_sha256":"9aae04f2ff821682fd4b3d26bc2d8cab544c1b1f6d434028a45b32c1be2138ab","block_sha256":"9aae04f2ff821682fd4b3d26bc2d8cab544c1b1f6d434028a45b32c1be2138ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a10ea2a4-98f7-416b-aa43-233c299087e2"},{"id":"occ_82a50d3e819fa498d07224ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a3a52e63-d55b-4df5-b0f7-00b3e01cfbb7","section_id":"sec_fe98db46-f50f-4c95-b9cf-ead6a4bdd961","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"## 性能比較：学習と推論の実測・公表値・推定の整理","quote_start":0,"quote_end":26,"text_sha256":"d1fe113be82da049e67818e4ca32568b7f8bfbf46060a8aac8b8f5f5481723bd","block_sha256":"d1fe113be82da049e67818e4ca32568b7f8bfbf46060a8aac8b8f5f5481723bd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a3a52e63-d55b-4df5-b0f7-00b3e01cfbb7"},{"id":"occ_66d90a223b7565144b2766cb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a535329f-f770-4601-a21f-f2664ab8946b","section_id":"sec_409e6cb8-dd7b-4ea8-9210-ad8f1d0a1609","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":7,"end":15,"exact":"KV cache","quote":"vLLM は、KV cache を **固定トークン数ごとの block** に分けて管理する PagedAttention 系の設計を説明しています。ブロック化する狙いは、KV cache を扱いやすくし、メモリの無駄や断片化を","quote_start":0,"quote_end":115,"text_sha256":"4a55665b0007eab171c6657a16cf884375febc9466c151f099dc711903929c88","block_sha256":"4a55665b0007eab171c6657a16cf884375febc9466c151f099dc711903929c88","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a535329f-f770-4601-a21f-f2664ab8946b"},{"id":"occ_59d32c122a740b973b22224e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a580f24b-50ce-4d73-939b-489c6ebbccf5","section_id":"sec_4901810f-ce1a-4bdb-9336-2c20928c23ed","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":84,"end":92,"exact":"KV cache","quote":"ん増える」** という構造になりやすく、ここで帯域が効いてきます。NVIDIAのベンチ資料でも、長い出力では KV cache のメモリコストが成長し、安定した inter-token latency は効率のよいメモリ管理や帯域利用を示す、とされています。 ([NVIDIA Docs](https://docs.nvidi","quote_start":29,"quote_end":192,"text_sha256":"597080fd39996324752edd4afdd7759f6b87f03d9a363a1562d65e8a39c00620","block_sha256":"597080fd39996324752edd4afdd7759f6b87f03d9a363a1562d65e8a39c00620","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a580f24b-50ce-4d73-939b-489c6ebbccf5"},{"id":"occ_05fa72f4d1231df6eb5f1dc9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a6f15856-b4d7-4e0a-8752-3a8523a8d0b0","section_id":"sec_3b51abe6-0b27-4425-8551-dea67c06ab4d","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"LOPS利用率\n- HBM帯域利用率\n- all-reduce時間\n- 通信待ち時間\n- スケーリング効率\n\n推論で見たいもの\n- TTFT\n- ITL\n- TPS\n- queueing\n- KV cache使用量\n- latencyのばらつき\n```","quote_start":16,"quote_end":142,"text_sha256":"5850b0a1b13b5cd4e4715c513e26cf9d11b7e5760b18823fea8805aaf2569b0b","block_sha256":"5850b0a1b13b5cd4e4715c513e26cf9d11b7e5760b18823fea8805aaf2569b0b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a6f15856-b4d7-4e0a-8752-3a8523a8d0b0"},{"id":"occ_3c06d8ed336178a1b658441e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a811a8e0-1bc0-4fb8-ba71-8ea03197b80a","section_id":"sec_3f5d34cd-20f0-40e9-a069-3e355f5474b5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"### Groq（LPU）の位置づけ：推論単価と対話品質の別軸を作る","quote_start":0,"quote_end":34,"text_sha256":"900efb98495c26c36a71b43e16dd8ed6f675fe73834d507cdfe50d117d1f4828","block_sha256":"900efb98495c26c36a71b43e16dd8ed6f675fe73834d507cdfe50d117d1f4828","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a811a8e0-1bc0-4fb8-ba71-8ea03197b80a"},{"id":"occ_cb039eea03edcd13b758edbf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_a922b362-c6a9-42a2-a4dd-edcc18642976","section_id":"sec_e2934bf0-f3e0-45a7-bb16-88627c87b08d","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":93,"end":101,"exact":"KV cache","quote":"tokenizer によって違いますが、ここでは「過去が増えるほど参照量が増える」ことを見るための簡略例です。KV cache は、こうした**処理済みトークンの履歴を保持して、後続トークンの attention で再利用する**ためにあります。TensorRT はこの再利用を GPU 計算削減のための仕組みとして説明してい","quote_start":38,"quote_end":201,"text_sha256":"501c013b17fdef46189383d6d162fde2543183a8fbf445253a4e9ef715d2a9a6","block_sha256":"501c013b17fdef46189383d6d162fde2543183a8fbf445253a4e9ef715d2a9a6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_a922b362-c6a9-42a2-a4dd-edcc18642976"},{"id":"occ_7c4c8bad318bcadd8299d476","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_aaa0fb5e-d6d8-4c27-840a-c6f4a8db64b9","section_id":"sec_2d061dfb-a4aa-4643-a2cc-4e1897124ae9","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"**ジッタ** が支配的になるのは、特に同期型処理やリアルタイム推論で、**平均より「遅い側の揺れ」が効く**からです。たとえば 8 GPU で all-reduce する場合、7枚が速く終わっても、1枚だけ一瞬遅れれば全員が待ちます。オンライン推論でも、平均応答時間","quote_start":0,"quote_end":134,"text_sha256":"e9deb41c1b7f07087678f96c1705bc495f2c3c608bf99e7b7d92e7e8e119c2cf","block_sha256":"e9deb41c1b7f07087678f96c1705bc495f2c3c608bf99e7b7d92e7e8e119c2cf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_aaa0fb5e-d6d8-4c27-840a-c6f4a8db64b9"},{"id":"occ_644cefcf97be5e31b9ebac85","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_ad87e119-004d-4495-bd89-1bdb7fa04896","section_id":"sec_b3572938-d67b-4605-b557-315055a6bcd5","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":9,"end":16,"exact":"Prefill","quote":"### 4-1. Prefill","quote_start":0,"quote_end":16,"text_sha256":"6387ae729d81f59f13bc4c4dd1d2ce5e0e636082fd056df71b7f0b243bdf4b87","block_sha256":"6387ae729d81f59f13bc4c4dd1d2ce5e0e636082fd056df71b7f0b243bdf4b87","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_ad87e119-004d-4495-bd89-1bdb7fa04896"},{"id":"occ_0fb1804bc779bcae1e2f8a82","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_b2b5afef-0bbd-4304-8494-35e25b6b2503","section_id":"sec_0a8cf111-965a-43ae-93c0-9991072df106","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":8,"end":15,"exact":"prefill","quote":"### \\0. prefill の直後","quote_start":0,"quote_end":19,"text_sha256":"292fccc84ffce062f7447458885d8ba1f19a3c1215cc555f1a182161216a2d54","block_sha256":"292fccc84ffce062f7447458885d8ba1f19a3c1215cc555f1a182161216a2d54","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_b2b5afef-0bbd-4304-8494-35e25b6b2503"},{"id":"occ_57558670f4dc15ecee4f3877","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_b2c73aed-7d80-4a1b-83b1-09f6f8d7d7a5","section_id":"sec_5d8a58ae-53a9-495c-b88b-15cfc651e808","layer":"code","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefill\n材料を大量投入\n→ GPUが大きな塊で計算\n→ まず最初の返答準備を終える\n\nだから効きやすいのは\nTTFT と GPU計算効率\n```","quote_start":0,"quote_end":80,"text_sha256":"5105244d7988b2a6bbd2559258b21eefc3a31b301867b056081f28e48fdfb6ec","block_sha256":"5105244d7988b2a6bbd2559258b21eefc3a31b301867b056081f28e48fdfb6ec","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_b2c73aed-7d80-4a1b-83b1-09f6f8d7d7a5"},{"id":"occ_c3d0ef7fa06903e313767fe2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_b4253090-443b-4fe7-b798-a013b9f9bfce","section_id":"sec_0289410f-2a3a-4d53-a244-efa6407f153a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":100,"end":102,"exact":"推論","quote":"er Engineの重要機能として掲げ、NVFP4性能を押し上げるとしています。一方で、第三者報道では「LLM推論において構造化スパースは効果が限定的だった」反省に触れ、Rubinの性能主張はスパースより圧縮・形式（FP4）側に寄せる、という説明があります。","quote_start":45,"quote_end":174,"text_sha256":"8959a0c33227cd66a9ee3cf125425950ff5680bca30317863f51169fca3b0b7c","block_sha256":"8959a0c33227cd66a9ee3cf125425950ff5680bca30317863f51169fca3b0b7c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_b4253090-443b-4fe7-b798-a013b9f9bfce"},{"id":"occ_999ad8493761aa6d4770852d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_b83e1420-dbeb-4b2d-bfbb-cf138787d9b2","section_id":"sec_ae441d93-02e2-4de4-9342-68c94664f2cd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"## \\5. 推論で「ジッタ」が嫌われる図","quote_start":0,"quote_end":21,"text_sha256":"09545eca9edcf361519c18cf473633a6f1f8bdae15c9944831bd105e411a3419","block_sha256":"09545eca9edcf361519c18cf473633a6f1f8bdae15c9944831bd105e411a3419","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_b83e1420-dbeb-4b2d-bfbb-cf138787d9b2"},{"id":"occ_ff47ea6c92627c6791482be4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_bbd9bb0a-7f7c-4c2c-9a72-8be44adb986d","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":11,"end":19,"exact":"KV cache","quote":"## \\4. FP8 KV cache とは何か","quote_start":0,"quote_end":24,"text_sha256":"63ea57d92c5afb0fe8b1832a0f0e06cd35fbed597f0465babb14423d5187b033","block_sha256":"63ea57d92c5afb0fe8b1832a0f0e06cd35fbed597f0465babb14423d5187b033","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_bbd9bb0a-7f7c-4c2c-9a72-8be44adb986d"},{"id":"occ_a3e3cf9fbecfe37cf086934e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_c7b18612-277b-4735-aef7-1dab064513de","section_id":"sec_71744e70-5690-46d9-8d50-9667d2d60b5b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":9,"end":11,"exact":"推論","quote":"### Groq：推論の「TTFT/デコード速度」を定量化しやすい","quote_start":0,"quote_end":33,"text_sha256":"c50d22be03d457253dcc165c9dd9f5d2713c3cb3520ee6a2a5cdaa9d8b959823","block_sha256":"c50d22be03d457253dcc165c9dd9f5d2713c3cb3520ee6a2a5cdaa9d8b959823","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_c7b18612-277b-4735-aef7-1dab064513de"},{"id":"occ_cdf047a29f343d82aab1596c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_cd43085b-642e-45ca-809a-5f10830ed31d","section_id":"sec_cf2db1c1-6854-4415-bc6b-f69a13c6dd26","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"### RLHF / 推論のためのRL：推論トークンが激増し、CPU・ストレージ・低レイテンシが前面に出る","quote_start":0,"quote_end":53,"text_sha256":"dfb077c6308cdfcdf48eb0af823df98ed89957eae53268a2ba53e99fbf665dc9","block_sha256":"dfb077c6308cdfcdf48eb0af823df98ed89957eae53268a2ba53e99fbf665dc9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_cd43085b-642e-45ca-809a-5f10830ed31d"},{"id":"occ_131bad1649af7796d06bbb7c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_ce655634-dcbf-415b-9668-c74b9157ef7b","section_id":"sec_7800b888-0c3d-4203-83d5-f95d6e4a3b61","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":9,"end":11,"exact":"推論","quote":"本調査の結論は、「推論（特にデコード）と“エージェント化が、GPU中心設計のボトルネックをメモリ階層＋通信＋ジッタ（ばらつき）へ押し上げ、Rubin世代ではHBM4・NVLink6・KVコンテキスト拡張（CMX/ICMS/","quote_start":0,"quote_end":111,"text_sha256":"4db121777fca6ef35b3a8584508ea16ee3eefd9d8745c2e4c016f730a52d08be","block_sha256":"4db121777fca6ef35b3a8584508ea16ee3eefd9d8745c2e4c016f730a52d08be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_ce655634-dcbf-415b-9668-c74b9157ef7b"},{"id":"occ_029985e7370cdc3d51562270","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_d0ddb132-ec4d-4932-b79f-436821857417","section_id":"sec_8e63d375-4e98-499e-b154-dc4234cd9739","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":77,"end":85,"exact":"KV cache","quote":"性][が][ある][仕事][を][3つ][教えて]\n   9個ぶんの過去を見る\n\n出力:\n[まず]\n\n出力後のKV cache:\n[1][2][3][4][5][6][7][8][9][10]\n```","quote_start":22,"quote_end":122,"text_sha256":"438da5cf274bf767a8e5b5a5da9a4efe7228e3388e68d78bebc6f95ed681121f","block_sha256":"438da5cf274bf767a8e5b5a5da9a4efe7228e3388e68d78bebc6f95ed681121f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_d0ddb132-ec4d-4932-b79f-436821857417"},{"id":"occ_6f3d043535afdea23b720a6a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_d4dd1aea-9a3f-41f4-a14d-d64233c0a0e4","section_id":"sec_5d8a58ae-53a9-495c-b88b-15cfc651e808","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefill\n\n長い入力を一気に処理\n[token][token][token][token]...\n         ↓\n大きな行列演算をまとめて回す\n         ↓\nKV cache を作る\n```","quote_start":0,"quote_end":109,"text_sha256":"fddc6431990630960be664aedb6ebd24e4f4bad27fefdd35afbd20e01dec8b0b","block_sha256":"fddc6431990630960be664aedb6ebd24e4f4bad27fefdd35afbd20e01dec8b0b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_d4dd1aea-9a3f-41f4-a14d-d64233c0a0e4"},{"id":"occ_50c0cfa05d60928496dda704","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_d7a98146-ef96-4647-af03-470ea73f65f5","section_id":"sec_a06a5f2d-3519-415c-b895-a0b56001c9b6","layer":"code","character_id":null,"count":6,"matched_aliases":["Decode","KV cache","Prefill","推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"```\nLLM推論の中身\n\n[Prefill]\n入力をまとめて読む\n→ KV cache を作る\n→ 最初の1トークンを出せる状態へ\n\n[Decode]\n1トークン出す\n→ KV cache に足す\n→ 次の1トークン","quote_start":0,"quote_end":109,"text_sha256":"c87a4304eb6d89b97222bb2c316732002843020aa8650e44b7f9586e3a58c6be","block_sha256":"c87a4304eb6d89b97222bb2c316732002843020aa8650e44b7f9586e3a58c6be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_d7a98146-ef96-4647-af03-470ea73f65f5"},{"id":"occ_149132a4f599472fc511b5c7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_d7b53387-b29f-4cfb-a687-d012d74e1151","section_id":"sec_28fea2c9-9a14-427f-a012-b845145f6909","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":30,"end":38,"exact":"KV cache","quote":"NVIDIA の説明をそのまま噛み砕くと、**長い出力ほど KV cache が増え、各新トークンの attention コストは入力＋出力長に線形に伸びるので、generation のメモリ帯域・容量要求が強くなり、ITL が上がりやすい**、ということです。 ([NVID","quote_start":0,"quote_end":138,"text_sha256":"a35267822e2ac840b6982cc8da92b912175fc3dc3050c4ef3d16fb271d9ee78b","block_sha256":"a35267822e2ac840b6982cc8da92b912175fc3dc3050c4ef3d16fb271d9ee78b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_d7b53387-b29f-4cfb-a687-d012d74e1151"},{"id":"occ_c9e900beb0c8727a7ff6db25","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_d7efe587-6fd9-42b3-aa36-e9c4a60dab64","section_id":"sec_07887d78-c99e-4994-a3a1-bc0a877a40b1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"**「学習」と「推論」で、どこでFLOPSより実効帯域・通信・ジッタが効いてくるのか**を図解っぽく並べます。基礎の見方は、NVIDIAのいう **「memory bandwidth / math bandwidth /","quote_start":0,"quote_end":110,"text_sha256":"c1da6a4af227485e453d38b183b9544160818f40ba747a5d65a786665166d5aa","block_sha256":"c1da6a4af227485e453d38b183b9544160818f40ba747a5d65a786665166d5aa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_d7efe587-6fd9-42b3-aa36-e9c4a60dab64"},{"id":"occ_d8afbb87deb0556b85676ed0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_daf3e80e-559a-4642-a52e-97a9348f3623","section_id":"sec_c0489759-32ff-45c9-a437-7a06d2f49d8c","layer":"body","character_id":null,"count":6,"matched_aliases":["KV cache","prefill","推論"],"evidence":{"text_basis":"markdown","start":2,"end":9,"exact":"prefill","quote":"**prefill** は、LLM推論の前半です。  \nユーザーの入力文をまとめて読み、attention に必要な **KV cache** を作る段階です。NVIDIA Dynamo の用語集では、prefill ","quote_start":0,"quote_end":109,"text_sha256":"59d1a687696d51cd0b9133050098b61c77b46c169e96d3ea7b3f93061aa2d99c","block_sha256":"59d1a687696d51cd0b9133050098b61c77b46c169e96d3ea7b3f93061aa2d99c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_daf3e80e-559a-4642-a52e-97a9348f3623"},{"id":"occ_977445ca20ec3a32293b006b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_de903181-8086-4c06-a5cc-a7ce1ef53f04","section_id":"sec_ae441d93-02e2-4de4-9342-68c94664f2cd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"つまり推論では、","quote_start":0,"quote_end":8,"text_sha256":"215aed541af28fa7e284bb3128114db3add41408d3282b03bee590f805fd0ffa","block_sha256":"215aed541af28fa7e284bb3128114db3add41408d3282b03bee590f805fd0ffa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_de903181-8086-4c06-a5cc-a7ce1ef53f04"},{"id":"occ_d9f7ed78e5c669f2fe326e47","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_e03dd7ed-3b30-4255-b96b-afd1ce97e5bf","section_id":"sec_c0489759-32ff-45c9-a437-7a06d2f49d8c","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":7,"end":14,"exact":"prefill","quote":"## \\1. prefill とは何か","quote_start":0,"quote_end":19,"text_sha256":"62007d9cfc0151578d5eac2b7af9836c2e6fc02531f52c816ab67155b5daea1a","block_sha256":"62007d9cfc0151578d5eac2b7af9836c2e6fc02531f52c816ab67155b5daea1a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_e03dd7ed-3b30-4255-b96b-afd1ce97e5bf"},{"id":"occ_0bebe1a1e3de45838c8e5a7d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_e3185001-fc0e-4d5e-8d82-d63c61176d93","section_id":"sec_496e2536-e98c-4a7d-95ea-2a6c2fbe2b80","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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S\\_","quote_start":0,"quote_end":110,"text_sha256":"badf8a2dac55cd4c188d879e8c4f2ac855ba9f7f2f8a6b4002f52554ae24bce0","block_sha256":"badf8a2dac55cd4c188d879e8c4f2ac855ba9f7f2f8a6b4002f52554ae24bce0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_e3185001-fc0e-4d5e-8d82-d63c61176d93"},{"id":"occ_d10411eddfd223271c4bc7fe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_e5363ac0-d530-42d8-bbdc-c8b7aeecc68b","section_id":"sec_914b2a87-a792-4119-894e-0af29a1acb8b","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":29,"end":37,"exact":"KV cache","quote":"**PagedAttention** は、**増え続ける KV cache をブロック単位で効率よく管理する考え方**です。NVIDIA Dynamo の用語集では、vLLM由来の「KV cache を requests を blocks に分けて効率よく管理する memo","quote_start":0,"quote_end":137,"text_sha256":"3f16e2c3cccaf62b414a4094c0c38787d300530fba4bf90b0b54961b2613a02f","block_sha256":"3f16e2c3cccaf62b414a4094c0c38787d300530fba4bf90b0b54961b2613a02f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_e5363ac0-d530-42d8-bbdc-c8b7aeecc68b"},{"id":"occ_ed44119b96bc7af2fdfe3d42","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_ec1ecf39-8a42-4121-b31b-0a4dbba7801a","section_id":"sec_c0489759-32ff-45c9-a437-7a06d2f49d8c","layer":"body","character_id":null,"count":2,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"prefill","quote":"つまり prefill は、**返答を始める前の下ごしらえ**です。  \n長い文章を読ませるほど、この前半戦が重くなります。NVIDIA は、入力長が長いほど prefill 段階のメモリ要求が増え、TTFT が伸びると説","quote_start":0,"quote_end":111,"text_sha256":"871d4816e29cb22b5cfe42052b715cf6d16456dc96508252300d3d2f9ebfaa27","block_sha256":"871d4816e29cb22b5cfe42052b715cf6d16456dc96508252300d3d2f9ebfaa27","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_ec1ecf39-8a42-4121-b31b-0a4dbba7801a"},{"id":"occ_7c455dd4d05123f025d1bf75","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_eca3526b-78c6-47c7-bd3d-152ef4d3fbc7","section_id":"sec_a06a5f2d-3519-415c-b895-a0b56001c9b6","layer":"body","character_id":null,"count":5,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"prefill","quote":"vLLM は、chunked prefill の説明で **prefill を compute-bound、decode を memory-bound** と明示的に対比しています。さらに、decode を優先して prefill を小分けに混","quote_start":0,"quote_end":122,"text_sha256":"a7e45a6725642b9cfe9f4f08419fe59866bd0ec5dee1e42372f2086e38679728","block_sha256":"a7e45a6725642b9cfe9f4f08419fe59866bd0ec5dee1e42372f2086e38679728","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_eca3526b-78c6-47c7-bd3d-152ef4d3fbc7"},{"id":"occ_b687df3b1ccc06d796d1eac4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_ef232b52-a8b6-401e-b0e5-a886931f6178","section_id":"sec_c837dfae-b8ff-4676-95d3-1e87938ee982","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"## \\4. 推論で帯域とジッタが支配的になる図","quote_start":0,"quote_end":24,"text_sha256":"823125f437aea9ba2f38e2a3fc11d6d4752ee5b4e72fda5a6ce2bb5521a3ee87","block_sha256":"823125f437aea9ba2f38e2a3fc11d6d4752ee5b4e72fda5a6ce2bb5521a3ee87","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_ef232b52-a8b6-401e-b0e5-a886931f6178"},{"id":"occ_68801ce44359355420e0ee30","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_f754d2c4-60ee-4c2c-b917-e1d42a25da44","section_id":"sec_a06a5f2d-3519-415c-b895-a0b56001c9b6","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":26,"end":33,"exact":"prefill","quote":"### 「TTFT・ITL・TPS の違い」 か、「prefill は compute-bound、decode は memory-bound になりやすい理由」 を図解っぽく解説","quote_start":0,"quote_end":90,"text_sha256":"d28d78656779dcc46db9ed0b7ab87f1c47c81dff80cde9e8a81bbd77d2ebae67","block_sha256":"d28d78656779dcc46db9ed0b7ab87f1c47c81dff80cde9e8a81bbd77d2ebae67","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_f754d2c4-60ee-4c2c-b917-e1d42a25da44"},{"id":"occ_5cacfa24d902e4c0093f6bb2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_f78bf5e5-f521-4b49-b1bd-17cf15de4092","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":6,"end":14,"exact":"KV 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に量子化するとメモリ使用量を大きく減らせて、**より多くのトークンを","quote_start":0,"quote_end":114,"text_sha256":"fa8fb531abca3f717d5d67ef68220c0239a8f48555f2924a45468f874ebf308b","block_sha256":"fa8fb531abca3f717d5d67ef68220c0239a8f48555f2924a45468f874ebf308b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_f78bf5e5-f521-4b49-b1bd-17cf15de4092"},{"id":"occ_67c69d197f6e3bf9d6cebe86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_f8e017e9-db17-4dbd-a68f-e7ebb84dcb12","section_id":"sec_7800b888-0c3d-4203-83d5-f95d6e4a3b61","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":180,"end":182,"exact":"推論","quote":"n NVL72の公式ページでは、**Blackwell比でMoE学習に必要なGPU数を1/4**、高対話・深い推論の推論コストを1/10（$/百万トークン）とする“経済性”指標を前面に置き、推論が「大量トークン×長文脈×低レイテンシ」の領域へ移ったことを明確にしています。","quote_start":125,"quote_end":261,"text_sha256":"47a1d17371478c750d7cf8090a77fe4a98b00b0c0cdecf57278e1c04f539fc78","block_sha256":"47a1d17371478c750d7cf8090a77fe4a98b00b0c0cdecf57278e1c04f539fc78","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_f8e017e9-db17-4dbd-a68f-e7ebb84dcb12"},{"id":"occ_4f6c892baf55b65b608509f3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_fb400a28-b331-4b56-b8b5-ac0337aa8926","section_id":"sec_e6ecc7b1-9e18-4209-bc22-1e2d95028607","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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cache","decode"],"evidence":{"text_basis":"markdown","start":46,"end":54,"exact":"KV cache","quote":"AIでは、特に **低 arithmetic intensity の処理、分散同期、LLMのKV cache を伴う decode、小さいカーネルの連打** が多いため、**ピークFLOPSだけでは実性能をほぼ説明しきれない**、というのが本質です。 ([NVIDIA Docs](https://docs.","quote_start":0,"quote_end":154,"text_sha256":"78e4647bf42165db8f0abb3d6e34da484506257588c59a555a22df6fe4f6462d","block_sha256":"78e4647bf42165db8f0abb3d6e34da484506257588c59a555a22df6fe4f6462d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_fe65c9b4-8609-4d9d-b6b2-a22321a5aeca"},{"id":"occ_dd1594bf565ccf33207bfa4e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b","work_id":"wrk_b4bdeaca-dc31-448c-8f47-03f9e39c14b0","block_id":"blk_ffb8ba05-eca3-4150-95a9-bf93da288b12","section_id":"sec_4b0c2319-5987-462c-9387-f66021b945c2","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":23,"end":30,"exact":"prefill","quote":"### \\3. TTFT・ITL・TPS と prefill/decode の対応","quote_start":0,"quote_end":41,"text_sha256":"e7be25f6dbceb4b07a140a9f813db8f9033da79d085452c91b406b4016cee23b","block_sha256":"e7be25f6dbceb4b07a140a9f813db8f9033da79d085452c91b406b4016cee23b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_584d62bb-c9f7-4d1f-b637-8fac93976f1b/#blk_ffb8ba05-eca3-4150-95a9-bf93da288b12"},{"id":"occ_d78b0d6229b5ef8026161ad8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5c1381d3-461d-40ab-acea-997127510f6e","work_id":"wrk_153585aa-ce70-48f0-8fcf-0c54fb47c7a9","block_id":"blk_4b357c59-dc28-45be-9e34-c8fa903715e9","section_id":"sec_dfe10c56-e2ca-4e35-9b41-3b0d9be91016","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":252,"end":254,"exact":"推論","quote":"クラスまで拡張\n  - Rubin Ultra Superpod 全体では、\n    \n    - **FP4推論 15 ExaFLOPS**\n    - **FP8学習 5 ExaFLOPS**\n    - = Blackwell Ultra世代の約14倍  \n      という数字が挙げられています。","quote_start":197,"quote_end":350,"text_sha256":"2fdcdb9ecaefdcdb45be1c29c218851587a56dbe37d7ae430441579073ae6880","block_sha256":"2fdcdb9ecaefdcdb45be1c29c218851587a56dbe37d7ae430441579073ae6880","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5c1381d3-461d-40ab-acea-997127510f6e/#blk_4b357c59-dc28-45be-9e34-c8fa903715e9"},{"id":"occ_abf7571baeb71a09dfbe7df3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5c1381d3-461d-40ab-acea-997127510f6e","work_id":"wrk_153585aa-ce70-48f0-8fcf-0c54fb47c7a9","block_id":"blk_6b9660b6-dcd8-4569-8a9f-9d6f4a314ae6","section_id":"sec_7d9fd5b5-93ab-4a07-9ac8-42b419a6b439","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"### 5-1. TCO の内訳イメージ（大規模 LLM トレーニング・推論クラスター）","quote_start":0,"quote_end":44,"text_sha256":"28f1eb194911a2ebfa4ffbe6dc992d6c2a3a3e83071c39724ff886f6ddd6dbff","block_sha256":"28f1eb194911a2ebfa4ffbe6dc992d6c2a3a3e83071c39724ff886f6ddd6dbff","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5c1381d3-461d-40ab-acea-997127510f6e/#blk_6b9660b6-dcd8-4569-8a9f-9d6f4a314ae6"},{"id":"occ_69fef7e6501bb1e5541e050e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5c1381d3-461d-40ab-acea-997127510f6e","work_id":"wrk_153585aa-ce70-48f0-8fcf-0c54fb47c7a9","block_id":"blk_7660ac37-4694-4f7e-8cca-6943241c9896","section_id":"sec_f363e30c-6f89-4bf8-ad62-ff1712f3a08f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":483,"end":485,"exact":"推論","quote":" 100 PF4 / GPU、1TB HBMクラス**\n  - Rubin Ultra Superpod：**推論 15 ExaFLOPS、学習 5 ExaFLOPS** を目標とする。([The Verge](https://www.theverge.com/news/631835/nvidia-blackwe","quote_start":428,"quote_end":585,"text_sha256":"5d0781ec199e7c4a471795dd6da809e34b0ad7654792fb95960d30821d06dde9","block_sha256":"5d0781ec199e7c4a471795dd6da809e34b0ad7654792fb95960d30821d06dde9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5c1381d3-461d-40ab-acea-997127510f6e/#blk_7660ac37-4694-4f7e-8cca-6943241c9896"},{"id":"occ_9b05dcc829fe41d238967a39","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5c1381d3-461d-40ab-acea-997127510f6e","work_id":"wrk_153585aa-ce70-48f0-8fcf-0c54fb47c7a9","block_id":"blk_7d9f4e80-d9bb-40f6-99cd-7e1369f1caf3","section_id":"sec_620022ef-82be-4ef1-965c-152a501efc70","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":153,"end":155,"exact":"推論","quote":"\n- **アーキテクチャ外した時のリスク**\n  \n  - TPUv4i が PaLM のような大型 LLM 推論に向かなかった事例のように、**アーキテクチャのミスは丸ごと自社が被るリスク**。([newsletter.semianalysis.com](https://newsletter.semianaly","quote_start":98,"quote_end":255,"text_sha256":"57dd46af89a83f73d2076ea9ff041c1569156b0418c88870da18038b54eb7de0","block_sha256":"57dd46af89a83f73d2076ea9ff041c1569156b0418c88870da18038b54eb7de0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5c1381d3-461d-40ab-acea-997127510f6e/#blk_7d9f4e80-d9bb-40f6-99cd-7e1369f1caf3"},{"id":"occ_985c964f6c0ded67fd179f1e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5c1381d3-461d-40ab-acea-997127510f6e","work_id":"wrk_153585aa-ce70-48f0-8fcf-0c54fb47c7a9","block_id":"blk_89eb3a37-dbb2-4cae-91fe-71042d474f87","section_id":"sec_bfc49134-fc3d-48b6-a2b6-703cafade4ab","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":402,"end":404,"exact":"推論","quote":"source=chatgpt.com))\n- Hopper世代（H100）との比較では、\n  \n  - 大規模推論で**最大10倍のレスポンス改善**、\n  - **5倍のスループット/W**、\n  - 「トークン収益」ベースで**50倍のAIファクトリー出力**というかなり誇張気味のマーケ指標を出しています。(","quote_start":347,"quote_end":504,"text_sha256":"b414dd55a5a21e965f609059ccaf0913fab812e6ba59d85c23a252753e206b08","block_sha256":"b414dd55a5a21e965f609059ccaf0913fab812e6ba59d85c23a252753e206b08","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5c1381d3-461d-40ab-acea-997127510f6e/#blk_89eb3a37-dbb2-4cae-91fe-71042d474f87"},{"id":"occ_67021a6ad1e7a816df3f7295","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5cb295dd-df59-4d28-9799-42065461c569","work_id":"wrk_5380fdec-35cc-4092-a93e-530c9abca5c9","block_id":"blk_0752c9ee-0d75-410c-8198-95d173266aa6","section_id":"sec_8ecd3b92-9d8e-4014-a351-12adc2a47050","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":37,"end":39,"exact":"推論","quote":"Tesla AIが「車やロボットを動かすAI」だとすれば、xAIは「言語、推論、検索、会話、生成、エージェント」を担うAIです。","quote_start":0,"quote_end":64,"text_sha256":"1b42eb47c194d4bb9edf32788a2c2875faffb4f971fbfd6d1772c0a7a804de55","block_sha256":"1b42eb47c194d4bb9edf32788a2c2875faffb4f971fbfd6d1772c0a7a804de55","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5cb295dd-df59-4d28-9799-42065461c569/#blk_0752c9ee-0d75-410c-8198-95d173266aa6"},{"id":"occ_a922cd514a6200b19a0c0ba1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5cb295dd-df59-4d28-9799-42065461c569","work_id":"wrk_5380fdec-35cc-4092-a93e-530c9abca5c9","block_id":"blk_1d01f1bb-8a1b-4777-a3d8-22f80508f3f0","section_id":"sec_f07eb27a-ae6e-4f12-b383-76f3bc65a17b","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"xt\nTesla\n→ FSD、Optimus、Robotaxiにチップが必要\n\nxAI\n→ Grok、LLM、推論、エージェントにチップが必要\n\nSpaceX\n→ Starlink、宇宙AI、AI compute外販にチップが必要\n\nTeraFab\n→ それら全てにチップを供給する\n```","quote_start":5,"quote_end":150,"text_sha256":"8b5a4a7c9da3442abab2bbdcc9549fd92f40baf7c8e71ccd1ff43f4d112705e9","block_sha256":"8b5a4a7c9da3442abab2bbdcc9549fd92f40baf7c8e71ccd1ff43f4d112705e9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5cb295dd-df59-4d28-9799-42065461c569/#blk_1d01f1bb-8a1b-4777-a3d8-22f80508f3f0"},{"id":"occ_5e0ef15cebc9d0f86cafe7c6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5cb295dd-df59-4d28-9799-42065461c569","work_id":"wrk_5380fdec-35cc-4092-a93e-530c9abca5c9","block_id":"blk_27f96f9a-c1c7-443e-9752-2bb52ebc953f","section_id":"sec_2a1d3cbe-925c-4885-956e-496c4812e6b6","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":334,"end":336,"exact":"推論","quote":"ータセンター\n│\n└─ TeraFab / 半導体供給構想\n   ├─ AIチップ\n   ├─ Tesla向け推論チップ\n   ├─ SpaceX/xAI向けAIデータセンターチップ\n   └─ 将来の軌道上compute向け半導体\n```","quote_start":279,"quote_end":399,"text_sha256":"8e7f8b11863708729e5501230db0b04daccaaa6ff88ce3dd5dc07d0b370e9e78","block_sha256":"8e7f8b11863708729e5501230db0b04daccaaa6ff88ce3dd5dc07d0b370e9e78","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5cb295dd-df59-4d28-9799-42065461c569/#blk_27f96f9a-c1c7-443e-9752-2bb52ebc953f"},{"id":"occ_05059178541804863cc33d80","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5cb295dd-df59-4d28-9799-42065461c569","work_id":"wrk_5380fdec-35cc-4092-a93e-530c9abca5c9","block_id":"blk_546fdb96-df3d-498e-af0f-c1605adb05c8","section_id":"sec_d12ebd40-21cb-4541-aa5e-c6107b5626c1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"* チップ\n* 電力\n* データセンター\n* 通信\n* 推論基盤\n* エージェント\n* ユーザー接点\n* 物理世界への実装","quote_start":0,"quote_end":61,"text_sha256":"5057837b7d9fb859ae52f712bce303bafc90abf44e3398fafaa7f6f30c22f424","block_sha256":"5057837b7d9fb859ae52f712bce303bafc90abf44e3398fafaa7f6f30c22f424","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5cb295dd-df59-4d28-9799-42065461c569/#blk_546fdb96-df3d-498e-af0f-c1605adb05c8"},{"id":"occ_0e70688ebeb7a9c59de3fd48","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5cb295dd-df59-4d28-9799-42065461c569","work_id":"wrk_5380fdec-35cc-4092-a93e-530c9abca5c9","block_id":"blk_6dd5cd3b-0162-447d-b3b4-4f007f60982f","section_id":"sec_d12ebd40-21cb-4541-aa5e-c6107b5626c1","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"```text\nAIエージェント需要が爆発\n↓\n推論トークン需要が急増\n↓\nAIデータセンター不足が深刻化\n↓\nSpaceX / xAIがcomputeを外販\n↓\nGrok、X、API、AIエージェント収益が伸びる\n↓\nStarlinkと宇宙compu","quote_start":0,"quote_end":126,"text_sha256":"394cbb657a6f544111e882cfc4714aa9a2903fc2a11b86d240012581219868f9","block_sha256":"394cbb657a6f544111e882cfc4714aa9a2903fc2a11b86d240012581219868f9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5cb295dd-df59-4d28-9799-42065461c569/#blk_6dd5cd3b-0162-447d-b3b4-4f007f60982f"},{"id":"occ_1b10d7114fd4ec722e72fb85","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":null,"section_id":null,"layer":"title","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"title","start":15,"end":17,"exact":"推論","quote":"HBMだけでは足りない: 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つまり、効率化は推論を安くして、長文、RAG、エージェントを増やす。","quote_start":0,"quote_end":44,"text_sha256":"dd7ae8c8ba6b8dea8dc819f79880b791db194b234152cb0e4d482f61aa411d50","block_sha256":"dd7ae8c8ba6b8dea8dc819f79880b791db194b234152cb0e4d482f61aa411d50","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_5a58dca9-1579-441a-868b-d17565684616"},{"id":"occ_42617a56862cbb13078453db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_5aa50e1a-6af7-4eda-af66-51ae45824ee8","section_id":"sec_69902492-7190-4bae-bcb8-d1d67dbabe84","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"これらを合わせると、同じ推論量あたりのHBM必要量は、2030年までに40〜55%程度減らせる可能性がある。","quote_start":0,"quote_end":54,"text_sha256":"91d8d3e15ce01a46b21921d930ce56fa18c5dd160940ede873b861e6a5cf05f5","block_sha256":"91d8d3e15ce01a46b21921d930ce56fa18c5dd160940ede873b861e6a5cf05f5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_5aa50e1a-6af7-4eda-af66-51ae45824ee8"},{"id":"occ_6ee6bf287c8c1cc914217227","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_5bcae5a9-de54-4734-a454-f88c88a9c8c1","section_id":"sec_2fe97cbb-1ea0-4ab5-92ef-0f961d8bef91","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"メモリ節約技術は、2027〜2030年にかけて確実に効いてくる。同じ推論量あたりのHBM必要量は、2030年までに40〜55%程度削減される可能性がある。","quote_start":0,"quote_end":77,"text_sha256":"a60e02080fe202cfa39c2af3174f8dc0bfc43fba595d75a323d87d4f453e8d5f","block_sha256":"a60e02080fe202cfa39c2af3174f8dc0bfc43fba595d75a323d87d4f453e8d5f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_5bcae5a9-de54-4734-a454-f88c88a9c8c1"},{"id":"occ_91f7e98d1e1795038a507fdf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_68c5c230-1ef9-4354-988c-2b5e336560de","section_id":"sec_021be68d-ce1a-4b16-9ee2-c261624ee63f","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":6,"end":13,"exact":"KVキャッシュ","quote":"代表的なのがKVキャッシュである。LLMは過去のトークンを参照しながら次のトークンを生成するため、会話が長くなるほど、ユーザーごとの中間データが増えていく。短いチャットなら大きな問題にならなくても、128K、1M、さらに動画・","quote_start":0,"quote_end":113,"text_sha256":"495e81611316746d36e7c4be77eb7707ea38acd7eb40e9254a57c619808ac9d7","block_sha256":"495e81611316746d36e7c4be77eb7707ea38acd7eb40e9254a57c619808ac9d7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_68c5c230-1ef9-4354-988c-2b5e336560de"},{"id":"occ_be00a69dbd2784e1c3b1966e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_6b57cba4-cbe3-44ec-af8e-67e0392fff5d","section_id":"sec_2fe97cbb-1ea0-4ab5-92ef-0f961d8bef91","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"むしろ、節約技術によってAI推論が安くなり、安くなった分だけ利用量が増え、節約分を新需要が食い尽くす可能性が高い。","quote_start":0,"quote_end":57,"text_sha256":"18ee852666d98a409f1d9ee1823dda76d138962b3c425a2904ef2327e3ab8a27","block_sha256":"18ee852666d98a409f1d9ee1823dda76d138962b3c425a2904ef2327e3ab8a27","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_6b57cba4-cbe3-44ec-af8e-67e0392fff5d"},{"id":"occ_ba908c4a6e9f3fbe1c005b19","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_76bd3237-b131-40c6-9449-bfcd77449ec2","section_id":"sec_2fe97cbb-1ea0-4ab5-92ef-0f961d8bef91","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論"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PC、車載AIの利用量が増える。","quote_start":0,"quote_end":89,"text_sha256":"fca801391719fe429a14092c0bb7d91f1b1b1571e1d1eaee573fdf96545ec55c","block_sha256":"fca801391719fe429a14092c0bb7d91f1b1b1571e1d1eaee573fdf96545ec55c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_76bd3237-b131-40c6-9449-bfcd77449ec2"},{"id":"occ_b83478981f876f5ef4ceca92","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_78231717-0127-45e6-a20f-76c02c3649dc","section_id":"sec_2294282e-6570-423c-9922-45d214fa2f1e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"# 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見るべき指標は価格だけでなく、tokens/sec/GB HBM、KV cache hit rate、p99 latency、AI向けeSSD比率、HBF標準化である。\n- 本稿の需給指数はシナリオであり、精密予測ではない。投資判断では一次情報、決算、設備投資、顧客認定、実出荷を必","quote_start":151,"quote_end":314,"text_sha256":"4e92566ac88fc2cdf699ad1215f2b17aa08aaa4344064c11ae6d1d08a6ef239f","block_sha256":"4e92566ac88fc2cdf699ad1215f2b17aa08aaa4344064c11ae6d1d08a6ef239f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_79fee9b1-cc1c-4fad-aace-0be53e5695e3"},{"id":"occ_ea26c456b95bd4079de1acdf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_7c1390f6-1dba-4cca-8918-702c2b423bed","section_id":"sec_e9426f80-4cfa-43b7-b2cc-8575eda1daff","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"しかし、AIの主戦場が大規模学習から、常時稼働する推論、RAG、エージェント、長文コンテキスト、動画・ロボットAIへ広がるにつれ、問題はGPUとHBMだけでは解けなくなっている。","quote_start":0,"quote_end":89,"text_sha256":"937c777773893e8eeccc9747a4241ab231e73c69d4c41671bff207f3ff301a13","block_sha256":"937c777773893e8eeccc9747a4241ab231e73c69d4c41671bff207f3ff301a13","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_7c1390f6-1dba-4cca-8918-702c2b423bed"},{"id":"occ_ed9182b78f696cc54862ed9d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_7dfacb80-9b7b-4834-9033-33a58005adc1","section_id":"sec_965a89fb-4500-404c-a89b-48f983873809","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論では、コールドKV、RAGデータ、ベクトルDB、文脈メモリ、ログ、検索インデックス、動画・画像データを大量に保持する必要がある。しかも、単なる安価な保存用NANDではなく、高IOPS、低レイテンシー、","quote_start":0,"quote_end":104,"text_sha256":"b33954db4531277d79424ebb225f7925b6077b14b4058748964ab5511796329c","block_sha256":"b33954db4531277d79424ebb225f7925b6077b14b4058748964ab5511796329c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_7dfacb80-9b7b-4834-9033-33a58005adc1"},{"id":"occ_fedee6965f039b08536bd95d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_8394548c-7def-493b-b248-b06a1946a1c2","section_id":"sec_e9426f80-4cfa-43b7-b2cc-8575eda1daff","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":34,"end":41,"exact":"KVキャッシュ","quote":"これから重要になるのは、HBM、DRAM、CXL、NAND、SSD、KVキャッシュをどう階層化するかである。","quote_start":0,"quote_end":54,"text_sha256":"529245afc9374742ecda07968efd0ef637e0cf3a3509b74d698a773364c19bb6","block_sha256":"529245afc9374742ecda07968efd0ef637e0cf3a3509b74d698a773364c19bb6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_8394548c-7def-493b-b248-b06a1946a1c2"},{"id":"occ_db05d4ab8c801ad8ff3ff443","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_851ecce8-9bd0-4bcf-ac47-8f7957e2ca37","section_id":"sec_6de5c832-bf67-4d84-89d4-3952cce6c539","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"、CMXによって従来型ストレージ比で最大5倍のtokens/secをうたっている。ただし、これはすべてのLLM推論が5倍になるという意味ではない。効くのは、長文コンテキスト、KVキャッシュ再利用、RAG、エージェント、GPUがデータ待ちで止まっているような環境である。","quote_start":7,"quote_end":141,"text_sha256":"adf4a1988b0a1d71e0c8eba482b6e193e16b3b9034d373f82060bac6fffc0b5f","block_sha256":"adf4a1988b0a1d71e0c8eba482b6e193e16b3b9034d373f82060bac6fffc0b5f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5dbe090a-744f-4659-8ee9-8fbc7195e238/#blk_851ecce8-9bd0-4bcf-ac47-8f7957e2ca37"},{"id":"occ_4b928ece78d625db824a8767","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5dbe090a-744f-4659-8ee9-8fbc7195e238","work_id":"wrk_22490a2d-59cf-4ab7-9288-4f1e6c0f5a39","block_id":"blk_8ba53eb7-c33c-4018-abea-a7530c986410","section_id":"sec_6de5c832-bf67-4d84-89d4-3952cce6c539","layer":"body","character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Factoryは何ですか？\n\nTrainingですか\nInferenceですか\nAgentですか\nRobotですか\nScientific AIですか\nSovereign AIですか\n\nCustom XPUを使いますか\nNVIDIA GPUを使いますか\n\n必要な部品を組み合","quote_start":0,"quote_end":151,"text_sha256":"911ae8332c4250ad0b9bc96cb28dcf8fb96e3bbb0b26669b35adbffaa27e3389","block_sha256":"911ae8332c4250ad0b9bc96cb28dcf8fb96e3bbb0b26669b35adbffaa27e3389","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_5f44b55a-c37f-42cf-ac49-298da914a87a/#blk_6c0a2a94-277c-457f-baff-433c044fb86e"},{"id":"occ_782791542233cf7e5ec4cbae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_5f44b55a-c37f-42cf-ac49-298da914a87a","work_id":"wrk_57e07914-940b-4727-b215-7d77a2ac51c0","block_id":"blk_7c9692c2-7fcf-42ad-8c95-d7b5c61b243e","section_id":"sec_b5863679-56d9-4843-8b34-cb01803ac38e","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":0,"end":9,"exact":"Inference","quote":"Inference EndpointではCPU、NVIDIA 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の内容からの推論です。 ([Reuters](https://www.reuters.com/world/asia-pacific/broadcom-flags-supply-constraints-says-tsm","quote_start":175,"quote_end":332,"text_sha256":"e3a21ed10eddc3b3c536a2a10f55625097618f7a80261f3e303bd90e465a840a","block_sha256":"e3a21ed10eddc3b3c536a2a10f55625097618f7a80261f3e303bd90e465a840a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_61f118e1-20e7-486f-b830-cae804dd9f14/#blk_668d761f-cc16-47b5-8389-9ded004e2645"},{"id":"occ_84f5a5ed56a7e783aef5152c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_61f118e1-20e7-486f-b830-cae804dd9f14","work_id":"wrk_7a4ffab3-0117-4e75-821b-23feb00ad9c5","block_id":"blk_89173f24-d317-48d9-9aac-bbc2c57dee97","section_id":"sec_9c5a9ff1-7bac-4af9-abf1-87a82660f20a","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":86,"end":88,"exact":"推論","quote":"プへの流れをさらに強めること**です。今後のAI半導体は、汎用GPUが引き続き重要である一方、車載、ロボット、推論、宇宙用途のように制約条件が異なる領域では、専用シリコンの価値が上がりやすくなります。Terafab 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keynote と公開需給情報からの推論です。 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自社DCとクラウドを統合 | 5 |\n| 大規模分散学習 | 数千GPUを同期運用 | 5＋ |\n| AI推論基盤 | 遅延、価格、モデルを最適化 | 4～5 |\n| RAG | 社内データをAIへ接続 | 3～5 |\n| エージェント基盤 | AIへ記憶・権限・ツールを提供 | 5 |\n| 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cacheをOSの仮想メモリのように管理し、同じレイテンシ条件でLLM推論スループットを2〜4倍改善したと報告されています。これはGPUクラウドが「LLM向けクラウドOS」に近づく重要な技術です。([arXiv","quote_start":0,"quote_end":131,"text_sha256":"110772a657de550cc902017169f60b42add45860c836c8ca7801cf737465ab6e","block_sha256":"110772a657de550cc902017169f60b42add45860c836c8ca7801cf737465ab6e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_05839c5c-5f11-432a-b5c2-aeeab3ff21ac"},{"id":"occ_fb526712d23be37f996e3d7c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_069fff38-4c14-4f5a-b02e-f82ade30d9d8","section_id":"sec_59a30840-3f80-46be-aa94-9d9cd7331f4b","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"- 推論専用ASIC\n- エッジNPU\n- ロボット用VLAチップ\n- 車載AI SoC\n- DPU/SmartNIC\n- メモリ近傍計算\n- 光インターコネクト\n- 専用KV cacheアクセラレータ","quote_start":0,"quote_end":101,"text_sha256":"bb4cee01467aaeef2dcee340f32fdf2c0ebc0cb237c4c372566ec9bf11a01ca6","block_sha256":"bb4cee01467aaeef2dcee340f32fdf2c0ebc0cb237c4c372566ec9bf11a01ca6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_069fff38-4c14-4f5a-b02e-f82ade30d9d8"},{"id":"occ_6dc7f202a2404e3b3b4c19b1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_06db30fd-9d76-4258-b089-c38528265ac2","section_id":"sec_7578d2e0-ef74-4382-b7cf-ad9d1c2952f3","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"vLLMはPagedAttentionを使い、LLM推論で問題になるKV cacheのメモリ無駄を減らす方向の代表例です。2025年のvLLM論文では、block-level memory managementとpreemptive request sch","quote_start":0,"quote_end":128,"text_sha256":"d1d8c6146ad96ca835d79007586ee7871c4cb362103f988a7dc981e069b7b027","block_sha256":"d1d8c6146ad96ca835d79007586ee7871c4cb362103f988a7dc981e069b7b027","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_06db30fd-9d76-4258-b089-c38528265ac2"},{"id":"occ_f65c87197470aecf26643ffa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_07dbb89b-dab3-4a58-bdf0-9b0aa7cf827d","section_id":"sec_67c7d199-a2f4-4058-b6a0-18b296e20e76","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":39,"end":47,"exact":"KV cache","quote":"- GPU利用率が改善する\n- 1リクエストあたりのGPUコストが下がる\n- KV cache管理が洗練される\n- prefill/decode分離が一般化する\n- GPUクラスタのスケジューリングが高度化する\n- マルチテナントGPUが普及する","quote_start":0,"quote_end":123,"text_sha256":"ef83b6bb54ab709a73ac2cb61cec584f6203de7a3be574eb24de46ee98bf2ee8","block_sha256":"ef83b6bb54ab709a73ac2cb61cec584f6203de7a3be574eb24de46ee98bf2ee8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_07dbb89b-dab3-4a58-bdf0-9b0aa7cf827d"},{"id":"occ_084ce5306392669c41dd9143","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_08827f75-c91a-4ff8-86f5-a6d09d2f8ee0","section_id":"sec_d32d82b3-f2c9-4767-a64c-1da8e863e79a","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"またGroqCloudは、リアルタイム推論に必要な低遅延・高効率なLLM推論を提供するものとして説明されています。([Groq](https://groq.com/newsroom/demand-for-real-time-ai-infere","quote_start":0,"quote_end":121,"text_sha256":"503dfce0b7025518d3187e027dc5166a831413ef0e538d69cdca73ed253621dd","block_sha256":"503dfce0b7025518d3187e027dc5166a831413ef0e538d69cdca73ed253621dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_08827f75-c91a-4ff8-86f5-a6d09d2f8ee0"},{"id":"occ_0b6bc60aeff9a5c876a0ed65","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_0992d562-4b01-45e7-81ff-d46a170fa630","section_id":"sec_2ca80241-bcaf-4abc-aa43-ec9330275a7e","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"```\nユーザー\n  ↓\nCPUサーバー\n  ↓\nAI推論サーバー\n  ↓\nGPU\n  ↓\n回答生成\n```","quote_start":0,"quote_end":54,"text_sha256":"bd65fdeee237020751a45672eefb21d10bc70067d4c7882a442c8f78861c7d51","block_sha256":"bd65fdeee237020751a45672eefb21d10bc70067d4c7882a442c8f78861c7d51","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_0992d562-4b01-45e7-81ff-d46a170fa630"},{"id":"occ_cdcc5ecd112a249c7eb52944","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_09a22bca-9e2a-4362-b3b8-b4dd6b5c5380","section_id":"sec_f33e4986-221e-4ec4-a922-4302306101f0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論が安くなると、","quote_start":0,"quote_end":11,"text_sha256":"45dc1ab6356413bfebca10955ea76da734b7afad6bbfa50a61c72e1d058b0a30","block_sha256":"45dc1ab6356413bfebca10955ea76da734b7afad6bbfa50a61c72e1d058b0a30","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_09a22bca-9e2a-4362-b3b8-b4dd6b5c5380"},{"id":"occ_8d77830af30ebab955519569","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_0b1ea4e5-7402-48d9-86a3-9e0d7a88075d","section_id":"sec_b50c6acb-2ce6-4849-9963-052cf62a540b","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":50,"end":58,"exact":"KV cache","quote":"2030年前後には、GPUクラウドは現在の「GPUインスタンスを貸す」段階から、  \n**トークン、KV cache、SLO、モデル常駐、GPUメモリ単位で管理するクラウドOS** に近づくと思います。","quote_start":0,"quote_end":101,"text_sha256":"f7250b9ce87593c722528f4b8ec849fb24f59bc2e72512a6db5271d63bd91a58","block_sha256":"f7250b9ce87593c722528f4b8ec849fb24f59bc2e72512a6db5271d63bd91a58","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_0b1ea4e5-7402-48d9-86a3-9e0d7a88075d"},{"id":"occ_1d31b5b76f0e6f8a7e46a5c3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_0d2ff0fc-ab99-4be5-8b84-0f1f01c5f76f","section_id":"sec_7e6570d5-47c1-4c8d-8c40-b67e49433c73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"しかし、AI推論が安くなると、AIエージェント、動画生成、音声AI、企業AI、ロボット、RAG、個人AI秘書が爆発的に増えます。すると、CPU側では次の処理が増えます。","quote_start":0,"quote_end":84,"text_sha256":"5cbe3cc5c24b86df299307f9f592f99cf1955eb4ce96a1c39c2edaee4c99a438","block_sha256":"5cbe3cc5c24b86df299307f9f592f99cf1955eb4ce96a1c39c2edaee4c99a438","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_0d2ff0fc-ab99-4be5-8b84-0f1f01c5f76f"},{"id":"occ_364da0f253decfa1cc097c30","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_0daec283-2456-4d5c-81a8-8050adc7e2dd","section_id":"sec_e8277738-b64e-435b-8135-df3e9a700faf","layer":"code","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"```\nTPUクラウド\n  → すでに本格実用。\n  → 学習・推論どちらも可能。\n  → Google Cloud上の大規模AI基盤。\n\nLPUクラウド\n  → すでに推論クラウドとして実用化。\n  → 特に低遅延LLM推論に強い。\n  → 学習や汎用AI処理には向","quote_start":0,"quote_end":134,"text_sha256":"8bb87c66b521d07ac6466b149c78ac2ab86a1bd58f78b2476611ff600f00c063","block_sha256":"8bb87c66b521d07ac6466b149c78ac2ab86a1bd58f78b2476611ff600f00c063","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_0daec283-2456-4d5c-81a8-8050adc7e2dd"},{"id":"occ_7a28bb2f939397aff84d0ad7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_0fa8be5c-2e50-4ad2-8fdc-b3847f1a7ff1","section_id":"sec_be81384c-22d0-4e73-b24c-902356664441","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":95,"end":97,"exact":"推論","quote":"\nしかしAI需要が急増するため、総GPU需要は増える。\n\n中期：\nGPUクラウドOS化でGPU利用率が改善。\n推論単価が下がり、AIエージェントが普及。\n総GPU需要はまだ増えやすい。\n\n長期：\nTPU、LPU、ASIC、NPU、エッジAIが普及。\n単純推論向けGPU需要の伸びは鈍化。\nGPUは動画生成、学習、V","quote_start":40,"quote_end":197,"text_sha256":"11effc30d734d8356f2dc036565a9682577b57b11b8994de8f269e912e1004cc","block_sha256":"11effc30d734d8356f2dc036565a9682577b57b11b8994de8f269e912e1004cc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_0fa8be5c-2e50-4ad2-8fdc-b3847f1a7ff1"},{"id":"occ_98105474149a09ac0e7fcaf1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_1187a9eb-ebf7-4cdb-afb0-a9250dabd651","section_id":"sec_7f34bca6-4bc4-4829-a7ce-c78aa72625d1","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":59,"end":61,"exact":"推論","quote":"考える\n  ↓\nツールを選ぶ\n  ↓\nAPIを呼ぶ\n  ↓\n結果を読む\n  ↓\n次の行動を決める\n  ↓\nまた推論する\n```","quote_start":4,"quote_end":67,"text_sha256":"9370a7b9136395c1af4237ccba8c7ff4e5a6bf80b947ab97337d23d44fc90822","block_sha256":"9370a7b9136395c1af4237ccba8c7ff4e5a6bf80b947ab97337d23d44fc90822","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_1187a9eb-ebf7-4cdb-afb0-a9250dabd651"},{"id":"occ_a5e1e3c75f3a9c9db6d4a7ff","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_11d70e29-019e-42ae-b326-d2f75344147c","section_id":"sec_59c2ad32-eafc-4c72-8ab0-1cb60d2ea8c3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"LPUクラウドの中心は、**学習ではなく推論**です。","quote_start":0,"quote_end":27,"text_sha256":"2dd5fc4fbcd5db91e4ea0b56fa4b3eadc0332358fb3fd9ebe9b05d34ce5ff8e9","block_sha256":"2dd5fc4fbcd5db91e4ea0b56fa4b3eadc0332358fb3fd9ebe9b05d34ce5ff8e9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_11d70e29-019e-42ae-b326-d2f75344147c"},{"id":"occ_53f34b7529029cfe4714c146","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_11d96fe9-96b7-420c-9b96-5fd79528d5bd","section_id":"sec_f859a282-7742-40dc-98c9-bdbf5336622e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":69,"end":71,"exact":"推論","quote":"NVIDIAはもはや「GPUを売って終わり」ではなく、**GPUをどうクラウド資源として詰め込むか、どう低遅延推論を回すか、どうネットワークまで含めて最適化するか**に進んでいます。","quote_start":14,"quote_end":105,"text_sha256":"64837c6eb5001f69f6a9ae020bdc28669df9a2b7aebef623b4c7c35a21c540ab","block_sha256":"64837c6eb5001f69f6a9ae020bdc28669df9a2b7aebef623b4c7c35a21c540ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_11d96fe9-96b7-420c-9b96-5fd79528d5bd"},{"id":"occ_4487a1a3afd1774bcca9e3f7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_1214813e-0163-4061-a060-e6d3b763c604","section_id":"sec_d0aef33e-d23a-416c-875c-f996b91b97c2","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":22,"end":24,"exact":"推論","quote":"用途LPUクラウドとの相性LLMリアルタイム推論非常に良いチャットAI良い音声対話AI良い低遅延エージェント良いコーディング補助良いAPI型LLM推論良い大規模学習基本的に不向き画像生成・動画生成GPUほど汎用ではない任意のCUDAアプリ不向き研究","quote_start":0,"quote_end":124,"text_sha256":"16339a97eeaa9361c4bc9a94f07fc2a05613ec87d35df8972773c132472742d8","block_sha256":"16339a97eeaa9361c4bc9a94f07fc2a05613ec87d35df8972773c132472742d8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_1214813e-0163-4061-a060-e6d3b763c604"},{"id":"occ_14d3848ea4eb217f4bb2cfee","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_12355d16-2b2f-4bc3-94c2-c7d1ce7a96ec","section_id":"sec_3f6b0c71-ab05-477d-a481-8ee0a78eefae","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"長所は、多数の小さい推論や軽量ジョブを詰め込めることです。  \n短所は、メモリ分離やfault isolationが弱くなりやすいことです。NVIDIA GPU Operatorの説明でも、time-slicingはより多く","quote_start":0,"quote_end":112,"text_sha256":"b0976e57c200a40f1dfa040859a90d2441d5d2458d8f2c4cff896092c4588d90","block_sha256":"b0976e57c200a40f1dfa040859a90d2441d5d2458d8f2c4cff896092c4588d90","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_12355d16-2b2f-4bc3-94c2-c7d1ce7a96ec"},{"id":"occ_678b3845486741876a73c405","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_17d752a6-b616-41d2-b34e-e3eb4b8340ee","section_id":"sec_a37d9e6f-78cd-4295-a307-8ed1497ab91e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"めに必要なGPU数は減ります。**  \nvLLMやDistServeのような技術により、同じGPU枚数で数倍の推論を処理できる可能性があります。([arXiv](https://arxiv.org/abs/2309.06180?utm_source=chatgpt.com))","quote_start":27,"quote_end":165,"text_sha256":"f31187988c0bad93c18623da2a9b19fe9ad387ac9dd381858aa7b9a33183b218","block_sha256":"f31187988c0bad93c18623da2a9b19fe9ad387ac9dd381858aa7b9a33183b218","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_17d752a6-b616-41d2-b34e-e3eb4b8340ee"},{"id":"occ_58a1df0347c3ac49aaef5ed9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_17e8e0e2-a146-42e4-8faf-048e2d61d69b","section_id":"sec_7e6570d5-47c1-4c8d-8c40-b67e49433c73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"AMDも2026年第1四半期決算で、推論とagentic AIが高性能CPUとアクセラレータ需要を押し上げていると述べています。([Advanced Micro Devices, Inc.](https://ir.amd.com/news-","quote_start":0,"quote_end":120,"text_sha256":"94c955c1b082526905aa497894e43f7c1168a13180525cc2b8f9c9afac005c57","block_sha256":"94c955c1b082526905aa497894e43f7c1168a13180525cc2b8f9c9afac005c57","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_17e8e0e2-a146-42e4-8faf-048e2d61d69b"},{"id":"occ_c259080c2949b2fab4cc2889","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_199ce646-6c41-494c-a42a-4c793aa2e027","section_id":"sec_b24b777b-a086-4096-a6f7-196e819d6c4d","layer":"code","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":108,"end":110,"exact":"推論","quote":"キュリティ\n  - エージェント管理\n  - クラウド制御\n\nGPUクラウド\n  - 大規模学習\n  - 汎用推論\n  - 画像生成\n  - 動画生成\n  - VLA\n  - 科学計算\n  - マルチモーダルAI\n\nTPUクラウド\n  - Google型大規模AI\n  - Transformer学習\n  - 高効","quote_start":53,"quote_end":210,"text_sha256":"a7d347f909b3b572f523eb3eab756432a28522cce72cb54f22bdabb6210127c6","block_sha256":"a7d347f909b3b572f523eb3eab756432a28522cce72cb54f22bdabb6210127c6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_199ce646-6c41-494c-a42a-4c793aa2e027"},{"id":"occ_0097951cc30243e0535259a0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_19b4d009-8f4d-40d4-892a-25ee2089861d","section_id":"sec_215e4155-9814-4b27-8f49-1b247c3e1472","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":65,"end":73,"exact":"KV cache","quote":"Uマルチテナント\n- 任意のCUDA workloadを安全に細かく共有\n- GPUメモリの完全な仮想化\n- KV cacheのクラスタ横断管理\n- prefill/decode/KV/通信を統合したクラスタOS\n- SLOベースで自動的にモデル配置を変える仕組み\n- GPUごとの本当の原価計算\n- サイドチャネル対策\n- ","quote_start":10,"quote_end":173,"text_sha256":"118d431d77ffd4d4bf54506c5884ddd02d1909bafb2459054251a0e1cabe974f","block_sha256":"118d431d77ffd4d4bf54506c5884ddd02d1909bafb2459054251a0e1cabe974f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_19b4d009-8f4d-40d4-892a-25ee2089861d"},{"id":"occ_a310054de8858726e5825b6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_1afbb5e6-b6e8-4b3b-85e4-0d07ee384422","section_id":"sec_e5e44156-0b06-4dce-8372-0aca3f2d053a","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":7,"end":14,"exact":"prefill","quote":"## \\2. prefill / decode分離","quote_start":0,"quote_end":25,"text_sha256":"42c9e1f7f611f26f354eaf51288042a4ed77ce5fac406bd074389b3d03172e82","block_sha256":"42c9e1f7f611f26f354eaf51288042a4ed77ce5fac406bd074389b3d03172e82","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_1afbb5e6-b6e8-4b3b-85e4-0d07ee384422"},{"id":"occ_248e02774cc3b4f610b5e6bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_1c32f2e9-162b-4d08-b396-5ea7a72103ed","section_id":"sec_3f6b0c71-ab05-477d-a481-8ee0a78eefae","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","decode"],"evidence":{"text_basis":"markdown","start":50,"end":56,"exact":"decode","quote":"```\n同じモデルを複数顧客で共有\n同じsystem promptのprefix cacheを共有\ndecode中のリクエストをcontinuous batchingで混ぜる\n空いたHBMにKV cacheを詰める\n低優先度batchを余剰時間に流す\n```","quote_start":0,"quote_end":129,"text_sha256":"3c97c9daa8d06e153a0560b04c712206148d9bd83c5a5d322d942354d10b8bcc","block_sha256":"3c97c9daa8d06e153a0560b04c712206148d9bd83c5a5d322d942354d10b8bcc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_1c32f2e9-162b-4d08-b396-5ea7a72103ed"},{"id":"occ_2e5e73bc31736a603321d84e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_1cb6e260-501a-49c9-be9c-c644e6ae2998","section_id":"sec_9869513e-af53-488b-b9ef-2846e7df2dff","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":75,"end":83,"exact":"KV cache","quote":"リ + ストレージ + 通信量」でした。  \nGPUクラウドでは「GPU時間 + HBM + token + KV cache + SLO」になります。","quote_start":20,"quote_end":96,"text_sha256":"16d67251d79e6ed7e246548df33d8b20e4b6f7c2ef381511509d4e180cb3dd02","block_sha256":"16d67251d79e6ed7e246548df33d8b20e4b6f7c2ef381511509d4e180cb3dd02","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_1cb6e260-501a-49c9-be9c-c644e6ae2998"},{"id":"occ_af2763b0de6e8c88f1ab8899","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_20b61fda-c874-4d7b-9633-100fe4260b5d","section_id":"sec_223083e1-3474-4d4e-8cc0-f1c1bda96df8","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"種類得意領域苦手領域位置づけGPUクラウド学習、推論、画像、動画、HPC、汎用AI電力・コスト・利用率最も汎用的なAIクラウドTPUクラウド大規模AI学習、Google系AI基盤、TransformerCUDA資産との互換性Google型の専用AIクラ","quote_start":0,"quote_end":126,"text_sha256":"acf81a1ff6ea5ab181198e567e9c55a0dfa2d076de8f415ba3e48a376ff01e20","block_sha256":"acf81a1ff6ea5ab181198e567e9c55a0dfa2d076de8f415ba3e48a376ff01e20","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_20b61fda-c874-4d7b-9633-100fe4260b5d"},{"id":"occ_0dd60f703062187b84a49acf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_217dfc90-4cfe-45d9-9822-371f422d20f3","section_id":"sec_38fea9f4-4c5a-4b32-bd81-225d5d883d90","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"低遅延を守るためには、GPUのバッチ推論のように「待たせてまとめて処理する」ことが難しいです。  \nそのため、フリート全体を常時80〜90%で回すより、**余裕を持って50〜70%台で低遅延を維持する**方が現実的です。","quote_start":0,"quote_end":110,"text_sha256":"9eefa49d2f5a2eab1851dd8a93bfdc30113eac62d20d3ce6f67717a9cf2313e7","block_sha256":"9eefa49d2f5a2eab1851dd8a93bfdc30113eac62d20d3ce6f67717a9cf2313e7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_217dfc90-4cfe-45d9-9822-371f422d20f3"},{"id":"occ_21f8cf68f6b7b3c7ba0d9c30","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_24400968-b541-4249-8c14-e3548be4305d","section_id":"sec_58745122-69e4-4bc2-ae16-3bae5e2bfd75","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":98,"end":100,"exact":"推論","quote":"  ↓\nコードを書く\n  ↓\nブラウザを操作する\n  ↓\n外部APIを呼ぶ\n  ↓\n結果を比較する\n  ↓\n再推論する\n  ↓\nレポートや画像や動画を作る\n  ↓\nユーザーに返す\n```","quote_start":43,"quote_end":136,"text_sha256":"ed32a4dc8676e886a569b2f13f5e36b87e53e6bb99b3511257ff236a36c45dcf","block_sha256":"ed32a4dc8676e886a569b2f13f5e36b87e53e6bb99b3511257ff236a36c45dcf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_24400968-b541-4249-8c14-e3548be4305d"},{"id":"occ_03c901cd4b19c7525863ab5a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_248a61f1-85f3-4b4c-95e2-f0bc78e76ef2","section_id":"sec_4fad2077-ba11-41e3-b84f-692496cb220a","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":139,"end":141,"exact":"推論","quote":"\n  → GPUクラウド\n\n低遅延チャット、音声対話、軽量エージェント\n  → LPUクラウド\n\n企業向けAI推論API\n  → GPU / LPU / TPU の混在\n\nフィジカルAI・VLA学習\n  → GPUクラウド中心、一部TPU/ASIC\n\nエッジAI\n  → NPU / 専用ASIC / 小型GPU\n","quote_start":84,"quote_end":241,"text_sha256":"2456538722254db1f69cc8675621c32e6331c79dbbb6b30dce00da0586554513","block_sha256":"2456538722254db1f69cc8675621c32e6331c79dbbb6b30dce00da0586554513","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_248a61f1-85f3-4b4c-95e2-f0bc78e76ef2"},{"id":"occ_bf8bebb318f7ca2f3cdf0907","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_24ee6f62-0140-4cc0-9c72-d4f34359437c","section_id":"sec_b3015863-c634-42f8-bf90-57c765dba5e5","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"prefill","quote":"Sarathi-Serveは、prefillをチャンク化してdecodeを止めないスケジューリングを行います。論文では、Mistral-7BでvLLM比2.6倍、Yi-34Bで最大3.7倍、Falcon-180Bでは最大5.6倍のserving","quote_start":0,"quote_end":122,"text_sha256":"abe1828fb02a8fa630c2e46acabf8560d3638a4e5a2866aafe734f4c4da14b5b","block_sha256":"abe1828fb02a8fa630c2e46acabf8560d3638a4e5a2866aafe734f4c4da14b5b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_24ee6f62-0140-4cc0-9c72-d4f34359437c"},{"id":"occ_9c0f429af386021bfde2a4de","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_255fbd60-9538-4212-a791-d0007729b269","section_id":"sec_36a188e4-7f51-4d03-afae-df59351b5013","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"```\nGPUクラウド\n  - LLM推論\n  - reasoning\n  - multimodal生成\n  - 動画生成\n  - VLA学習\n  - embedding/rerank\n  - シミュレーション\n\nCPUクラウド\n  - AP","quote_start":0,"quote_end":121,"text_sha256":"58bf53de5380142d923686fa53c77891e7ba21a10a89dd08ef7b7e387321cd97","block_sha256":"58bf53de5380142d923686fa53c77891e7ba21a10a89dd08ef7b7e387321cd97","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_255fbd60-9538-4212-a791-d0007729b269"},{"id":"occ_1060c101b688055b28291870","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_29fec858-7c63-4a87-a6f7-2240aac86e04","section_id":"sec_17d19a96-2685-4011-8824-dfda4605ffd2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"LPUは、ざっくり言うと **LLM推論、特にトークン生成を高速・低遅延・予測可能に行うための専用チップ**です。","quote_start":0,"quote_end":57,"text_sha256":"40b0233aa141bfa919886e2264a120081f0389ead4ac1978e2eea1694360d8f5","block_sha256":"40b0233aa141bfa919886e2264a120081f0389ead4ac1978e2eea1694360d8f5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_29fec858-7c63-4a87-a6f7-2240aac86e04"},{"id":"occ_915bf786e8d687060484ea24","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2a570052-64e2-4277-9f11-44b132fdfcb8","section_id":"sec_08b4a9e3-3511-4b6c-905e-d0a7356407e5","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"*GroqCloud** です。GroqはLPU、Language Processing Unitを使ったAI推論クラウドを提供しており、GroqCloudは高速LLM推論、OpenAI互換API、スケーラブルな推論基盤として説明されています。([GroqCloud](https://console.groq.c","quote_start":6,"quote_end":163,"text_sha256":"9d10cac62589127fb3f4da7f9ca9ffcf4065c4ff1cd5abb63a0ca2b20445b647","block_sha256":"9d10cac62589127fb3f4da7f9ca9ffcf4065c4ff1cd5abb63a0ca2b20445b647","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2a570052-64e2-4277-9f11-44b132fdfcb8"},{"id":"occ_a947fa76039fc1f6ffb9a980","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2ad0b374-7cb1-412f-9d6b-990fd734b2dc","section_id":"sec_8c386d0d-3385-4a7f-8017-702e5566fdbf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"LPUはGPUやTPUと違い、主に**推論特化**です。特に、音声対話、リアルタイムチャット、AITuber、カスタマーサポート、軽量エージェントのように、1トークンごとの応答速度が重要な用途に向いています。","quote_start":0,"quote_end":104,"text_sha256":"0346996695f22f54a03f4bd70d45e5afcc70e9d39048f985b0875ff5d81c9937","block_sha256":"0346996695f22f54a03f4bd70d45e5afcc70e9d39048f985b0875ff5d81c9937","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2ad0b374-7cb1-412f-9d6b-990fd734b2dc"},{"id":"occ_33f0072d71d3bb25c73ebfb2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2aede369-ff01-4a78-a78f-069c28b03c87","section_id":"sec_80dd1de7-9eff-436b-aaa3-157453e9bebb","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":21,"end":29,"exact":"KV cache","quote":"> **GPUをトークン単位、モデル単位、KV cache単位、SLO単位で動的に共有・課金・監視する段階**","quote_start":0,"quote_end":55,"text_sha256":"956c9e0f5138827f26e66c7a2ffd61926e2bb37558a408d0ff3fc1b6861720c1","block_sha256":"956c9e0f5138827f26e66c7a2ffd61926e2bb37558a408d0ff3fc1b6861720c1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2aede369-ff01-4a78-a78f-069c28b03c87"},{"id":"occ_4349d83744409e4cbb8693e3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2b5ef93d-6074-4fb4-b5ba-d726d625b269","section_id":"sec_a7efb4db-4fd8-48f3-8c3d-f7588df3d8dc","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":17,"end":25,"exact":"KV cache","quote":"- GPUが通信待ちしている\n- KV cacheでHBMが無駄になっている\n- prefillとdecodeが干渉している\n- 小さい推論リクエストをうまくバッチ化できない\n- モデルごとにGPUを固定してしまい空き時間が出る\n- マルチテナント化","quote_start":0,"quote_end":125,"text_sha256":"bd0edc17ebe03e80244b0c356ac84435838f2218f92c3ae8a0599646c8d62221","block_sha256":"bd0edc17ebe03e80244b0c356ac84435838f2218f92c3ae8a0599646c8d62221","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2b5ef93d-6074-4fb4-b5ba-d726d625b269"},{"id":"occ_c55966ec23b3f379615dc1ea","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2d29625f-8268-48c6-9e6c-f6e5863a7b50","section_id":"sec_7578d2e0-ef74-4382-b7cf-ad9d1c2952f3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"この段階は、かなり早く成熟すると思います。  \n理由は、LLM推論のコスト削減効果が巨大だからです。","quote_start":0,"quote_end":50,"text_sha256":"72ab8d775a44a96755e922629927d8151e3c8a0b52a914fff0ffc4308f5ddaa5","block_sha256":"72ab8d775a44a96755e922629927d8151e3c8a0b52a914fff0ffc4308f5ddaa5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2d29625f-8268-48c6-9e6c-f6e5863a7b50"},{"id":"occ_e670243e01d38b027998dc23","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2e1a6cc9-33fd-4c6e-9643-bb703ab02eaa","section_id":"sec_c32c7c49-725a-4976-8a9d-913de857bee5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":22,"end":24,"exact":"推論","quote":"なので、GPUクラウド成熟によって、**AI推論そのもののボトルネックは緩和される**。  \nしかし、**エージェント社会を動かす周辺インフラ需要はむしろ増える**。","quote_start":0,"quote_end":83,"text_sha256":"f0f78d9125e9619e4b9783ead207fea12819caad91764adc47c74bfee467fd68","block_sha256":"f0f78d9125e9619e4b9783ead207fea12819caad91764adc47c74bfee467fd68","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2e1a6cc9-33fd-4c6e-9643-bb703ab02eaa"},{"id":"occ_d4d073819997a0d03bb2de6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2e7943cc-162e-4f29-8541-740e8aa1ad5d","section_id":"sec_e2c11f6d-3238-4551-964e-bc128f1dc940","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"という仕事では、GPU推論だけでなく、CPU・DB・ネットワーク・セキュリティが大量に必要です。","quote_start":0,"quote_end":48,"text_sha256":"c055a5a1d6952b10d272a90932b35f9200f870fa40ab4464d7601faa6e72800a","block_sha256":"c055a5a1d6952b10d272a90932b35f9200f870fa40ab4464d7601faa6e72800a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2e7943cc-162e-4f29-8541-740e8aa1ad5d"},{"id":"occ_c5fa4b4cd14976456210aeac","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2f547408-242d-4d9c-9dd8-61dce2ebfda6","section_id":"sec_efae00ac-e1e8-4909-902d-102abd79a201","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":19,"end":26,"exact":"prefill","quote":"DistServeはこの問題に対して、prefillとdecodeを分離して別々に最適化する方式を提案しています。既存LLM servingでは両者を同じGPU群で処理するため干渉が起きるが、DistServeは両フェーズを分離してgoodputを改善","quote_start":0,"quote_end":126,"text_sha256":"92b9ed3df72a139fbe59d1a903e1dfe5b5c423b5312b3c7ae462f11c425f8b34","block_sha256":"92b9ed3df72a139fbe59d1a903e1dfe5b5c423b5312b3c7ae462f11c425f8b34","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2f547408-242d-4d9c-9dd8-61dce2ebfda6"},{"id":"occ_2b413f056bd4e9660020e433","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_2fe68ad3-693d-440c-a7c7-03009330231f","section_id":"sec_45280871-ea21-4d3c-99e6-94cb0ec44aff","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"*GroqCloud** です。GroqはLPU、Language Processing Unitを使ったAI推論クラウドを提供しており、GroqCloudは高速LLM推論、OpenAI互換API、スケーラブルな推論基盤として説明されています。([GroqCloud](https://console.groq.c","quote_start":6,"quote_end":163,"text_sha256":"9d10cac62589127fb3f4da7f9ca9ffcf4065c4ff1cd5abb63a0ca2b20445b647","block_sha256":"9d10cac62589127fb3f4da7f9ca9ffcf4065c4ff1cd5abb63a0ca2b20445b647","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_2fe68ad3-693d-440c-a7c7-03009330231f"},{"id":"occ_9051f167bc49ce4ce770b036","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3081fc7a-cac6-4023-9d2b-aeaf509536ba","section_id":"sec_0ddaeb93-1732-4096-96c9-c776a8183e73","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":49,"end":57,"exact":"KV cache","quote":"つまり、GPUクラウドは2030年代に向けて、**GPUをただ貸すサービス**から、**トークン、KV cache、GPUメモリ、SLO、モデル常駐、推論フェーズ単位で管理するクラウドOS**へ進化していくと考えられます。","quote_start":0,"quote_end":111,"text_sha256":"b7ec3e2c4c47700ac18106d7cff09839d8edc557000302335746a8d8891c90e5","block_sha256":"b7ec3e2c4c47700ac18106d7cff09839d8edc557000302335746a8d8891c90e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3081fc7a-cac6-4023-9d2b-aeaf509536ba"},{"id":"occ_0ebb8ef7938da34d5eb186e2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_35300359-9921-408f-b8ed-2e7978d764e7","section_id":"sec_8c386d0d-3385-4a7f-8017-702e5566fdbf","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"```\n巨大モデル学習：GPU / TPU\n汎用推論：GPU / TPU\n低遅延会話：LPU\n画像・動画生成：GPU\nGoogle系大規模AI：TPU\nAIエージェントの軽量推論：GPU / LPU\n企業業務AI：CPU + GPU/TPU/LPU +","quote_start":0,"quote_end":126,"text_sha256":"d50c4e8a37f1f1994764c68c51402d2883f1f004efee01f119f3f4c75b51b4ea","block_sha256":"d50c4e8a37f1f1994764c68c51402d2883f1f004efee01f119f3f4c75b51b4ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_35300359-9921-408f-b8ed-2e7978d764e7"},{"id":"occ_1a1331cc242bd124b7a40157","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_37b3c60f-5010-48b6-a18e-782bdeddfeed","section_id":"sec_2132be84-3261-41a6-a850-a4634816410b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":44,"end":46,"exact":"推論","quote":"たとえば報道では、NVIDIAのVera Rubin NVL72はBlackwell比で推論性能を大きく高め、トークンあたりコストを下げ、必要GPUを減らす方向の設計だとされています。([Tom's Hardware](https://www.tomshardware.com/pc-comp","quote_start":0,"quote_end":146,"text_sha256":"863759f0f50a53845035339d0b4ba2d98a1d502989b1abb6f90ac4d33e3774c0","block_sha256":"863759f0f50a53845035339d0b4ba2d98a1d502989b1abb6f90ac4d33e3774c0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_37b3c60f-5010-48b6-a18e-782bdeddfeed"},{"id":"occ_cadc8040e1b35dc6b1faf8a4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_384ea9b9-a8ba-4648-befe-e14405bb07e8","section_id":"sec_34c6259f-6349-4bc3-9ef7-3f1311c73105","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"学習は「AIを育てる」。  \n推論は「育ったAIを使う」。","quote_start":0,"quote_end":29,"text_sha256":"ad1350bd11544123b0ef4d3286b0b1508eb7877b93929820a57a7ae0e81696e7","block_sha256":"ad1350bd11544123b0ef4d3286b0b1508eb7877b93929820a57a7ae0e81696e7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_384ea9b9-a8ba-4648-befe-e14405bb07e8"},{"id":"occ_196298f8cb595dd528721d31","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_38a5335a-28fc-47c6-aad4-7a7f0f626486","section_id":"sec_56739c37-b595-4789-84f9-79ef964cef67","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"用途TPUクラウドで可能かLLM学習可能LLM推論可能ファインチューニング可能画像生成モデル可能CNN/推薦モデル可能大規模分散学習可能JAX/TensorFlow系ワークロード得意CUDA前提のGPUアプリ苦手","quote_start":0,"quote_end":106,"text_sha256":"52e57bf45f84e89aedb2a9c7472de4c16031a6572d9a9ee487c13cad58a66bd5","block_sha256":"52e57bf45f84e89aedb2a9c7472de4c16031a6572d9a9ee487c13cad58a66bd5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_38a5335a-28fc-47c6-aad4-7a7f0f626486"},{"id":"occ_f987009c9a64f30b2c68a432","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_38bc6cd5-7e9f-4086-affd-bb5f5f70a091","section_id":"sec_67c7d199-a2f4-4058-b6a0-18b296e20e76","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"```\nGPUクラウド成熟\n  ↓\n推論コスト低下\n  ↓\nAIエージェント利用増加\n  ↓\nAPI・DB・認証・ログ・監査・課金が増える\n  ↓\nCPU需要は高止まり\n```","quote_start":0,"quote_end":88,"text_sha256":"eb897f05d2d0b50d937fd0a56c9e9d148be677048fba5dada9ff819787610452","block_sha256":"eb897f05d2d0b50d937fd0a56c9e9d148be677048fba5dada9ff819787610452","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_38bc6cd5-7e9f-4086-affd-bb5f5f70a091"},{"id":"occ_d20322776f6b3177021a0e5d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3a3aad82-206e-44fe-a231-a63aae699206","section_id":"sec_d4de89a5-0d88-41d1-998b-a7b33adb771f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"**GPUクラウド**とは、クラウド上でGPUを使い、AI学習・AI推論・画像生成・動画生成・科学計算などを行う仕組みです。","quote_start":0,"quote_end":62,"text_sha256":"3b1dbf688cb764e843da7b4b31268fcea876c24e044c34c977fd041a33ac27a5","block_sha256":"3b1dbf688cb764e843da7b4b31268fcea876c24e044c34c977fd041a33ac27a5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3a3aad82-206e-44fe-a231-a63aae699206"},{"id":"occ_b64381bc096807b579140d89","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3b6ede4f-2161-485e-967a-f511f40c0d3c","section_id":"sec_0ddaeb93-1732-4096-96c9-c776a8183e73","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"DistServeは、LLM推論のprefillとdecodeを分離し、それぞれを別GPUに割り当てることで干渉を減らします。論文では、既存方式より最大7.4倍多いリクエスト、または12.6倍厳しいSLOを満たせると報告されています","quote_start":0,"quote_end":116,"text_sha256":"b84d89169ffebe973ab716979bcfe4b481e409fbd086da3af36ca51f6163dc3c","block_sha256":"b84d89169ffebe973ab716979bcfe4b481e409fbd086da3af36ca51f6163dc3c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3b6ede4f-2161-485e-967a-f511f40c0d3c"},{"id":"occ_5b02d7986affa4b5bc56f1e9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3c499ead-f3f8-48ff-a138-a9ae04d5d897","section_id":"sec_464705c9-5456-4a75-8565-30bf403331fc","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"なぜならLLM推論では、","quote_start":0,"quote_end":12,"text_sha256":"775862397a34cfd076dc1375143d4ffc1ccbbb902257b9fa0456fc72f1bd878d","block_sha256":"775862397a34cfd076dc1375143d4ffc1ccbbb902257b9fa0456fc72f1bd878d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3c499ead-f3f8-48ff-a138-a9ae04d5d897"},{"id":"occ_8345cb43af0b95b5270a6d56","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3d6eb668-1699-44ae-a3b0-dcc19c3119bd","section_id":"sec_8064b9c8-b582-4cc3-bb35-b46da0b29f1b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"emini系  \n> Anthropicのような大口AI企業  \n> Google Cloud上の大規模学習・推論  \n> 電力効率重視のAIデータセンター","quote_start":16,"quote_end":94,"text_sha256":"a2e29932f329a381e72a063bae0714672c90153af1f7a27289747855f6e71276","block_sha256":"a2e29932f329a381e72a063bae0714672c90153af1f7a27289747855f6e71276","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3d6eb668-1699-44ae-a3b0-dcc19c3119bd"},{"id":"occ_3c8d0559c7bbfacfb1320e6e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_3eb57a22-1e54-4eb0-a690-0d2c2795adbc","section_id":"sec_9869513e-af53-488b-b9ef-2846e7df2dff","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":24,"end":32,"exact":"KV cache","quote":"## \\8. 課金 → GPU秒、token秒、KV cache秒、推論SLO課金","quote_start":0,"quote_end":41,"text_sha256":"addf946ef9c0053fe4068a66551992a77afa9b9a8d5930addf766c67982cffcc","block_sha256":"addf946ef9c0053fe4068a66551992a77afa9b9a8d5930addf766c67982cffcc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_3eb57a22-1e54-4eb0-a690-0d2c2795adbc"},{"id":"occ_8957b0506dd3cbf271bae1e0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_453baa64-d9de-47e6-8fa0-b4cc79873c7b","section_id":"sec_c36d4c47-1622-4afc-81a9-fc71567aa0f3","layer":"code","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":13,"end":21,"exact":"KV cache","quote":"```\nモデル\nトークン\nKV cache\nGPUメモリ\nprefill\ndecode\n推論SLO\n```","quote_start":0,"quote_end":53,"text_sha256":"0839c416becd0b4b93a1d9763de800d1e717a491f1d4d6718b045d4ef0a60018","block_sha256":"0839c416becd0b4b93a1d9763de800d1e717a491f1d4d6718b045d4ef0a60018","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_453baa64-d9de-47e6-8fa0-b4cc79873c7b"},{"id":"occ_6452821f1cdb1ccaaf3d3cc6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_466722cc-6d9f-49d1-a7af-918c6e0e0ca8","section_id":"sec_eee898d1-c2cb-4447-b7e5-fb9e261cab0a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":69,"end":71,"exact":"推論","quote":"、vLLM、SGLang、TensorRT-LLMなどを置き換えるのではなく、それらをデータセンター規模の分散推論システムとして協調させるオーケストレーション層です。GitHub上でも、disaggregated serving、intelligent routing、multi-tier KV caching、","quote_start":14,"quote_end":171,"text_sha256":"8b2fd97489c21c58ce4eb0fc07f7abd38eae6da47019bb2271647dc7bf8fbfb2","block_sha256":"8b2fd97489c21c58ce4eb0fc07f7abd38eae6da47019bb2271647dc7bf8fbfb2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_466722cc-6d9f-49d1-a7af-918c6e0e0ca8"},{"id":"occ_5e0458488af9e87fc530db28","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_478603b2-41f7-4f2f-b38b-4133c49b5ce6","section_id":"sec_fbf61cc5-9f59-47ef-81e8-5b283a84043d","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"```\nNVIDIA GPU搭載インスタンス\nAI学習用クラスタ\n推論API基盤\n画像生成サーバー\n動画生成サーバー\n```","quote_start":0,"quote_end":62,"text_sha256":"8d90cc44ad4b594711a26e6e8a242bac67733bd5ad5b8bf2cc8055471999abfa","block_sha256":"8d90cc44ad4b594711a26e6e8a242bac67733bd5ad5b8bf2cc8055471999abfa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_478603b2-41f7-4f2f-b38b-4133c49b5ce6"},{"id":"occ_d936de02c3817922e5bb4e29","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4918c7fc-a59b-4484-8ae6-73ffd2ba502d","section_id":"sec_e5e44156-0b06-4dce-8372-0aca3f2d053a","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"エージェントは長い入力を読み、長い出力を出し、途中で何度も再推論します。  \nそのため、prefillとdecodeを分けて最適化する効果が大きいです。","quote_start":0,"quote_end":76,"text_sha256":"2d8bfbde0920c4121be89f06d5d3e7d756d9b6539aac01f1dffe6147d0cda19e","block_sha256":"2d8bfbde0920c4121be89f06d5d3e7d756d9b6539aac01f1dffe6147d0cda19e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4918c7fc-a59b-4484-8ae6-73ffd2ba502d"},{"id":"occ_3b578e0c7e97fb902e1f849b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4acf70b3-b162-4a7b-ae1e-87030ae1046d","section_id":"sec_7f34bca6-4bc4-4829-a7ce-c78aa72625d1","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"AIエージェントは、1回の巨大推論だけではなく、小さな推論を何度も繰り返します。","quote_start":0,"quote_end":40,"text_sha256":"70a8400368b56164e6fa4dd5fc4b59796f7dc2b092f47c04f714fd7b9fc74ae0","block_sha256":"70a8400368b56164e6fa4dd5fc4b59796f7dc2b092f47c04f714fd7b9fc74ae0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4acf70b3-b162-4a7b-ae1e-87030ae1046d"},{"id":"occ_1686c8012e63680ba5bf79ca","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4b27d0da-6c33-4ba2-b221-b745b5ac4474","section_id":"sec_73f53de8-0e73-446a-8f6d-0cbb434a19a5","layer":"body","character_id":null,"count":4,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":11,"end":18,"exact":"prefill","quote":"DistServeは、prefillとdecodeを同じGPUで処理すると互いに干渉すると見て、prefill用GPUとdecode用GPUを分けます。TTFTとTPOTの要求を別々に最適化し、論文では既存方式より最大7.4倍多いリクエ","quote_start":0,"quote_end":118,"text_sha256":"30290e3e29909372f72cdb2b84bf0524775e0289fd03ca06b2b6c5eec2cf9e79","block_sha256":"30290e3e29909372f72cdb2b84bf0524775e0289fd03ca06b2b6c5eec2cf9e79","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4b27d0da-6c33-4ba2-b221-b745b5ac4474"},{"id":"occ_5d11999a67e7083d9f95f593","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4b608baf-f259-4120-b7e2-0fa6dea597e4","section_id":"sec_0ab28b0f-3669-4f74-a3ce-afe5cff0da16","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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\nその結果、同じエージェント数を動かすために必要なGPU枚数は減る可能性があります。","quote_start":0,"quote_end":89,"text_sha256":"ef5add80ebcbcba4def7dd630e646274bd91f330730cb9940a0ae090315bf986","block_sha256":"ef5add80ebcbcba4def7dd630e646274bd91f330730cb9940a0ae090315bf986","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4db42485-9a38-4360-b7d2-ea3dcc91990f"},{"id":"occ_2e1fb54430ce42595e5b57c5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4e4b1edb-f7db-4752-a833-2fec78e43a7c","section_id":"sec_497bb58a-a483-4a7e-a196-9391220a7942","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":29,"end":31,"exact":"推論","quote":"TPUはGPUより汎用性は低いですが、**大規模AI学習・推論に特化した垂直統合基盤**としては非常に強いです。","quote_start":0,"quote_end":56,"text_sha256":"1ead83cfad0d307e7e7eaddec27adc4bb65c3c38ac1d5260bcb0afdcc239954a","block_sha256":"1ead83cfad0d307e7e7eaddec27adc4bb65c3c38ac1d5260bcb0afdcc239954a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4e4b1edb-f7db-4752-a833-2fec78e43a7c"},{"id":"occ_68f8128983fe2769018af3dc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4eb81663-e69c-42dd-b92f-ec6704e7f366","section_id":"sec_0ba18597-c60c-42c3-916e-a2e9292e71f6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"## \\3. 推論前後処理","quote_start":0,"quote_end":13,"text_sha256":"5510814ea6e8d38260154fb6b7349fca012ed76c9bb0f360098df76dfe4b0ca2","block_sha256":"5510814ea6e8d38260154fb6b7349fca012ed76c9bb0f360098df76dfe4b0ca2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4eb81663-e69c-42dd-b92f-ec6704e7f366"},{"id":"occ_74270201cdafff210ac82903","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_4f9a1b2a-65a5-4777-956f-ab5a56bc4617","section_id":"sec_d6048052-b453-45bf-b44f-f6de73b7c164","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":54,"end":62,"exact":"KV 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cache、prefill、decode、ネットワーク通信、モデル重み、バッチング、レイテンシ制約が絡み合います。","quote_start":0,"quote_end":114,"text_sha256":"3f0024cab3990c51cce68c3641491b9cac4276fd584f26f0a1aab6c31a9e753f","block_sha256":"3f0024cab3990c51cce68c3641491b9cac4276fd584f26f0a1aab6c31a9e753f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_4f9a1b2a-65a5-4777-956f-ab5a56bc4617"},{"id":"occ_74c44bd54f930dfdb8ab868c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5031bb37-debb-4093-b2b9-9d20a07e3dc4","section_id":"sec_407e0be8-ea08-48d5-a7ef-c4035ca4dd47","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":141,"end":143,"exact":"推論","quote":"vGPUやtime-slicingで共有する\n- vLLM、TensorRT-LLM、SGLangなどでLLM推論を高速化する\n- GPU監視、ログ、メトリクスを取る\n- CoreWeaveなどのネオクラウドがAI特化GPUクラウドを大規模運用する","quote_start":86,"quote_end":210,"text_sha256":"814aa8863e29fb0b2aa865abd394c3a5a99429715760cf8a3d5e2c39fe46f02f","block_sha256":"814aa8863e29fb0b2aa865abd394c3a5a99429715760cf8a3d5e2c39fe46f02f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5031bb37-debb-4093-b2b9-9d20a07e3dc4"},{"id":"occ_83d55e38971561317acdd312","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_52233d03-b334-4d59-b6ae-0d64a8028750","section_id":"sec_08b4a9e3-3511-4b6c-905e-d0a7356407e5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"Groqの公式ページでも、GroqCloudは開発者向けのAI推論プラットフォームで、public、private、co-cloud構成で提供可能とされています。([Groq](https://groq.com/groqcloud?utm_source=chatgp","quote_start":0,"quote_end":133,"text_sha256":"65e95febcadbe1fee6f4a46e4a3e6931b8a5ac1730dc666cedcc88cd4184f1ee","block_sha256":"65e95febcadbe1fee6f4a46e4a3e6931b8a5ac1730dc666cedcc88cd4184f1ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_52233d03-b334-4d59-b6ae-0d64a8028750"},{"id":"occ_230ad34cbd3e3abbc52b4fc8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_54e6e83e-2971-40b5-a11c-74e5bbc8024f","section_id":"sec_7627ce8f-acd4-4fdb-b993-42c1809a78a7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":49,"end":51,"exact":"推論","quote":"- AIデータセンター建設\n- GPUクラウド成熟前の非効率\n- AIエージェントの普及初期\n- 推論需要の爆発\n- 企業AI導入\n- sovereign AI\n- GPUホストCPU需要\n- クラウド制御CPU需要\n- Arm/EPYC/Grace/カスタムCPUの競争","quote_start":0,"quote_end":136,"text_sha256":"e465676fc7c75ee369ca3baa6b483ea27024aa6ec5abfc1539b2b2f7f9ce3db4","block_sha256":"e465676fc7c75ee369ca3baa6b483ea27024aa6ec5abfc1539b2b2f7f9ce3db4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_54e6e83e-2971-40b5-a11c-74e5bbc8024f"},{"id":"occ_dad0cb4d5e0e6f0c412ff987","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5567ae77-62a5-4a9d-947f-e6ba3f416e4e","section_id":"sec_0ddaeb93-1732-4096-96c9-c776a8183e73","layer":"body","character_id":null,"count":3,"matched_aliases":["KV 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cacheの無駄をほぼゼロに近づけ、同じレイテンシ条件で既存システムより2〜4倍のスループット改善を示したとされています。([ar","quote_start":0,"quote_end":144,"text_sha256":"247ae8837d9994249441bb5095a1c06e0ccb4aecd3dcf8c849efb2bee3da2d98","block_sha256":"247ae8837d9994249441bb5095a1c06e0ccb4aecd3dcf8c849efb2bee3da2d98","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5567ae77-62a5-4a9d-947f-e6ba3f416e4e"},{"id":"occ_599a1ddbb30b5b51ee0be9ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_56ce4246-6277-4a6e-b1e9-77d4cd8fd5eb","section_id":"sec_ffdc9937-2285-4e5b-89d4-18d96e67d197","layer":"body","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"> **TPUクラウド：すでに本格実用されている。学習・推論どちらも可能。**  \n> **LPUクラウド：すでに推論クラウドとして実用化されている。ただしGPU/TPUより用途は狭く、主に超低遅延LLM推論向け。**  \n> **GPUクラウド：最も汎用性が","quote_start":0,"quote_end":130,"text_sha256":"4aadd63deb4e9eee03c92c6089d7c81a4691da801ce42ae2b32cd3f8b9e856e5","block_sha256":"4aadd63deb4e9eee03c92c6089d7c81a4691da801ce42ae2b32cd3f8b9e856e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_56ce4246-6277-4a6e-b1e9-77d4cd8fd5eb"},{"id":"occ_998811aaceed093251f41c20","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_577cb272-0629-4b73-860e-6b2206981d9a","section_id":"sec_b303d619-174e-45e1-a89d-3a319eafef04","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"つまりTPUクラウドは、単なる「小さいAI推論用」ではなく、**巨大AIモデルの学習・推論を行うクラウド基盤**です。","quote_start":0,"quote_end":59,"text_sha256":"d4c1a3f999f5a42f36544064e9e6e8e7e515989e89b820aae31cc2c9e717a97a","block_sha256":"d4c1a3f999f5a42f36544064e9e6e8e7e515989e89b820aae31cc2c9e717a97a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_577cb272-0629-4b73-860e-6b2206981d9a"},{"id":"occ_41d83bafbf04aadf4b29a053","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_582a595b-e173-4bc0-bc59-7e5ad64c8a80","section_id":"sec_efae00ac-e1e8-4909-902d-102abd79a201","layer":"code","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":48,"end":55,"exact":"prefill","quote":"```\nどのモデルを\nどのGPUに常駐させ\nどのリクエストを\nどのタイミングでbatchに混ぜ\nprefillとdecodeをどこで処理し\nKV cacheをどこに置き\nレイテンシSLOを守るか\n```","quote_start":0,"quote_end":101,"text_sha256":"82872eddf1eba0fbc6f9ab02ea67d224e51a30e8d88dbb82474b2c97f090a0e5","block_sha256":"82872eddf1eba0fbc6f9ab02ea67d224e51a30e8d88dbb82474b2c97f090a0e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_582a595b-e173-4bc0-bc59-7e5ad64c8a80"},{"id":"occ_a06d111616b96b1eb0917fe1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5a5379a9-79cb-4d52-9386-2a8849697268","section_id":"sec_fd55f986-652f-4cc3-8cf4-54af3e755e17","layer":"code","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":91,"end":93,"exact":"推論","quote":"- 認証\n  - 課金\n  - 管理\n  - セキュリティ\n\nGPUクラウド\n  - 大規模学習\n  - 汎用推論\n  - 画像生成\n  - 動画生成\n  - VLA\n  - 科学計算\n\nTPUクラウド\n  - 大規模AI学習\n  - Google型AI基盤\n  - 高効率Transformer処理\n\nLPUクラ","quote_start":36,"quote_end":193,"text_sha256":"4849afb6ed52df3896bdc03cc3d759b3572a602f3d2bcfe313bb80724ed24d31","block_sha256":"4849afb6ed52df3896bdc03cc3d759b3572a602f3d2bcfe313bb80724ed24d31","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5a5379a9-79cb-4d52-9386-2a8849697268"},{"id":"occ_16b3582a68ec357824f1a22f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5ccfecaa-0f20-44ac-9570-be3c2a632062","section_id":"sec_c656c7fc-605f-492e-a960-66d82e74a6c5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"**推論**とは、学習済みAIモデルを使って実際に答えを出すことです。","quote_start":0,"quote_end":35,"text_sha256":"00b1822e31f0fd22ebd8c845937c38e8e388c32572c27b8fd9446bd45b1f314a","block_sha256":"00b1822e31f0fd22ebd8c845937c38e8e388c32572c27b8fd9446bd45b1f314a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5ccfecaa-0f20-44ac-9570-be3c2a632062"},{"id":"occ_b8afe8c1eae1fadfe87031d9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5d34c47d-8f24-4ca3-95f4-7801ff3b32cd","section_id":"sec_e00c5f8c-1522-4b87-ba97-46f9ec64d45f","layer":"code","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":91,"end":93,"exact":"推論","quote":"- 認証\n  - 課金\n  - 管理\n  - セキュリティ\n\nGPUクラウド\n  - 大規模学習\n  - 汎用推論\n  - 画像生成\n  - 動画生成\n  - VLA\n  - 科学計算\n\nTPUクラウド\n  - 大規模AI学習\n  - Google型AI基盤\n  - 高効率Transformer処理\n\nLPUクラ","quote_start":36,"quote_end":193,"text_sha256":"4849afb6ed52df3896bdc03cc3d759b3572a602f3d2bcfe313bb80724ed24d31","block_sha256":"4849afb6ed52df3896bdc03cc3d759b3572a602f3d2bcfe313bb80724ed24d31","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5d34c47d-8f24-4ca3-95f4-7801ff3b32cd"},{"id":"occ_1fa97f55d7983b7c1a1418aa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5d9df1c1-901e-49e1-8b0b-7937a7cf9c38","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":30,"end":38,"exact":"KV cache","quote":"- 入力トークン数\n- 出力トークン数\n- 同時会話数\n- KV cache量\n- prefill量\n- decode量\n- モデル常駐数\n- LoRA adapter数\n- priority/SLO","quote_start":0,"quote_end":100,"text_sha256":"b4ccc35d8627d181382c43dbc9d1c3db1b92af0eb78517f0c0a523491ad99cc5","block_sha256":"b4ccc35d8627d181382c43dbc9d1c3db1b92af0eb78517f0c0a523491ad99cc5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5d9df1c1-901e-49e1-8b0b-7937a7cf9c38"},{"id":"occ_0e66ec6c2251238db2f84ff9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5e0bcba9-0c2f-4510-93a2-b077e24be3e8","section_id":"sec_06d3a5f5-31cf-46c8-afab-e76693d7d652","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"弱点内容学習には向きにくい基本的に推論特化対応モデルが限られるコンパイラやランタイム対応が必要GPUほど汎用ではないCUDA資産をそのまま使えない画像・動画生成ではGPU優位マルチモーダル全般ではGPUが強いエコシステムが小さいNVIDI","quote_start":0,"quote_end":119,"text_sha256":"6b9e3af3eb060822aa454079026479b7ff261da11304efb3f52c3f6597102a70","block_sha256":"6b9e3af3eb060822aa454079026479b7ff261da11304efb3f52c3f6597102a70","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5e0bcba9-0c2f-4510-93a2-b077e24be3e8"},{"id":"occ_d14778e002535a8b5709e17b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5e29402d-6550-428e-beea-906b2708e39c","section_id":"sec_3ed2a696-c34d-42a7-8e62-75970d04cafa","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":59,"end":61,"exact":"推論","quote":"考える\n  ↓\nツールを選ぶ\n  ↓\nAPIを呼ぶ\n  ↓\n結果を読む\n  ↓\n次の行動を決める\n  ↓\nまた推論する\n```","quote_start":4,"quote_end":67,"text_sha256":"9370a7b9136395c1af4237ccba8c7ff4e5a6bf80b947ab97337d23d44fc90822","block_sha256":"9370a7b9136395c1af4237ccba8c7ff4e5a6bf80b947ab97337d23d44fc90822","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5e29402d-6550-428e-beea-906b2708e39c"},{"id":"occ_1885abed11639fc9217f10c7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_5e6c6e47-5a66-421b-ac3b-448037c407f7","section_id":"sec_e00c5f8c-1522-4b87-ba97-46f9ec64d45f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"その中でGPUは最も汎用的な中心に残り、TPUはGoogle系・大規模AIに強く、LPUは低遅延推論で存在感を持つ、という分担になると思います。","quote_start":0,"quote_end":72,"text_sha256":"1e6accfd2d2ab37210c6a334040e6538c796cb2775cfb733d7281dbba34753a6","block_sha256":"1e6accfd2d2ab37210c6a334040e6538c796cb2775cfb733d7281dbba34753a6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_5e6c6e47-5a66-421b-ac3b-448037c407f7"},{"id":"occ_11ae6c49671f0a1b3b8e11d2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_601f73aa-5777-49d6-a73a-419bb7fe39f4","section_id":"sec_34c6259f-6349-4bc3-9ef7-3f1311c73105","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"## 学習と推論の違い","quote_start":0,"quote_end":11,"text_sha256":"3f55174739d1a83521ef1ce10423e0a6c87734d4d1911b76085f3b0c6660ca52","block_sha256":"3f55174739d1a83521ef1ce10423e0a6c87734d4d1911b76085f3b0c6660ca52","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_601f73aa-5777-49d6-a73a-419bb7fe39f4"},{"id":"occ_b4ce2e884ba9ff4708d9da12","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_61ba02f8-b675-4b76-b678-3af69a2b2872","section_id":"sec_71d97541-7ee4-426e-8210-eb8853969707","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":40,"end":42,"exact":"推論","quote":"AIを使うことで、この速度は加速します。  \n特に、GPU kernel最適化、推論スケジューリング、ログ解析、障害予測、セキュリティ検査、インフラ自動運用でAIエージェントが使われるようになるため、CPUクラウドが25年かけた進化を、GPUクラウドは**5〜8年程度で一気に圧縮する","quote_start":0,"quote_end":142,"text_sha256":"8e71f88209e1f6bc3a1e0ef86079cdb7d5d5c958587ae43da0f87e1eb565ec15","block_sha256":"8e71f88209e1f6bc3a1e0ef86079cdb7d5d5c958587ae43da0f87e1eb565ec15","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_61ba02f8-b675-4b76-b678-3af69a2b2872"},{"id":"occ_e43716d77318f53f2d94eefc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_622a435d-a49a-479f-8e09-0112e97126b6","section_id":"sec_d32d82b3-f2c9-4767-a64c-1da8e863e79a","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"LPUはGPUと違い、主に **LLM推論、特にdecode / token generation** に最適化されています。","quote_start":0,"quote_end":63,"text_sha256":"d9c819d58dbe1e121d188f2bbf306757b8f526c3e8b9e38a189920251471c0f3","block_sha256":"d9c819d58dbe1e121d188f2bbf306757b8f526c3e8b9e38a189920251471c0f3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_622a435d-a49a-479f-8e09-0112e97126b6"},{"id":"occ_5ecefaee669cbcef0d28cb4e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_62465f4b-de46-45e5-8df9-8fdd2b38834a","section_id":"sec_efae00ac-e1e8-4909-902d-102abd79a201","layer":"code","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"prefill","quote":"```\nprefill：\n  入力プロンプト全体を一気に読む\n  計算量が大きい\n  GPU演算を使いやすい\n\ndecode：\n  1トークンずつ出す\n  逐次性が強い\n  HBM帯域やKV cacheが効く\n```","quote_start":0,"quote_end":108,"text_sha256":"2e76842a056167bc21e20a4c76c5cbade83dbd10707aada964fdd0de1442d2f2","block_sha256":"2e76842a056167bc21e20a4c76c5cbade83dbd10707aada964fdd0de1442d2f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_62465f4b-de46-45e5-8df9-8fdd2b38834a"},{"id":"occ_f6a4a0015943d816550dfd0e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_62e1b43a-27e4-4777-9118-4246acb0f586","section_id":"sec_58745122-69e4-4bc2-ae16-3bae5e2bfd75","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"なので、エージェント増加による負荷のうち、**モデル推論・生成部分はGPUクラウドでかなり吸収できる**。 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cache\n- LoRA adapter\n- tokenizer状態\n- speculative decoding用draft model\n- safety model","quote_start":0,"quote_end":112,"text_sha256":"d85e294ca845f72fa3f70b4285a4a25926fd53fd8fba4e5e6372b08c87a4d71e","block_sha256":"d85e294ca845f72fa3f70b4285a4a25926fd53fd8fba4e5e6372b08c87a4d71e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_660abedd-5c50-45ae-ac0e-854b0b4b8758"},{"id":"occ_279fbf8aa50b41e971ed609b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_675b413c-a0cf-4216-a080-3f72d9043b4e","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"code","character_id":null,"count":3,"matched_aliases":["KV 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depth\nprefill/decode比率\n```","quote_start":0,"quote_end":75,"text_sha256":"6e97496dd88834602b49dd42fe44156b7eb01a51c7890d1076b1be14d12bc5b3","block_sha256":"6e97496dd88834602b49dd42fe44156b7eb01a51c7890d1076b1be14d12bc5b3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_675b413c-a0cf-4216-a080-3f72d9043b4e"},{"id":"occ_12026014bc6d3a6ee2f81960","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_676b7ff0-3067-4a0f-bba8-e07b81f8226f","section_id":"sec_8d2b2352-24dc-4d96-8a3a-4886849636a9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"LPUは対応モデルに限れば非常に効率的です。  \nただし低遅延推論なので、需要変動を吸収するための余裕が必要です。  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 \nCPUクラウドでも、全サーバーを常時95%で回すと障害や遅延に弱くなります。GPUクラウドも同じです。","quote_start":0,"quote_end":98,"text_sha256":"e0a0ee9668f884193e027c36088e76111af9a6b73a61c4f2c4fc2c38a9b67beb","block_sha256":"e0a0ee9668f884193e027c36088e76111af9a6b73a61c4f2c4fc2c38a9b67beb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_6974ad05-d818-4f48-b091-2e2f84b5cd31"},{"id":"occ_5fae9c74eb01aed4bd61b058","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_6a245ebe-8214-48e5-9623-82883732b900","section_id":"sec_d865bb91-89c7-4bdd-8405-cd9e677171e8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":38,"end":40,"exact":"推論","quote":"MIGは「1枚のGPUを物理的に区切る」方向には強いですが、 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:9,"end":11,"exact":"推論","quote":"つまり、**LLM推論クラウドの成熟度は比較的高く、Lv3に近い**です。","quote_start":0,"quote_end":37,"text_sha256":"f0a03aec04db6c9b40a5a8eb63fe9e1891b1750694aa424faf82c1c7f58543ba","block_sha256":"f0a03aec04db6c9b40a5a8eb63fe9e1891b1750694aa424faf82c1c7f58543ba","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_6ce8c7e1-8aa7-444a-9f1a-e636eecc532a"},{"id":"occ_ba9542bccfc53624fc8b5477","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_6da88587-1cc6-4022-aa93-bd9cbd195c4e","section_id":"sec_9869513e-af53-488b-b9ef-2846e7df2dff","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":10,"end":18,"exact":"KV cache","quote":"特に重要なのは、**KV cache課金**です。","quote_start":0,"quote_end":25,"text_sha256":"a72cc6e0dc0d6efbdd3fbaf69336a3f581722e0e10f76ed81e2d19da1ea25970","block_sha256":"a72cc6e0dc0d6efbdd3fbaf69336a3f581722e0e10f76ed81e2d19da1ea25970","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_6da88587-1cc6-4022-aa93-bd9cbd195c4e"},{"id":"occ_9798ffc5fb3983133d47ac36","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_6fd1d81d-594e-43f0-ad23-805e4a8f612a","section_id":"sec_c656c7fc-605f-492e-a960-66d82e74a6c5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"## 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cache","推論"],"evidence":{"text_basis":"markdown","start":76,"end":78,"exact":"推論","quote":"管理\n- NVIDIA側のGPUオーケストレーション\n- vLLM/SGLang/TensorRT-LLM系の推論エンジン\n- MIG/vGPU/Time slicing\n- KV cache管理\n- 分散推論\n- Confidential Computing","quote_start":21,"quote_end":151,"text_sha256":"52c34f76630b49f059de7f6167bba2c869219952fb130d84cd529354bef4c7db","block_sha256":"52c34f76630b49f059de7f6167bba2c869219952fb130d84cd529354bef4c7db","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_70b55825-8938-4403-a03d-202b2f7d994f"},{"id":"occ_440f933b652e73fd4dfc9bb3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_721e9834-2c85-4154-8255-27ead00029c4","section_id":"sec_27f53173-52ca-4dc9-8a68-3d2e7d9f7742","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"AI推論が安くなると、今まで高すぎて使えなかった用途が一気に増えます。","quote_start":0,"quote_end":35,"text_sha256":"b6990bededebae04afceae8ab27cc26146e737378f210d90432202a37362b84e","block_sha256":"b6990bededebae04afceae8ab27cc26146e737378f210d90432202a37362b84e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_721e9834-2c85-4154-8255-27ead00029c4"},{"id":"occ_4166423a148b7f1e29d8d32b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_72c7ef02-0b5f-4376-9efd-a327f7d29810","section_id":"sec_6b5f9f20-ce40-4f12-8ea3-c979a59146f7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"り重要です。  \nGPUクラウドの再発明は、NVIDIAだけでなく、Kubernetes、クラウド事業者、AI推論エンジン、セキュリティ企業、観測ツールが一体で進めることになります。","quote_start":5,"quote_end":96,"text_sha256":"1c62d611e1fa3fb36d8d586d233b681d9aa990822ebfd99ad86223ab4783308d","block_sha256":"1c62d611e1fa3fb36d8d586d233b681d9aa990822ebfd99ad86223ab4783308d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_72c7ef02-0b5f-4376-9efd-a327f7d29810"},{"id":"occ_58d68021d6c5dc75ef9d3014","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_733ccfba-b5f3-42eb-bae6-f62104b9dd70","section_id":"sec_fda76914-b726-4f80-bca7-f3beb184a212","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"```\nA100 / H100 / B200 など\n  ├─ GPU Instance 1：小型LLM推論\n  ├─ GPU Instance 2：embedding\n  ├─ GPU Instance 3：画像分類\n  └─ GPU Instance 4：別ユーザーの推論\n```","quote_start":0,"quote_end":141,"text_sha256":"b6a7e84152e6db669b55942e20224a713ad3dde57f27247bccedc34a7a0a6679","block_sha256":"b6a7e84152e6db669b55942e20224a713ad3dde57f27247bccedc34a7a0a6679","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_733ccfba-b5f3-42eb-bae6-f62104b9dd70"},{"id":"occ_79f99c49690c4638f065ac98","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_751a3071-f5df-4c88-a3ac-279eeac9321d","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":44,"end":52,"exact":"KV cache","quote":"vLLMのPagedAttentionは、このトークン単位スケーリングの基盤になります。KV cacheはリクエストごとに巨大で動的に増減し、従来方式では断片化や重複でメモリが無駄になり、batch sizeが制限されます。PagedAttentionはOSの仮想メモリ/ページングに着想を得て、KV c","quote_start":0,"quote_end":152,"text_sha256":"b6a98638385b09eb1740cac92d70d42d52282090aeff5ba8822d3f729894b0f4","block_sha256":"b6a98638385b09eb1740cac92d70d42d52282090aeff5ba8822d3f729894b0f4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_751a3071-f5df-4c88-a3ac-279eeac9321d"},{"id":"occ_9afe69e8112d44c171f36376","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_754875f2-623d-4758-94ce-ec4843c896b0","section_id":"sec_45280871-ea21-4d3c-99e6-94cb0ec44aff","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"Groqの公式ページでも、GroqCloudは開発者向けのAI推論プラットフォームで、public、private、co-cloud構成で提供可能とされています。([Groq](https://groq.com/groqcloud?utm_source=chatgp","quote_start":0,"quote_end":133,"text_sha256":"65e95febcadbe1fee6f4a46e4a3e6931b8a5ac1730dc666cedcc88cd4184f1ee","block_sha256":"65e95febcadbe1fee6f4a46e4a3e6931b8a5ac1730dc666cedcc88cd4184f1ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_754875f2-623d-4758-94ce-ec4843c896b0"},{"id":"occ_287cf0db6ec9f6e2d0616129","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7709f869-5218-4049-9ff9-c24166f19f83","section_id":"sec_f3f6bb06-b5e5-4d2b-ab7a-4837dfe6d756","layer":"code","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"Iエージェント追加需要 = 100\n\nGPUクラウド成熟でかなり吸収できる部分\n  50〜70\n  - LLM推論\n  - 長文推論\n  - reasoning\n  - 画像/動画生成\n  - embedding/rerank\n  - KV cache\n  - batch scheduling\n\nGPUクラウドだ","quote_start":5,"quote_end":162,"text_sha256":"f9bd88bfdcaa689720d90f4b0d9b950243667b071fb80cdfec71d8d0437785bd","block_sha256":"f9bd88bfdcaa689720d90f4b0d9b950243667b071fb80cdfec71d8d0437785bd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7709f869-5218-4049-9ff9-c24166f19f83"},{"id":"occ_c9a7c1c8d941c179e986fe39","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_778512f1-5261-4dca-b70a-0803d109c220","section_id":"sec_13d83acd-d459-47cf-b7af-7a65adfe0277","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":44,"end":46,"exact":"推論","quote":"- Google Cloud中心である\n- CUDA資産が巨大すぎる\n- GPUは学習・推論・画像・動画・HPC・ロボティクスで汎用\n- NVIDIAはネットワーク、ソフト、ラック、推論基盤まで統合している\n- AMDや他ASICも競争に入る\n- GoogleがTPUをNVIDIAのように広","quote_start":0,"quote_end":146,"text_sha256":"a3e7ab6ec770f5fc08978898e1c42c29c3f23135935aff43c1977ef118527e62","block_sha256":"a3e7ab6ec770f5fc08978898e1c42c29c3f23135935aff43c1977ef118527e62","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_778512f1-5261-4dca-b70a-0803d109c220"},{"id":"occ_07cea4d2421e55956919a31e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_786986d1-92c5-418e-9392-0c63b391485c","section_id":"sec_0a8db19a-a15e-4eb6-9a83-4a685462289f","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"今はまだGPU利用率が低く、推論スタックも発展途上です。  \nそのため、vLLM、SGLang、Dynamo、Aegaeon、prefill/decode分離、KV cache最適化だけでも、かなりの余力があります。","quote_start":0,"quote_end":108,"text_sha256":"84f43bbddca5f3cbb35615225d43fb221b1d6718e5a334729b7d57a153894975","block_sha256":"84f43bbddca5f3cbb35615225d43fb221b1d6718e5a334729b7d57a153894975","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_786986d1-92c5-418e-9392-0c63b391485c"},{"id":"occ_dddcc644682678e3609d6547","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_786be463-14d4-47e8-92fe-db8a6eed775f","section_id":"sec_2fcdbe46-ebad-470b-b6ea-94ffa48a219c","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":37,"end":39,"exact":"推論","quote":"```\nGPUサーバーが増える\n  ↓\nホストCPUが増える\n  ↓\nAI推論APIが増える\n  ↓\n認証・課金・ログ・監視が増える\n  ↓\nクラウド制御用CPUも増える\n```","quote_start":0,"quote_end":89,"text_sha256":"2590b50a7c588ae9bab2f5f74608491d75130bb0eadb9a63230a9eceeafbc93e","block_sha256":"2590b50a7c588ae9bab2f5f74608491d75130bb0eadb9a63230a9eceeafbc93e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_786be463-14d4-47e8-92fe-db8a6eed775f"},{"id":"occ_b3c44e1f39953ec8f0869743","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7aa68097-edaa-4099-af39-9dc1c4d250dc","section_id":"sec_b303d619-174e-45e1-a89d-3a319eafef04","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"用途TPUクラウドで可能かLLM学習可能LLM推論可能ファインチューニング可能画像生成モデル可能CNN/推薦モデル可能大規模分散学習可能JAX/TensorFlow系ワークロード得意CUDA前提のGPUアプリ苦手","quote_start":0,"quote_end":106,"text_sha256":"52e57bf45f84e89aedb2a9c7472de4c16031a6572d9a9ee487c13cad58a66bd5","block_sha256":"52e57bf45f84e89aedb2a9c7472de4c16031a6572d9a9ee487c13cad58a66bd5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7aa68097-edaa-4099-af39-9dc1c4d250dc"},{"id":"occ_2c51121cf3b05555d73c5381","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7ad5e26f-ee2e-48c6-af05-6c5dd08afc55","section_id":"sec_d6048052-b453-45bf-b44f-f6de73b7c164","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"特にAIエージェント時代には、1回の巨大推論だけでなく、小さな推論、ツール呼び出し、再推論、RAG、長文処理、JSON出力、音声・画像・動画処理が混ざります。その結果、GPUを単純に「1モデル1GPU」「1顧客1GPU」で割り当てると、すぐに無","quote_start":0,"quote_end":122,"text_sha256":"8d4679d228c1edaace52142d42a2dc3fe1493a97b66092d03a3310ba08c06065","block_sha256":"8d4679d228c1edaace52142d42a2dc3fe1493a97b66092d03a3310ba08c06065","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7ad5e26f-ee2e-48c6-af05-6c5dd08afc55"},{"id":"occ_c00d11808fc364bb1df60906","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7b086dc4-fde6-4ce7-9a96-b6667bc9e2b3","section_id":"sec_3f6b0c71-ab05-477d-a481-8ee0a78eefae","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":60,"end":68,"exact":"KV cache","quote":"U oversubscriptionより難しいです。  \n理由は、GPUではHBMが詰まった瞬間に性能が落ち、KV cacheがユーザーごとに動的に増減し、低遅延SLOを守る必要があるからです。","quote_start":5,"quote_end":102,"text_sha256":"7b6664a4c64ba2080052b9281abf6a9b33475d32058cf09f5d0c0f98545b3ae8","block_sha256":"7b6664a4c64ba2080052b9281abf6a9b33475d32058cf09f5d0c0f98545b3ae8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7b086dc4-fde6-4ce7-9a96-b6667bc9e2b3"},{"id":"occ_c8dd22b6a982bec4802c0d89","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7b1385c0-62c8-419e-897b-3fe4fab629b3","section_id":"sec_0ab28b0f-3669-4f74-a3ce-afe5cff0da16","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":7,"end":15,"exact":"KV cache","quote":"## \\1. KV cache管理","quote_start":0,"quote_end":17,"text_sha256":"932e48d6eb0f56a4227602040b8d651dc63ebdd81d35fa8c145eea21e7dbedae","block_sha256":"932e48d6eb0f56a4227602040b8d651dc63ebdd81d35fa8c145eea21e7dbedae","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7b1385c0-62c8-419e-897b-3fe4fab629b3"},{"id":"occ_61bd130ce4825f6aa65e0870","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7d3a52a1-5591-467a-841c-06b378d942f6","section_id":"sec_e8277738-b64e-435b-8135-df3e9a700faf","layer":"body","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"> **TPUクラウド：すでに本格実用されている。学習・推論どちらも可能。**  \n> **LPUクラウド：すでに推論クラウドとして実用化されている。ただしGPU/TPUより用途は狭く、主に超低遅延LLM推論向け。**  \n> **GPUクラウド：最も汎用性が","quote_start":0,"quote_end":130,"text_sha256":"4aadd63deb4e9eee03c92c6089d7c81a4691da801ce42ae2b32cd3f8b9e856e5","block_sha256":"4aadd63deb4e9eee03c92c6089d7c81a4691da801ce42ae2b32cd3f8b9e856e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7d3a52a1-5591-467a-841c-06b378d942f6"},{"id":"occ_b6fccc191bca366d446ef428","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7d830d95-d495-4744-bd6b-5d989941d56f","section_id":"sec_06d3a5f5-31cf-46c8-afab-e76693d7d652","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"> **LLM推論の高速道路**","quote_start":0,"quote_end":16,"text_sha256":"e3c436b3923dfb9880682b9e166548800b8565ee43b42b46cec284bc7e23ee5c","block_sha256":"e3c436b3923dfb9880682b9e166548800b8565ee43b42b46cec284bc7e23ee5c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7d830d95-d495-4744-bd6b-5d989941d56f"},{"id":"occ_d4dd368af961213f399dd08a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7ec72720-4395-4583-85fc-7e9f18ec5f10","section_id":"sec_0d381801-36d5-45d4-a35a-0638c327aa9e","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"```\n巨大な学習：クラウドGPU\n重い推論：クラウドGPU\n軽い推論：PC/スマホ/エッジAIチップ\nリアルタイム制御：ロボットや車載チップ\n```","quote_start":0,"quote_end":75,"text_sha256":"bc090cfaf80a3d59d1894ef8f9ebe5b1081ef89ccef21ef35e660b7f57bdd600","block_sha256":"bc090cfaf80a3d59d1894ef8f9ebe5b1081ef89ccef21ef35e660b7f57bdd600","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7ec72720-4395-4583-85fc-7e9f18ec5f10"},{"id":"occ_2b9f6e5a63436a54be470377","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_7f38311b-e23e-48b7-8efb-48d61c015dd2","section_id":"sec_1c5fa0b8-11ec-4cfb-b3c8-126a361656db","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":43,"end":45,"exact":"推論","quote":"**GPUクラウド**とは、インターネット越しにGPUサーバーを借りて、AI学習・AI推論・画像生成・動画生成・シミュレーションなどを動かせるクラウドサービスのことです。","quote_start":0,"quote_end":85,"text_sha256":"a68bb83fdcdd11e333460b1d96f8fd44e448444e1e346c002bfaa626dd18c3f8","block_sha256":"a68bb83fdcdd11e333460b1d96f8fd44e448444e1e346c002bfaa626dd18c3f8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_7f38311b-e23e-48b7-8efb-48d61c015dd2"},{"id":"occ_a983d48bc48563e1e551e80d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_813429e7-d75d-4757-9bed-ecff82a77433","section_id":"sec_d4de89a5-0d88-41d1-998b-a7b33adb771f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"AIの学習も推論も、多くはクラウドで行われています。  \n特にChatGPTのような大規模AIは、裏側で巨大なGPUクラスタが動いています。","quote_start":0,"quote_end":70,"text_sha256":"014da125046e87e271baefd7cd0f983e8d62e08d197f8047d0202687ad400551","block_sha256":"014da125046e87e271baefd7cd0f983e8d62e08d197f8047d0202687ad400551","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_813429e7-d75d-4757-9bed-ecff82a77433"},{"id":"occ_216381de3e26c669ab687e13","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_8539c2ee-c57e-4f52-a475-33bd2e4079c9","section_id":"sec_67abc6cf-0257-41ae-9bc8-4a18e2b88159","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"GPUクラウドが成熟すると、エージェントの推論処理はかなり効率化されます。","quote_start":0,"quote_end":37,"text_sha256":"c2e5702ce29f673f19b3112bdf94f75828945c7d1ef94118d544bc8651c8e522","block_sha256":"c2e5702ce29f673f19b3112bdf94f75828945c7d1ef94118d544bc8651c8e522","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_8539c2ee-c57e-4f52-a475-33bd2e4079c9"},{"id":"occ_22331779217ea1affcab00fc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_85def2c7-e4e0-4ed5-b759-e4b6447bd3ef","section_id":"sec_251671e3-b21e-4a22-bd91-d9fdbf8f1999","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":208,"end":210,"exact":"推論","quote":"狙う。GPUより早く成熟しやすいLPUクラウドフリート稼働率は非公開。対応モデルでは低遅延・高効率に動く低遅延推論で高効率化。ただし用途が狭く、需要変動で30〜70%程度に揺れやすい","quote_start":153,"quote_end":244,"text_sha256":"0176690def37d2645f134b13dec1c7cc37d2c5177816db00032d4d75824d5ce4","block_sha256":"0176690def37d2645f134b13dec1c7cc37d2c5177816db00032d4d75824d5ce4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_85def2c7-e4e0-4ed5-b759-e4b6447bd3ef"},{"id":"occ_120c601e12fa55217235f576","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_8627893a-9d3c-49a4-a361-159857c2e0a1","section_id":"sec_6dec4f7e-9e3e-46df-a1bd-b7ab8a079098","layer":"code","character_id":null,"count":2,"matched_aliases":["KV 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です。","quote_start":0,"quote_end":38,"text_sha256":"c8ffaaf4d0fa0a3cbf5ae22e0f04777ec8ccea4587b8b32c0906e1fa07473986","block_sha256":"c8ffaaf4d0fa0a3cbf5ae22e0f04777ec8ccea4587b8b32c0906e1fa07473986","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_8a684e06-4ab1-4de2-9ac1-4c05ed87f0f9"},{"id":"occ_f2ff1242aaf60ef74cbf63ba","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_8b063cf4-285e-42d3-bb30-ab97c790526e","section_id":"sec_59a30840-3f80-46be-aa94-9d9cd7331f4b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"特にフィジカルAI、ロボット、工場、自動運転では、GPU/NPUが知覚や推論を担っても、**CPU/MCU/リアルタイム制御系**は残ります。","quote_start":0,"quote_end":71,"text_sha256":"4111ce008f6459282d06cb1a3ed51d76dcfec4132ce0bd009e6074e3a68f92b1","block_sha256":"4111ce008f6459282d06cb1a3ed51d76dcfec4132ce0bd009e6074e3a68f92b1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_8b063cf4-285e-42d3-bb30-ab97c790526e"},{"id":"occ_31dc8df3646a15ac111467ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_8b991b4b-c48e-441f-aee7-e60d0b24776a","section_id":"sec_e8277738-b64e-435b-8135-df3e9a700faf","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"> **GPUクラウドが万能基盤、TPUクラウドが大規模AI専用基盤、LPUクラウドが低遅延推論基盤**","quote_start":0,"quote_end":52,"text_sha256":"cda6f7a1946e9b87b0f68976e137e74da841f37b2c33c47411f5e3c2ed2afa04","block_sha256":"cda6f7a1946e9b87b0f68976e137e74da841f37b2c33c47411f5e3c2ed2afa04","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_8b991b4b-c48e-441f-aee7-e60d0b24776a"},{"id":"occ_3b68132e4bdb3171724f6157","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_8e7d54a2-6963-434a-af36-4b51251746aa","section_id":"sec_e5e44156-0b06-4dce-8372-0aca3f2d053a","layer":"body","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"prefill","quote":"DistServeはprefillとdecodeを分離して、互いの干渉を減らすLLMサービング方式です。OSDI 2024の論文では、既存方式より最大7.4倍多いリクエストを処理できる、または12.6倍厳しいSLOを満たせると報告され","quote_start":0,"quote_end":117,"text_sha256":"1639a97d9ac050f7ccabdf4717d82caeaf50343b2ca1357bd3b4a14c6c4a574d","block_sha256":"1639a97d9ac050f7ccabdf4717d82caeaf50343b2ca1357bd3b4a14c6c4a574d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_8e7d54a2-6963-434a-af36-4b51251746aa"},{"id":"occ_7a71a2795e3396568aad47ea","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9494b41f-e4d6-4672-893b-fa1d7ab6a9ed","section_id":"sec_961b1eda-207e-40bf-81c5-3228d5460b18","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":66,"end":68,"exact":"推論","quote":"HBMは80GBか？\nMIG 2g.20gbが必要か？\n同じNVLink island内に4枚必要か？\n低遅延推論用か？\nbatch学習用か？\nconfidential computingが必要か？\n```","quote_start":11,"quote_end":114,"text_sha256":"40c442e3708ced3d1bbc508f48158fa848dd3591fa049122f612cc59c268200b","block_sha256":"40c442e3708ced3d1bbc508f48158fa848dd3591fa049122f612cc59c268200b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9494b41f-e4d6-4672-893b-fa1d7ab6a9ed"},{"id":"occ_dc0082bd2670a8cc137fb033","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_94fbe44b-8d3a-4872-88c4-49471c3e7721","section_id":"sec_19ebd9ad-9d35-4b93-a22e-cc9213aa446b","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":104,"end":106,"exact":"推論","quote":"近い。\n\n2027〜2028年：\n  vLLM/SGLang/Dynamo/DRA/MIG運用が一般化。\n  推論クラウドはかなり改善。\n\n2029〜2031年：\n  token、KV cache、SLO単位のスケジューリングと課金が広がる。\n  GPUクラウドOS化が進む。\n\n2030年代前半：\n  CPUクラ","quote_start":49,"quote_end":206,"text_sha256":"1247bc2b04d0f37230bf156aee2b5a9ddd2812cabdddae8a1293cfc7e8983826","block_sha256":"1247bc2b04d0f37230bf156aee2b5a9ddd2812cabdddae8a1293cfc7e8983826","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_94fbe44b-8d3a-4872-88c4-49471c3e7721"},{"id":"occ_6301e4098162905e6d2ff1b3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_97a1fa4f-3495-4f44-af81-dbc736ebc80f","section_id":"sec_49bfd3f1-99df-48fb-9390-5247be61cb90","layer":"body","character_id":null,"count":4,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"prefill","quote":"> このリクエストはprefillが重い。decodeは軽いが長く続く。KV cacheはこれくらい増える。TTFTを守る必要がある。GPUメモリに余裕がない。別GPUにdecodeだけ逃がすか。prefix cacheを再利用できる","quote_start":0,"quote_end":117,"text_sha256":"875e09387b12ecf7c71cfc55e20a1c8738b4e3a643492487fe619c1079d2c9e2","block_sha256":"875e09387b12ecf7c71cfc55e20a1c8738b4e3a643492487fe619c1079d2c9e2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_97a1fa4f-3495-4f44-af81-dbc736ebc80f"},{"id":"occ_9e557764ad5d8ec9d8b3a5b2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_98e11b4f-f234-4d5e-8e70-7404b9716759","section_id":"sec_a1dc352b-54dc-455d-87a5-2f98a945adb5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"GPUクラウド最適化は、単にGPUを高稼働にするだけでは不十分です。  \nAIエージェントが増えると、推論リクエスト、APIキー、モデル選択、レート制限、コスト管理、認証、ログ、攻撃防御が必要になります。","quote_start":0,"quote_end":102,"text_sha256":"9cbc7576f7c0a3ad4590e1af2425734a0bbdc166934b8dd9c2a06b9df53416cc","block_sha256":"9cbc7576f7c0a3ad4590e1af2425734a0bbdc166934b8dd9c2a06b9df53416cc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_98e11b4f-f234-4d5e-8e70-7404b9716759"},{"id":"occ_dcd7ebc90aac49fc589ccedd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9ac296ec-7573-4acc-9764-b60beb101cb5","section_id":"sec_3ed2a696-c34d-42a7-8e62-75970d04cafa","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"AIエージェントは、1回の巨大推論だけではなく、小さな推論を何度も繰り返します。","quote_start":0,"quote_end":40,"text_sha256":"70a8400368b56164e6fa4dd5fc4b59796f7dc2b092f47c04f714fd7b9fc74ae0","block_sha256":"70a8400368b56164e6fa4dd5fc4b59796f7dc2b092f47c04f714fd7b9fc74ae0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9ac296ec-7573-4acc-9764-b60beb101cb5"},{"id":"occ_229340fe6ddd56a43eb04b6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9b4215af-85ce-4a1f-acff-a7416f4c1c43","section_id":"sec_4484b634-cd33-4ce3-970b-a731e9a7e4f0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"普通のGPUは大量の汎用並列計算に強いですが、LLM推論では、","quote_start":0,"quote_end":31,"text_sha256":"ca3ab363bc30bff714b449be129127f9dc7d968a65bfa4cd822db1805087c646","block_sha256":"ca3ab363bc30bff714b449be129127f9dc7d968a65bfa4cd822db1805087c646","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9b4215af-85ce-4a1f-acff-a7416f4c1c43"},{"id":"occ_8d6cc3d95e11a295e5a220a3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9b5a28c9-0299-4e83-b566-522eb874586d","section_id":"sec_836104cc-c08c-464b-b7f1-bc7b08c7428b","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":75,"end":83,"exact":"KV cache","quote":"そこにアプリを載せる」で済みました。  \nしかしGPUクラウドでは、**1つのLLMリクエストがGPUメモリ、KV cache、トークン生成、ネットワーク、CPU前処理、ストレージ、セキュリティを横断する**ため、もっと複雑です。","quote_start":20,"quote_end":135,"text_sha256":"b3e1471457780da22ec9bb9719269356c99cb3e050928013a2b4af75b9d8f36e","block_sha256":"b3e1471457780da22ec9bb9719269356c99cb3e050928013a2b4af75b9d8f36e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9b5a28c9-0299-4e83-b566-522eb874586d"},{"id":"occ_a40378bb48bed03a5a1db4e4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9c6447b1-6f31-4473-96b8-2bb567f557c8","section_id":"sec_7578d2e0-ef74-4382-b7cf-ad9d1c2952f3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"## 第2段階：LLM推論を高効率に詰め込む","quote_start":0,"quote_end":22,"text_sha256":"486d4874e5e9c6ccb63b99c6fb2ccfca30e23510030d52278b43fd262250ec37","block_sha256":"486d4874e5e9c6ccb63b99c6fb2ccfca30e23510030d52278b43fd262250ec37","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9c6447b1-6f31-4473-96b8-2bb567f557c8"},{"id":"occ_baadb20fabe804074560c835","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9cae1884-c7cb-4889-8d43-37ac59b4de44","section_id":"sec_c656c7fc-605f-492e-a960-66d82e74a6c5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":54,"end":56,"exact":"推論","quote":"はい。ChatGPT、Claude、Gemini、Copilot、画像生成AI、動画生成AIなど、多くのAI推論もクラウドで行われています。","quote_start":0,"quote_end":70,"text_sha256":"a570858c3f99677eb3771bf12659db274cf92ee5143782ccd2b5155154786bb5","block_sha256":"a570858c3f99677eb3771bf12659db274cf92ee5143782ccd2b5155154786bb5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9cae1884-c7cb-4889-8d43-37ac59b4de44"},{"id":"occ_054b9f86ba5a615ffa2944c9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_9dff6142-d9f9-408f-81fc-476a168d3eab","section_id":"sec_a1dc352b-54dc-455d-87a5-2f98a945adb5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"この領域は、GPUクラウドの「外側」にあります。  \nしかしAIエージェント時代には、GPU推論そのものよりも、**どのAIに、誰が、どの権限で、どれだけ使わせるか**が重要になります。","quote_start":0,"quote_end":93,"text_sha256":"573fbfcbdb7b18fadc22321492b1b715e6ab3db26d886a1c9d3da7ad6877e116","block_sha256":"573fbfcbdb7b18fadc22321492b1b715e6ab3db26d886a1c9d3da7ad6877e116","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_9dff6142-d9f9-408f-81fc-476a168d3eab"},{"id":"occ_498e796be2fcaa4c6c6417f6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_a1a642d4-de1d-45ce-9a3b-614938fcf208","section_id":"sec_a27dfbb6-e14e-484a-97dc-7dabf96046be","layer":"body","character_id":null,"count":5,"matched_aliases":["KV cache","decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"GPUが大量にあっても、GPU間通信が詰まると、学習も推論も止まります。  \n特に分散学習、MoE、prefill/decode分離、KV cache共有、マルチノード推論では、ネットワークがGPU稼働率を左右します。","quote_start":0,"quote_end":109,"text_sha256":"09d91902b29a94cec75b27ec34e00c16032e493f47294029d3e130a8814ce2f3","block_sha256":"09d91902b29a94cec75b27ec34e00c16032e493f47294029d3e130a8814ce2f3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_a1a642d4-de1d-45ce-9a3b-614938fcf208"},{"id":"occ_cfb939a3090d4abe05dff5be","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_a209f8ec-8553-4ab3-aefb-ab80d7501dfe","section_id":"sec_e4db8852-d772-4f64-9b92-9c34d2510aff","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"ここで重要なのは、**LLM推論では“計算時間”だけでなく“状態の保持”が価値になる**ことです。","quote_start":0,"quote_end":49,"text_sha256":"d097289d44b9e46c6d2b9a139bc5aa1eddda34f1ba9df599661946063ab64c6b","block_sha256":"d097289d44b9e46c6d2b9a139bc5aa1eddda34f1ba9df599661946063ab64c6b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_a209f8ec-8553-4ab3-aefb-ab80d7501dfe"},{"id":"occ_84cb9fdc9ae46d38073e9170","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_a3bb7ca7-bdeb-4b1a-a00b-de2df14ab283","section_id":"sec_1a856153-da5e-4157-a30b-5b01ab76a58b","layer":"code","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":18,"end":26,"exact":"KV cache","quote":"```\nAIリクエストルーター\n+\nKV cacheルーター\n+\nモデル常駐ルーター\n+\nSLO制御器\n```","quote_start":0,"quote_end":55,"text_sha256":"0dd79b314a6efb3731aed38fa1340908be2f62920d625b6f54c45b55100e6c29","block_sha256":"0dd79b314a6efb3731aed38fa1340908be2f62920d625b6f54c45b55100e6c29","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_a3bb7ca7-bdeb-4b1a-a00b-de2df14ab283"},{"id":"occ_3c4f8cedeb502bdb9fe27b6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_a99d6b5c-c732-453e-8b03-0d9ab4984186","section_id":"sec_a759dd1a-5c7c-4c39-9576-5d398b8cc90e","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":"``\nLv1：GPUサーバーを貸せる\nLv2：KubernetesやMIGである程度運用できる\nLv3：LLM推論をvLLM/SGLang/TensorRT-LLMなどで最適化し始めた\nLv4：トークン/KV cache/SLO単位で自動スケール・課金・ルーティングできる\nLv5：CPUクラウド並みに安全・高稼働","quote_start":1,"quote_end":158,"text_sha256":"30957c4c0bb213f81436fbd22a0a322d4a31b4e53d58dcd51f8e3de1b8820600","block_sha256":"30957c4c0bb213f81436fbd22a0a322d4a31b4e53d58dcd51f8e3de1b8820600","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_a99d6b5c-c732-453e-8b03-0d9ab4984186"},{"id":"occ_253c3a404b16e1269f541a15","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_a9a6bfad-2f0b-4407-824f-c20c1550f517","section_id":"sec_3751a969-a9f8-4805-a45c-1cb291ed56d3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":64,"end":66,"exact":"推論","quote":"なGPU運用に比べて、成熟したGPUクラウドでは同じGPU枚数で3〜10倍、特定条件ではそれ以上のエージェント推論を処理できる可能性がある。**","quote_start":9,"quote_end":81,"text_sha256":"f44e71cdfe82942fd8ce2b886bc20f57a0ca48a1277e611467592c9851c69b33","block_sha256":"f44e71cdfe82942fd8ce2b886bc20f57a0ca48a1277e611467592c9851c69b33","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_a9a6bfad-2f0b-4407-824f-c20c1550f517"},{"id":"occ_35d41c9d67b90bd90d826ca0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_aac0dc86-8a0d-4924-8c16-19f510163dec","section_id":"sec_60a8a145-1f54-4dc1-9e65-16cc2eb54c44","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"エージェントが本格化すると、モデル推論だけでなく、","quote_start":0,"quote_end":25,"text_sha256":"4d510c35cd3033bec2a300eea8c6f37a3f757083fde667dff6749a0d211c9eb4","block_sha256":"4d510c35cd3033bec2a300eea8c6f37a3f757083fde667dff6749a0d211c9eb4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_aac0dc86-8a0d-4924-8c16-19f510163dec"},{"id":"occ_572c966b03305ad0d35828eb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ab3ba01f-5048-40d4-9d01-d01968d0ed5b","section_id":"sec_464705c9-5456-4a75-8565-30bf403331fc","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":9,"end":17,"exact":"KV cache","quote":"- 会話履歴\n- KV cache\n- prefix cache\n- tool callの中間状態\n- エージェントの作業メモリ\n- 長文コンテキスト\n- マルチモーダル埋め込み\n- モデルごとの常駐重み","quote_start":0,"quote_end":102,"text_sha256":"afcc5075cb68062ad7061a934f69c7563995a1fa8df02dc9b369e27a96287347","block_sha256":"afcc5075cb68062ad7061a934f69c7563995a1fa8df02dc9b369e27a96287347","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ab3ba01f-5048-40d4-9d01-d01968d0ed5b"},{"id":"occ_8abe3736c9bb669fe9595d1e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ac2312a7-7d14-425c-980d-1446021655c3","section_id":"sec_f9df470f-5155-42ec-97eb-cf96aea7d2de","layer":"code","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":19,"end":27,"exact":"KV cache","quote":"```\nGPU秒\nGPUメモリGB秒\nKV cache GB秒\ntoken単位課金\nTTFT/TPOT保証\n推論SLO\nモデル常駐課金\nagent memory課金\nマルチテナント隔離\nGPU障害復旧\nGPUクラスタ観測\n```","quote_start":0,"quote_end":114,"text_sha256":"8d5e7ff2b98112c196a1eca7937ed51bf69474100f64cc15c6b45ec50597198b","block_sha256":"8d5e7ff2b98112c196a1eca7937ed51bf69474100f64cc15c6b45ec50597198b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ac2312a7-7d14-425c-980d-1446021655c3"},{"id":"occ_afc669494d173701d910a519","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ad0143a0-3116-4883-8117-ebb5e2c0ca50","section_id":"sec_f859a282-7742-40dc-98c9-bdbf5336622e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"特に重要なのが **NVIDIA Dynamo** です。Dynamoは大規模分散推論向けのオープンソースフレームワークで、reasoning modelや生成AIを低遅延・高スループットで動かすための仕組みです。NVIDIAは、DeepSeek-R1系モデルをBlackwell上で動","quote_start":0,"quote_end":143,"text_sha256":"c671b90484268cd88938b908c46a70802254831451a87b8934127d7b14698736","block_sha256":"c671b90484268cd88938b908c46a70802254831451a87b8934127d7b14698736","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ad0143a0-3116-4883-8117-ebb5e2c0ca50"},{"id":"occ_28f04e0dddf16056a4451cc7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_af28a65e-81cc-4430-b836-2d51c44beb9d","section_id":"sec_290c3175-ff73-4c57-8c7e-cbb44d19088d","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":57,"end":65,"exact":"KV cache","quote":"continuous batching\n- paged attention\n- prefix cache\n- KV cache管理\n- prefill/decode分離\n- speculative decoding\n- routing\n- model multiplexing\n- multi-LoRA serving\n- d","quote_start":2,"quote_end":165,"text_sha256":"4b12b1c94bf62cb995190471b755c69b48185afc5d640f94f6fb066ac9527a13","block_sha256":"4b12b1c94bf62cb995190471b755c69b48185afc5d640f94f6fb066ac9527a13","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_af28a65e-81cc-4430-b836-2d51c44beb9d"},{"id":"occ_4a7ce02437eecf22cf064d39","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_afa89cbb-4efe-42ff-8da4-57493621f232","section_id":"sec_4484b634-cd33-4ce3-970b-a731e9a7e4f0","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"LPUは、ざっくり言うと **LLM推論、特にトークン生成を高速・低遅延・予測可能に行うための専用チップ**です。","quote_start":0,"quote_end":57,"text_sha256":"40b0233aa141bfa919886e2264a120081f0389ead4ac1978e2eea1694360d8f5","block_sha256":"40b0233aa141bfa919886e2264a120081f0389ead4ac1978e2eea1694360d8f5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_afa89cbb-4efe-42ff-8da4-57493621f232"},{"id":"occ_0f92a2e66b9553ed810b7113","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b1c30ea9-1125-420a-85f0-c6dea6c0d5e5","section_id":"sec_8870f90c-89ae-4d60-aa00-32e849ed82e9","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","推論"],"evidence":{"text_basis":"markdown","start":16,"end":24,"exact":"KV cache","quote":"たとえば、チャットLLM中心ならKV cacheとdecode最適化が重要。  \n動画生成中心なら巨大な生成バッチとメモリ帯域が重要。  \nロボティクス/VLA中心なら、シミュレーション、動画理解、リアルタイム推論、エッジ連携が重要になります。","quote_start":0,"quote_end":122,"text_sha256":"da971f4149a581c27152300e6bde81f7db821621007305ccb5a54b6263649fce","block_sha256":"da971f4149a581c27152300e6bde81f7db821621007305ccb5a54b6263649fce","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b1c30ea9-1125-420a-85f0-c6dea6c0d5e5"},{"id":"occ_022529336f073516b4c00b55","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b1ef36d3-dfb5-4d21-85dd-ab463ff3d991","section_id":"sec_290c3175-ff73-4c57-8c7e-cbb44d19088d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"## \\8. 推論エンジン/AIサービング企業：vLLM、SGLang、Anyscale、Together AI、Fireworks AI","quote_start":0,"quote_end":69,"text_sha256":"0a5415fdf5089ed998f853dea851154f666db6317b0c9fc735d6251c6e5f02b4","block_sha256":"0a5415fdf5089ed998f853dea851154f666db6317b0c9fc735d6251c6e5f02b4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b1ef36d3-dfb5-4d21-85dd-ab463ff3d991"},{"id":"occ_d2262c16c0c5bc8b70b6c2d2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b3a100d6-74f3-44f8-a7f8-1ecd81cc9def","section_id":"sec_baffb438-6cd2-4b65-9f3e-9f0215b7e422","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":26,"end":34,"exact":"KV cache","quote":"GPUクラウドでは、トークン単位のスケジューリング、KV cache管理、prefill/decode分離、モデル配置、障害復旧、セキュリティ、課金などが必要になります。これらはGPU上の計算だけではなく、CPU側の制御・管理が重要になります。","quote_start":0,"quote_end":122,"text_sha256":"b312c811796fc16417b46637d4fb8286cb5ebe3f4121c471f2c6d9bff451559c","block_sha256":"b312c811796fc16417b46637d4fb8286cb5ebe3f4121c471f2c6d9bff451559c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b3a100d6-74f3-44f8-a7f8-1ecd81cc9def"},{"id":"occ_3c8d58fa21ad0cd5585fce15","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b5482819-4b42-41ff-b826-58bb710aa8d1","section_id":"sec_f8d96e2c-49bd-4d63-90b8-80dd952e79e5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"> **LLM推論の高速道路**","quote_start":0,"quote_end":16,"text_sha256":"e3c436b3923dfb9880682b9e166548800b8565ee43b42b46cec284bc7e23ee5c","block_sha256":"e3c436b3923dfb9880682b9e166548800b8565ee43b42b46cec284bc7e23ee5c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b5482819-4b42-41ff-b826-58bb710aa8d1"},{"id":"occ_5a1372f30e768b2e443b4546","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b6c7d873-d540-4edb-9fdf-ed40f71e34a1","section_id":"sec_9869513e-af53-488b-b9ef-2846e7df2dff","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":57,"end":59,"exact":"推論","quote":"`\n短文チャットを大量に処理\n長文コンテキストを少数処理\n動画生成を処理\nembeddingを処理\nbatch推論を処理\n低遅延SLO付き推論を処理\n```","quote_start":2,"quote_end":80,"text_sha256":"b66ebfd86c6205957ca6b0530879d172e234c6d4fa3ecdf7c2be4912430421b0","block_sha256":"b66ebfd86c6205957ca6b0530879d172e234c6d4fa3ecdf7c2be4912430421b0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b6c7d873-d540-4edb-9fdf-ed40f71e34a1"},{"id":"occ_ebe421121a14c1f835d1347e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_b7be2315-ecd9-420f-ade9-6a8c1b45e7a7","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"## \\6. オートスケーリング → 推論トークン単位のスケーリング","quote_start":0,"quote_end":34,"text_sha256":"f577f08d5a0605e8d36796343a8d230c01df3e7f62e028f7d49f603ae9637620","block_sha256":"f577f08d5a0605e8d36796343a8d230c01df3e7f62e028f7d49f603ae9637620","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_b7be2315-ecd9-420f-ade9-6a8c1b45e7a7"},{"id":"occ_34f589a59ccba89cf5358151","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ba0030ac-6eab-44a3-97a7-adfdbce51685","section_id":"sec_1a856153-da5e-4157-a30b-5b01ab76a58b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"SON decoding、multi-turn chatのような複雑なLLMアプリケーションで、構造を利用して推論を効率化するフレームワークです。論文では、RadixAttentionなどにより複雑な言語モデルプログラムで最大6.4倍のスループット改善が報告されています。([arXiv](https://arxi","quote_start":27,"quote_end":184,"text_sha256":"630e798aee9e8ac75aaaf3ddaab484a8738bb1679b73ae7996fdde2e56b22f06","block_sha256":"630e798aee9e8ac75aaaf3ddaab484a8738bb1679b73ae7996fdde2e56b22f06","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ba0030ac-6eab-44a3-97a7-adfdbce51685"},{"id":"occ_3c8ff418b337d5c1f1eada1b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_baecfd62-c1de-493a-9b86-d447891a4cad","section_id":"sec_6dec4f7e-9e3e-46df-a1bd-b7ab8a079098","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":61,"end":63,"exact":"推論","quote":"Uサーバーを借りる時代\n  ↓\nMIG/vGPU/Kubernetesで分割・運用し始めた時代\n  ↓\nLLM推論エンジンで高効率化し始めた時代\n```","quote_start":6,"quote_end":82,"text_sha256":"d1893f11b46ced6e22838236aa5ef948b617b4356ab99d599d25d05c43f42e77","block_sha256":"d1893f11b46ced6e22838236aa5ef948b617b4356ab99d599d25d05c43f42e77","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_baecfd62-c1de-493a-9b86-d447891a4cad"},{"id":"occ_9ab7684fb4f79f30b8abb805","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_bb76f34f-36bd-4c5b-bc28-6c2eaca73f4a","section_id":"sec_5d9bd8c2-44e4-4e83-bc14-19ea8583f14e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"AI処理量だけを前提にすれば、GPUクラウドの成熟によって必要GPU数は縮小します。**  \nただし現実には、推論単価が下がることでAI利用量が爆発するため、**世界全体のGPU需要は縮小せず、むしろ増え続ける可能性が高い**です。","quote_start":7,"quote_end":123,"text_sha256":"a37500c2f6ae28a80230bae00c4d8ed69443dd526c5e4eb02906a2a3044f4d73","block_sha256":"a37500c2f6ae28a80230bae00c4d8ed69443dd526c5e4eb02906a2a3044f4d73","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_bb76f34f-36bd-4c5b-bc28-6c2eaca73f4a"},{"id":"occ_355eb5d2d2fa6e5c6501a339","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_bbb25651-2657-4529-b203-cbf66d5f3448","section_id":"sec_b3015863-c634-42f8-bf90-57c765dba5e5","layer":"body","character_id":null,"count":2,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":68,"end":75,"exact":"prefill","quote":"待ち時間だらけになります。  \nそこで、複数リクエストを動的に混ぜるcontinuous batchingや、prefillを小さく分割するchunked prefillが重要になります。","quote_start":13,"quote_end":107,"text_sha256":"27a33589a49789e3081ca88545cab9ade9d75980d91710e850d06e1b80ded2d8","block_sha256":"27a33589a49789e3081ca88545cab9ade9d75980d91710e850d06e1b80ded2d8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_bbb25651-2657-4529-b203-cbf66d5f3448"},{"id":"occ_3e73c31d9d14980abe627bc7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_bbbb4823-59c7-466c-abcf-09d4e6d068ff","section_id":"sec_8c386d0d-3385-4a7f-8017-702e5566fdbf","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"LPUクラウドは、GroqのようなLanguage Processing Unitを使い、LLM推論を低遅延で提供するクラウドです。GroqCloudは、LPUを使ってテキスト、音声、画像入力系の生成AIモデルに高速推論を提供すると説明されています。([Groq](https://groq.com/","quote_start":0,"quote_end":150,"text_sha256":"a2b062f796438035cdc0c41b313855ba9b6fd9c568661ffe81951aa92c03c3ba","block_sha256":"a2b062f796438035cdc0c41b313855ba9b6fd9c568661ffe81951aa92c03c3ba","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_bbbb4823-59c7-466c-abcf-09d4e6d068ff"},{"id":"occ_c1758e1e3d1757f85aab36c2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_bc6e42b9-c59a-443f-a116-e2a2e17aa49d","section_id":"sec_e848089f-b5fd-482c-a1e7-5ab19f8eb838","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"一方、古い汎用サーバーCPU、CPUだけでAI推論を処理する構成、過剰なCPU/GPU比率のサーバーは相対的に弱くなります。","quote_start":0,"quote_end":62,"text_sha256":"14d87ea8e8767dbe5cb8e76d349c9ea3e98181d73873d4dc3a513013e6aac7e8","block_sha256":"14d87ea8e8767dbe5cb8e76d349c9ea3e98181d73873d4dc3a513013e6aac7e8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_bc6e42b9-c59a-443f-a116-e2a2e17aa49d"},{"id":"occ_deb395efffef530ffaea8b5f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_be4e20fb-e86d-41d4-a5e6-4cc9619f4737","section_id":"sec_4ad25583-0d21-4b4a-b736-1e95b9a2ed9f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"ラウドは、未最適化な企業環境では5〜20%程度の利用率にとどまる例があります。一方、最適化された学習クラスタや推論基盤では35〜50%程度、トップ層ではさらに上を狙う形になります。","quote_start":7,"quote_end":97,"text_sha256":"f763dc61b6938474422b0acbe917da52a7358b82eb941ed3dad13226af854461","block_sha256":"f763dc61b6938474422b0acbe917da52a7358b82eb941ed3dad13226af854461","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_be4e20fb-e86d-41d4-a5e6-4cc9619f4737"},{"id":"occ_97fdfd1f8523bf0aa82a4a14","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c0befd07-da8f-44e5-97a6-91865aec6806","section_id":"sec_2c3bae8a-7a17-469a-b63b-2faea427c6f5","layer":"code","character_id":null,"count":4,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"```\nTPUクラウド\n  → すでに本格実用。\n  → 学習・推論どちらも可能。\n  → Google Cloud上の大規模AI基盤。\n\nLPUクラウド\n  → すでに推論クラウドとして実用化。\n  → 特に低遅延LLM推論に強い。\n  → 学習や汎用AI処理には向","quote_start":0,"quote_end":134,"text_sha256":"8bb87c66b521d07ac6466b149c78ac2ab86a1bd58f78b2476611ff600f00c063","block_sha256":"8bb87c66b521d07ac6466b149c78ac2ab86a1bd58f78b2476611ff600f00c063","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c0befd07-da8f-44e5-97a6-91865aec6806"},{"id":"occ_ae0bc52f5afcd6c407a652d5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c0c173c6-1bbc-42ca-be03-3fa402e59897","section_id":"sec_3455ca7e-a616-4a2a-8c72-1427686a5562","layer":"code","character_id":null,"count":4,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":174,"end":182,"exact":"KV cache","quote":"ving runtime：\n  vLLM / SGLang / TensorRT-LLM / Dynamo\n\nKV cache manager：\n  PagedAttention、prefix cache、multi-tier KV cache\n\ntoken-level autoscaler：\n  request数ではなくt","quote_start":119,"quote_end":282,"text_sha256":"8e86320cb94a239b2b3c8a0f2b9087f23c1ce77c7553668bcbe6cfd6b69db7bc","block_sha256":"8e86320cb94a239b2b3c8a0f2b9087f23c1ce77c7553668bcbe6cfd6b69db7bc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c0c173c6-1bbc-42ca-be03-3fa402e59897"},{"id":"occ_a063807fc1c89ace8eaab5af","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c13fe7df-e56d-4daa-942b-3a1a85e1ef3d","section_id":"sec_e5e44156-0b06-4dce-8372-0aca3f2d053a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":43,"end":45,"exact":"推論","quote":"つまり、GPUクラウドが成熟すると、  \n**同じGPU数でも、より多くのエージェント推論をさばける**  \nようになります。","quote_start":0,"quote_end":63,"text_sha256":"13e8ff465c139d1f4063ba58e7cc41c6c5ab184c8bb1536299f750f46a989a4d","block_sha256":"13e8ff465c139d1f4063ba58e7cc41c6c5ab184c8bb1536299f750f46a989a4d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c13fe7df-e56d-4daa-942b-3a1a85e1ef3d"},{"id":"occ_694524a0111a660c75e4e7d1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c1749f49-25c2-4613-a9a2-bd517d6db711","section_id":"sec_0ab28b0f-3669-4f74-a3ce-afe5cff0da16","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":12,"end":20,"exact":"KV cache","quote":"LLMは長文を扱うほど、KV cacheというメモリを大量に使います。  \nエージェントは過去の会話、作業履歴、ツール結果、資料を何度も参照するので、KV cache管理が非常に重要です。","quote_start":0,"quote_end":94,"text_sha256":"9f9d6ed934a8623ea3b6efb4876bfc4dff684fb8544486e5992863078ec3f8f9","block_sha256":"9f9d6ed934a8623ea3b6efb4876bfc4dff684fb8544486e5992863078ec3f8f9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c1749f49-25c2-4613-a9a2-bd517d6db711"},{"id":"occ_5f0ca4d5da53101aef92789e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c38ccbc6-9433-4df6-af3d-80dfcea62c84","section_id":"sec_4ad25583-0d21-4b4a-b736-1e95b9a2ed9f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"ただし、常時90%以上を目指すのは現実的ではありません。AI推論では急激な需要変動、低遅延SLO、障害時の余裕容量が必要だからです。","quote_start":0,"quote_end":66,"text_sha256":"f32233a3cc11e92bdc788e7e68af2861c967e9f917d5d3139be7226b8b02be95","block_sha256":"f32233a3cc11e92bdc788e7e68af2861c967e9f917d5d3139be7226b8b02be95","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c38ccbc6-9433-4df6-af3d-80dfcea62c84"},{"id":"occ_de9cbd6ddcf4e6a40d364f9d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c535ca0e-5792-496f-bad7-f853d913dc16","section_id":"sec_7e6570d5-47c1-4c8d-8c40-b67e49433c73","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":120,"end":128,"exact":"KV cache","quote":"\n- ツール実行\n- ブラウザ操作\n- エージェント状態管理\n- RAG検索\n- GPUスケジューリング\n- KV cache管理のメタ制御","quote_start":65,"quote_end":135,"text_sha256":"7e1f17b0d50bb8bdea0421d3521754303a95f34d6614ed70f2b834c3a129c5d3","block_sha256":"7e1f17b0d50bb8bdea0421d3521754303a95f34d6614ed70f2b834c3a129c5d3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c535ca0e-5792-496f-bad7-f853d913dc16"},{"id":"occ_64bafe62628df30f0b0e9e29","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c6836cc6-ab6c-4c86-9650-aef89ad94ab7","section_id":"sec_857d3d77-d7e6-41fb-8581-c6fe6bea6608","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":42,"end":50,"exact":"KV cache","quote":"特に障害復旧は重要です。2026年のGhostServeは、LLM servingでKV cacheをerasure codingにより保護し、GPU障害時に高コストな再計算や完全複製なしで推論を再開する研究です。これは、GPUクラウドがCPUクラウドのような高可用性に近づくには、KV cacheレ","quote_start":0,"quote_end":150,"text_sha256":"46488eac7973395e0d098d483812af251bcb29ec11b0e75c68a7d6cd04274930","block_sha256":"46488eac7973395e0d098d483812af251bcb29ec11b0e75c68a7d6cd04274930","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c6836cc6-ab6c-4c86-9650-aef89ad94ab7"},{"id":"occ_019bcf0915715fb7065289f7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c72d50a4-abe7-49b2-bf8d-4268a3a3421b","section_id":"sec_0d381801-36d5-45d4-a35a-0638c327aa9e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":64,"end":66,"exact":"推論","quote":"、基本的にはクラウド中心です。  \nただし今後は、スマホ、PC、自動車、ロボット、工場、監視カメラなどにもAI推論が広がります。","quote_start":9,"quote_end":73,"text_sha256":"eb423a54310bca817c55b26c84ddd71379e8dab2d8d1c3d3b9ba7982bc68f6ee","block_sha256":"eb423a54310bca817c55b26c84ddd71379e8dab2d8d1c3d3b9ba7982bc68f6ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c72d50a4-abe7-49b2-bf8d-4268a3a3421b"},{"id":"occ_d0d244f0c9257715f151dc22","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c777f357-8de3-47e3-8207-be009070f4b9","section_id":"sec_17d19a96-2685-4011-8824-dfda4605ffd2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"普通のGPUは大量の汎用並列計算に強いですが、LLM推論では、","quote_start":0,"quote_end":31,"text_sha256":"ca3ab363bc30bff714b449be129127f9dc7d968a65bfa4cd822db1805087c646","block_sha256":"ca3ab363bc30bff714b449be129127f9dc7d968a65bfa4cd822db1805087c646","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c777f357-8de3-47e3-8207-be009070f4b9"},{"id":"occ_b2f3d9ef776fdb51becde7b2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c86dbd25-a2e2-491e-a4f3-ce054915806c","section_id":"sec_2c3bae8a-7a17-469a-b63b-2faea427c6f5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"> **GPUクラウドが万能基盤、TPUクラウドが大規模AI専用基盤、LPUクラウドが低遅延推論基盤**","quote_start":0,"quote_end":52,"text_sha256":"cda6f7a1946e9b87b0f68976e137e74da841f37b2c33c47411f5e3c2ed2afa04","block_sha256":"cda6f7a1946e9b87b0f68976e137e74da841f37b2c33c47411f5e3c2ed2afa04","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c86dbd25-a2e2-491e-a4f3-ce054915806c"},{"id":"occ_23302ebbc06e9a5ad6fa0863","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c8c934d4-c639-46a8-a541-55590e4d78b3","section_id":"sec_7e6570d5-47c1-4c8d-8c40-b67e49433c73","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"GPUクラウドが成熟すると、1単位のAI推論に必要なGPU枚数やCPU補助処理は減る可能性があります。つまり、**同じAI処理量に必要なサーバー台数は減る**かもしれません。","quote_start":0,"quote_end":87,"text_sha256":"a52a2f5787674071a1e91ee0fee805f4214217aae09f79f5e7459cb6579d186a","block_sha256":"a52a2f5787674071a1e91ee0fee805f4214217aae09f79f5e7459cb6579d186a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c8c934d4-c639-46a8-a541-55590e4d78b3"},{"id":"occ_8d9576c903295d5971a39e51","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c8e6e5a0-8da9-4b16-9ab6-3b76c61aabf4","section_id":"sec_0ddaeb93-1732-4096-96c9-c776a8183e73","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":58,"end":66,"exact":"KV cache","quote":"かく分ける  \n> 動的に配置する  \n> 複数顧客で安全に共有する  \n> トークン単位で課金する  \n> KV cacheを管理する  \n> レイテンシSLOを保証する  \n> 障害時に復旧する  \n> 稼働率を常時監視する","quote_start":3,"quote_end":117,"text_sha256":"7534f7ead2a087ecb946cc79a6e1c1f85b0418360035e454d0a59462ab5ec818","block_sha256":"7534f7ead2a087ecb946cc79a6e1c1f85b0418360035e454d0a59462ab5ec818","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c8e6e5a0-8da9-4b16-9ab6-3b76c61aabf4"},{"id":"occ_e910e004db597c8eda8dd82f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c9c1fdf8-207d-4645-a796-6b527f2e8c16","section_id":"sec_3751a969-a9f8-4805-a45c-1cb291ed56d3","layer":"body","character_id":null,"count":2,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":49,"end":51,"exact":"推論","quote":"ただし、これは「全部の処理が3〜10倍になる」という意味ではありません。  \nあくまで、**LLM推論・生成・KV cache管理・バッチング・GPUプーリングの部分**です。","quote_start":0,"quote_end":88,"text_sha256":"e5c2d67a69d4f38550b86c894a3ef92ab4dcd2e3cd0e9d6d8bec5efc6a6adaac","block_sha256":"e5c2d67a69d4f38550b86c894a3ef92ab4dcd2e3cd0e9d6d8bec5efc6a6adaac","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c9c1fdf8-207d-4645-a796-6b527f2e8c16"},{"id":"occ_f4cc7ce91c85be9cd1c42bed","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_c9d298d2-b812-45f8-826d-ad405fe11296","section_id":"sec_6469f556-42cd-4404-803b-318074aba2d8","layer":"code","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":29,"end":31,"exact":"推論","quote":"```\nGPUクラウド\n  汎用AIクラウド。\n  学習、推論、画像、動画、VLA、HPCまで担当。\n  稼働率改善余地が最大。\n\nTPUクラウド\n  Google型の高効率AIクラウド。\n  大規模学習・推論で高MFUを狙う。\n  用途はGPUより狭いが、はま","quote_start":0,"quote_end":131,"text_sha256":"67c6aeaf704d766a6a6d907036345cdcc8f5d15ccd4c977f12ee8b2483aeefbb","block_sha256":"67c6aeaf704d766a6a6d907036345cdcc8f5d15ccd4c977f12ee8b2483aeefbb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_c9d298d2-b812-45f8-826d-ad405fe11296"},{"id":"occ_91d136d9ffd64c9394261a69","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_cb3463af-9be5-46fe-b2e7-1c30ad938027","section_id":"sec_0bdbe1a5-d81e-4c96-aaab-c4202201d851","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"## \\5. データセンター規模の推論オーケストレーション","quote_start":0,"quote_end":29,"text_sha256":"50ca406de84bcb34f38505eb4c5653f71fc56f732a76fa59fb76c37e82f933f5","block_sha256":"50ca406de84bcb34f38505eb4c5653f71fc56f732a76fa59fb76c37e82f933f5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_cb3463af-9be5-46fe-b2e7-1c30ad938027"},{"id":"occ_512fac5efde87dfa42912c7b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ccf80481-8c1d-4b46-8914-def6aa1e7ae0","section_id":"sec_1a856153-da5e-4157-a30b-5b01ab76a58b","layer":"code","character_id":null,"count":2,"matched_aliases":["decode","prefill"],"evidence":{"text_basis":"markdown","start":28,"end":35,"exact":"prefill","quote":"```\nこのリクエストはGPUではなくLPUへ\nこの長文prefillはGPUクラスタAへ\ndecodeは別GPUプールへ\n社内機密データなのでconfidential GPUへ\n低価格プランなのでbatch queueへ\n```","quote_start":0,"quote_end":115,"text_sha256":"38be8844a472e18a40a3663f250013742bf94c47b04de93fc250cdbbd9038b78","block_sha256":"38be8844a472e18a40a3663f250013742bf94c47b04de93fc250cdbbd9038b78","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ccf80481-8c1d-4b46-8914-def6aa1e7ae0"},{"id":"occ_edbd27538f0cac1cbb24250d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_cd1b8d9e-be45-4170-a13e-1f8529db1fd8","section_id":"sec_a7efb4db-4fd8-48f3-8c3d-f7588df3d8dc","layer":"body","character_id":null,"count":3,"matched_aliases":["decode","prefill","推論"],"evidence":{"text_basis":"markdown","start":13,"end":20,"exact":"prefill","quote":"またDistServeは、prefillとdecodeを分離することでLLM推論の干渉を減らす方式で、既存方式より多くのリクエストを処理できることを示しています。([arXiv](https://arxiv.org/abs/2401.096","quote_start":0,"quote_end":120,"text_sha256":"04019567e6b68ff75ed64b236d61e5a59c4a24576ee641c68c9f417cf20e40b3","block_sha256":"04019567e6b68ff75ed64b236d61e5a59c4a24576ee641c68c9f417cf20e40b3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_cd1b8d9e-be45-4170-a13e-1f8529db1fd8"},{"id":"occ_f1fd7143d853c992c2671695","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ce6d64a8-b661-4322-ac1b-277f08a3dee9","section_id":"sec_3659146e-a906-43e3-add4-8d543a5746da","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"CoreWeaveは2026年第1四半期に売上20.8億ドルを報告し、市場予想を上回りました。AI学習・推論向けの高性能クラウド需要が急増しており、同社の受注残は約994億ドルまで拡大したと報じられています。([Reuters](https://www.reuters.com/technology/core","quote_start":0,"quote_end":154,"text_sha256":"3cfdc4f062f20a5cf566c04c48cd5d879444f643bbba30fb1a64a5c41f2d1dfc","block_sha256":"3cfdc4f062f20a5cf566c04c48cd5d879444f643bbba30fb1a64a5c41f2d1dfc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ce6d64a8-b661-4322-ac1b-277f08a3dee9"},{"id":"occ_0079023c102b771afcaf8ff3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_cfa21689-28ee-4dbe-9d2a-754b4dbc2b4a","section_id":"sec_eee898d1-c2cb-4447-b7e5-fb9e261cab0a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"## \\4. 一番進んでいるのは「LLM推論最適化」","quote_start":0,"quote_end":26,"text_sha256":"43b7cec08131bb7a8996e975da76603ff084ab5769e4b7958011b483ea0e5944","block_sha256":"43b7cec08131bb7a8996e975da76603ff084ab5769e4b7958011b483ea0e5944","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_cfa21689-28ee-4dbe-9d2a-754b4dbc2b4a"},{"id":"occ_3a29fdc2b141e02c1aa17879","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_d08bcf27-668d-44df-bcbf-a8bd35703af2","section_id":"sec_c42c4247-a0ca-40b2-9db5-720eeb1343be","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":31,"end":39,"exact":"KV cache","quote":"これはまさに、**CPUクラウドの仮想メモリ技術を、GPU上のKV cache管理に持ち込んだ例**です。","quote_start":0,"quote_end":53,"text_sha256":"2da44693c0626c71c87dab2c190021c2a1e10b3ac3e6a48c937bec1863f8e336","block_sha256":"2da44693c0626c71c87dab2c190021c2a1e10b3ac3e6a48c937bec1863f8e336","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_d08bcf27-668d-44df-bcbf-a8bd35703af2"},{"id":"occ_aab6c00476ebd9636d6bc734","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_d125a92f-848f-413e-8136-9d6b0e1abc1b","section_id":"sec_f8ea86dd-d6e2-4341-8811-d80d90926e85","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"また、NVIDIA Dynamoは2025年に発表された大規模分散推論向けのフレームワークで、単一GPUや単一ノードの最適化ではなく、データセンター規模のGPU群を協調した推論システムとして扱う方向です。NVIDIAはDynamoについて、reasoning model","quote_start":0,"quote_end":135,"text_sha256":"25cd22477ddf2ef3c81963758c8c9a10455fed904717ac84fa1fc4b1d64091a7","block_sha256":"25cd22477ddf2ef3c81963758c8c9a10455fed904717ac84fa1fc4b1d64091a7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_d125a92f-848f-413e-8136-9d6b0e1abc1b"},{"id":"occ_f2aac60841a64eb441cbba74","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_d15644f4-49bc-480f-abe5-55f4082897bb","section_id":"sec_b3015863-c634-42f8-bf90-57c765dba5e5","layer":"body","character_id":null,"count":1,"matched_aliases":["prefill"],"evidence":{"text_basis":"markdown","start":37,"end":44,"exact":"prefill","quote":"## \\3. continuous batching / chunked prefill","quote_start":0,"quote_end":44,"text_sha256":"12af833668a0baf144d1807ab78b90983b63beae1a5bcc94d49ec880c233d8ae","block_sha256":"12af833668a0baf144d1807ab78b90983b63beae1a5bcc94d49ec880c233d8ae","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_d15644f4-49bc-480f-abe5-55f4082897bb"},{"id":"occ_c61e7afe7f793006c66b30a3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_d5811cf9-9bab-499d-a77a-e25549d016ad","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":11,"end":13,"exact":"推論","quote":"GPU時代、特にLLM推論では、スケール単位が「サーバー台数」だけではありません。","quote_start":0,"quote_end":41,"text_sha256":"b85f9b5c99607334fe4e7fa89a332e73ca395509270472f8bd4f24ec814ae2d0","block_sha256":"b85f9b5c99607334fe4e7fa89a332e73ca395509270472f8bd4f24ec814ae2d0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_d5811cf9-9bab-499d-a77a-e25549d016ad"},{"id":"occ_aba4a8c57591dc5f0aa6b088","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_d7b510da-9b55-465d-810a-1c9408e482e0","section_id":"sec_2fcdbe46-ebad-470b-b6ea-94ffa48a219c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":99,"end":101,"exact":"推論","quote":"、EPYC CPU需要とInstinct GPU出荷が牽引したと説明しています。またLisa Su CEOは、推論とagentic AIが高性能CPUとアクセラレータ需要を押し上げていると述べています。([Advanced Micro Devices, Inc.](https://ir.amd.com/news-","quote_start":44,"quote_end":201,"text_sha256":"ac2542d671d4e345656bb98ce21ee872aa2830d0d567e1b12be628c651cd8feb","block_sha256":"ac2542d671d4e345656bb98ce21ee872aa2830d0d567e1b12be628c651cd8feb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_d7b510da-9b55-465d-810a-1c9408e482e0"},{"id":"occ_db102769de7c8547ce0aed59","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_dade23c3-517e-43c9-a8f3-f912708ba34d","section_id":"sec_f9df470f-5155-42ec-97eb-cf96aea7d2de","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"ただし、ここまで行くには時間がかかります。  \n理由は、LLM推論がまだ急速に変化しているからです。モデル構造、context length、MoE、reasoning、動画生成、VLA、エージェント処理が変わるたびに、GPUクラウド側も作り直しになります。","quote_start":0,"quote_end":129,"text_sha256":"09e23b43105656ef68a55b243744aae1d256440432e8082b346b3346328af18d","block_sha256":"09e23b43105656ef68a55b243744aae1d256440432e8082b346b3346328af18d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_dade23c3-517e-43c9-a8f3-f912708ba34d"},{"id":"occ_8ebf1eb891b2758e227ba70c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_db5e4bdb-30e5-4bae-aba8-8230eb411083","section_id":"sec_2742df87-267e-4cad-a541-d9704836f064","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":99,"end":107,"exact":"KV cache","quote":"et帯域\n- SM occupancy\n- queue time\n- TTFT\n- TPOT / ITL\n- KV cache hit率\n- batch効率\n- tenant別コスト\n- モデル別原価\n- 部門別GPU消費","quote_start":44,"quote_end":155,"text_sha256":"a695a9f0a9ea9f6166896b9e1fb563728a57ca140cf057067a4b1f90dfd825ad","block_sha256":"a695a9f0a9ea9f6166896b9e1fb563728a57ca140cf057067a4b1f90dfd825ad","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_db5e4bdb-30e5-4bae-aba8-8230eb411083"},{"id":"occ_3bcb221bb14b6c58112060e1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e0d165f9-d738-4dc0-95bf-4e1ecc487f1f","section_id":"sec_a37d9e6f-78cd-4295-a307-8ed1497ab91e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"しかし、AI推論が安くなることで利用量が爆発するため、**2030年代前半までは世界全体のGPU需要は縮小しにくい**と思います。","quote_start":0,"quote_end":65,"text_sha256":"00ef2e068ff833b8ef2a23cadc2fc9e0ffd089bf4ba20aca7663376d9ff44fb6","block_sha256":"00ef2e068ff833b8ef2a23cadc2fc9e0ffd089bf4ba20aca7663376d9ff44fb6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e0d165f9-d738-4dc0-95bf-4e1ecc487f1f"},{"id":"occ_5c2fa8103445efc1ab679c2e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e0d8ed6a-0c1f-4ae3-9dca-9db549936b47","section_id":"sec_f4b10071-2e20-4e00-b1ba-2d1c3dd83a74","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":139,"end":141,"exact":"推論","quote":"\n  → GPUクラウド\n\n低遅延チャット、音声対話、軽量エージェント\n  → LPUクラウド\n\n企業向けAI推論API\n  → GPU / LPU / TPU の混在\n\nフィジカルAI・VLA学習\n  → GPUクラウド中心、一部TPU/ASIC\n\nエッジAI\n  → NPU / 専用ASIC / 小型GPU\n","quote_start":84,"quote_end":241,"text_sha256":"2456538722254db1f69cc8675621c32e6331c79dbbb6b30dce00da0586554513","block_sha256":"2456538722254db1f69cc8675621c32e6331c79dbbb6b30dce00da0586554513","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e0d8ed6a-0c1f-4ae3-9dca-9db549936b47"},{"id":"occ_e1677728dc85995f0cf21de7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e22975dc-681b-4c52-9989-5a27807ad4a0","section_id":"sec_d1392b3f-47cb-4ea3-ae91-dd03d42cd72d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"- GPUホストCPU\n- AIクラウド制御CPU\n- 推論API用CPU\n- セキュリティ/認証/課金/監視用CPU\n- ストレージ・ネットワーク制御用CPU\n- Arm CPUやDPUを含む広義のCPU的処理","quote_start":0,"quote_end":106,"text_sha256":"10f27c91db98a5e5fc35a3f1e393d17e8532807a0f620ea9515907c57d2c9962","block_sha256":"10f27c91db98a5e5fc35a3f1e393d17e8532807a0f620ea9515907c57d2c9962","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e22975dc-681b-4c52-9989-5a27807ad4a0"},{"id":"occ_51c6b0fc2055d4fa70f24f73","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e299e1f2-56c3-49b7-a7f2-f253a9b836b1","section_id":"sec_e5e44156-0b06-4dce-8372-0aca3f2d053a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論には大きく2段階あります。","quote_start":0,"quote_end":18,"text_sha256":"c56d2b8da38eeda76dbaab1a33c3cdeee107fb6a5ef469ae652eb4697f4d555c","block_sha256":"c56d2b8da38eeda76dbaab1a33c3cdeee107fb6a5ef469ae652eb4697f4d555c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e299e1f2-56c3-49b7-a7f2-f253a9b836b1"},{"id":"occ_df1b06f8b50cfa1f8572ba82","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e318ca03-daa1-4a33-93ff-487e3c94689e","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論で本当に増減するのは、","quote_start":0,"quote_end":16,"text_sha256":"ce48093396cba1ef953f6fd12e85d190657bb6493f50605ad8c713b12c1e193f","block_sha256":"ce48093396cba1ef953f6fd12e85d190657bb6493f50605ad8c713b12c1e193f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e318ca03-daa1-4a33-93ff-487e3c94689e"},{"id":"occ_092e90e3569b71e6e7e2ada4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e4b8b4b8-d570-48ef-90eb-830c3c3cf172","section_id":"sec_b50c6acb-2ce6-4849-9963-052cf62a540b","layer":"body","character_id":null,"count":4,"matched_aliases":["KV 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token単位スケジューリ","quote_start":5,"quote_end":162,"text_sha256":"cb70103425bc17478e20ddb2a88f51621b0c3cffbc2577bda079d94e017a5cf6","block_sha256":"cb70103425bc17478e20ddb2a88f51621b0c3cffbc2577bda079d94e017a5cf6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e4b8b4b8-d570-48ef-90eb-830c3c3cf172"},{"id":"occ_05406ec6f913991a43c48eda","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e5ab2a48-f67d-4472-9389-0f44acbfbfa4","section_id":"sec_b3015863-c634-42f8-bf90-57c765dba5e5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"これはエージェント時代の「大量の細かい推論を詰め込む」技術です。","quote_start":0,"quote_end":32,"text_sha256":"6ea18a198e7cbf1c053569bd66b30bcc999290f7edcc8f5643f306e65c259da6","block_sha256":"6ea18a198e7cbf1c053569bd66b30bcc999290f7edcc8f5643f306e65c259da6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e5ab2a48-f67d-4472-9389-0f44acbfbfa4"},{"id":"occ_a8d1c2d95d5f224eff92656b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e7263bc6-fe6e-4a63-b2bd-0462f15fa0f6","section_id":"sec_56739c37-b595-4789-84f9-79ef964cef67","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"つまりTPUクラウドは、単なる「小さいAI推論用」ではなく、**巨大AIモデルの学習・推論を行うクラウド基盤**です。","quote_start":0,"quote_end":59,"text_sha256":"d4c1a3f999f5a42f36544064e9e6e8e7e515989e89b820aae31cc2c9e717a97a","block_sha256":"d4c1a3f999f5a42f36544064e9e6e8e7e515989e89b820aae31cc2c9e717a97a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e7263bc6-fe6e-4a63-b2bd-0462f15fa0f6"},{"id":"occ_dc887be9aa8570b5f5fd3068","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e7c96e83-6d96-42cf-9255-665c0e4a4022","section_id":"sec_2132be84-3261-41a6-a850-a4634816410b","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"NVIDIAも、単にGPU枚数を増やすだけではなく、ラック全体、ネットワーク、DPU、CPU、メモリ階層、推論ソフトを統合して、1ラックあたりの推論能力を上げる方向に進んでいます。","quote_start":0,"quote_end":90,"text_sha256":"e48bc48725e2259b87d11b8939c309823c28b78d1ef0af224e80c3d63d2ef0b7","block_sha256":"e48bc48725e2259b87d11b8939c309823c28b78d1ef0af224e80c3d63d2ef0b7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e7c96e83-6d96-42cf-9255-665c0e4a4022"},{"id":"occ_43dd9a0f14606aa72a3c4321","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e8552278-3e19-481c-8b35-83673daa064a","section_id":"sec_d2e0b6e3-e6ee-4ed5-b94a-2aeff971584a","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":88,"end":90,"exact":"推論","quote":"して、ホストCPU・クラウド制御CPUが増える。\n\n2027〜2030年\n伸び率のピーク。\nAIエージェント、推論爆発、GPUクラウドOS化でCPU需要は非常に強い。\nただし、EPYC、Grace、Arm、DPUなどに選別が進む。\n\n2030〜2035年\nGPUクラウド成熟で1処理あたりのCPU必要量は下がる。\n","quote_start":33,"quote_end":190,"text_sha256":"c729fcd95af552c16cd1fbb08c5965f64d6686433ec77b9bd6062385a17c008e","block_sha256":"c729fcd95af552c16cd1fbb08c5965f64d6686433ec77b9bd6062385a17c008e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e8552278-3e19-481c-8b35-83673daa064a"},{"id":"occ_500f1f3753fb448a501a32a8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_e89fa7ab-82a5-4bb9-b13c-5a4838b1b582","section_id":"sec_b5001920-30e7-4546-b243-794b28e5a3ea","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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cache再利用、構造化出力の高速化などを使い、複数のLLM/マルチモーダルタスクで最大6.4倍のスループット改善を報告しています。([arXiv](https://arxiv.org/abs/2312.071","quote_start":24,"quote_end":187,"text_sha256":"79ab0ab2772846673c3c8da21f884e46f48e4da9157019b9af06958d73ac7d7f","block_sha256":"79ab0ab2772846673c3c8da21f884e46f48e4da9157019b9af06958d73ac7d7f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_e89fa7ab-82a5-4bb9-b13c-5a4838b1b582"},{"id":"occ_9c5165626ed913650f08e644","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ea97df2b-9a11-4420-a0a7-1061e8132f16","section_id":"sec_efae00ac-e1e8-4909-902d-102abd79a201","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論では、以下を見ます。","quote_start":0,"quote_end":15,"text_sha256":"431906a2d7a1cca6617ad19484de717e60d114c0e2cdb7e7058e81ed506ac11b","block_sha256":"431906a2d7a1cca6617ad19484de717e60d114c0e2cdb7e7058e81ed506ac11b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ea97df2b-9a11-4420-a0a7-1061e8132f16"},{"id":"occ_74e6297e4e521fe0e6a90571","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ed7705a5-2c94-4524-bc99-92506f73e746","section_id":"sec_0bdbe1a5-d81e-4c96-aaab-c4202201d851","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":98,"end":100,"exact":"推論","quote":"、生成AIやreasoning modelを大規模分散環境で低遅延・高スループットに動かすためのオープンソース推論フレームワークとして説明しており、DeepSeek-R1系モデルで最大30倍のリクエスト処理改善を主張しています。([NVIDIA Developer](https://developer.nvidi","quote_start":43,"quote_end":200,"text_sha256":"9a0fd93b2cb172ee82aa52b1bafea181285d96e420621fdcc86e23b237bf8071","block_sha256":"9a0fd93b2cb172ee82aa52b1bafea181285d96e420621fdcc86e23b237bf8071","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ed7705a5-2c94-4524-bc99-92506f73e746"},{"id":"occ_1cab1aaec3d4def63ee33f3b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ede8b715-f38e-429f-9cbb-84e7097ddd8f","section_id":"sec_fd55f986-652f-4cc3-8cf4-54af3e755e17","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"その中でGPUは最も汎用的な中心に残り、TPUはGoogle系・大規模AIに強く、LPUは低遅延推論で存在感を持つ、という分担になると思います。","quote_start":0,"quote_end":72,"text_sha256":"1e6accfd2d2ab37210c6a334040e6538c796cb2775cfb733d7281dbba34753a6","block_sha256":"1e6accfd2d2ab37210c6a334040e6538c796cb2775cfb733d7281dbba34753a6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ede8b715-f38e-429f-9cbb-84e7097ddd8f"},{"id":"occ_1203cb6a98de45db26d374cc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_ee898220-0f29-46b3-81cc-f090cb21b6c8","section_id":"sec_a37d9e6f-78cd-4295-a307-8ed1497ab91e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"そして最終的には、  \n**単純な推論はLPU/TPU/ASIC/NPUへ分散し、GPUは学習・動画生成・VLA・マルチモーダル・科学AIのような重い領域に集中する**  \nという流れになると考えます。","quote_start":0,"quote_end":101,"text_sha256":"30c16720a02abbe1e5fb47f5f20f3e401d529ad7f95b4f56102ca2491d92f4ea","block_sha256":"30c16720a02abbe1e5fb47f5f20f3e401d529ad7f95b4f56102ca2491d92f4ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_ee898220-0f29-46b3-81cc-f090cb21b6c8"},{"id":"occ_5717723430829229cf880047","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_efe7c549-41b3-4f57-ae2d-3bfa6bfec415","section_id":"sec_a3051e28-451e-48d2-af67-ff6cfe6609f9","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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kernel、GPUドライバ、ネットワークDMAまで守る**必要があります。","quote_start":6,"quote_end":119,"text_sha256":"c722cf03bc696b8863012e4c995c4128d1d1f87b2580709fc832c7cae5a51de3","block_sha256":"c722cf03bc696b8863012e4c995c4128d1d1f87b2580709fc832c7cae5a51de3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_efe7c549-41b3-4f57-ae2d-3bfa6bfec415"},{"id":"occ_f7709d57aeb9002fa72e0077","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f01ae258-5833-4740-9816-811c2f803b03","section_id":"sec_76fbe14c-dc85-41f9-b2fa-c3fb2cdd2f77","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":149,"end":151,"exact":"推論","quote":"atorでMIG/time-slicingを扱う\n- vLLM/SGLang/TensorRT-LLMでLLM推論を高効率化する\n- Prometheus/Grafana/DCGMなどでGPU監視する\n- token課金・GPU時間課金を組み合わせる\n- 一部のConfidential Computingを使う","quote_start":94,"quote_end":250,"text_sha256":"c018b34e82f627d6ffc449631771b94b9e35904eef9231b788b14cf2bb7e50e0","block_sha256":"c018b34e82f627d6ffc449631771b94b9e35904eef9231b788b14cf2bb7e50e0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f01ae258-5833-4740-9816-811c2f803b03"},{"id":"occ_1b28a3423347d6d8d461501f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f0a1393e-391d-420b-a3f5-abedddd5834b","section_id":"sec_e4db8852-d772-4f64-9b92-9c34d2510aff","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":47,"end":55,"exact":"KV cache","quote":"CPUクラウドではメモリやディスクを使った分だけ払うのが自然でした。  \nGPUクラウドでは、KV cache、長文コンテキスト、エージェントの作業状態、マルチモーダル中間表現をどれだけ保持するかが、原価と性能を大きく左右します。","quote_start":0,"quote_end":115,"text_sha256":"d5aff31fc7882354dd324888420d84058e49461ce25e8a23ddf9e3008db48e91","block_sha256":"d5aff31fc7882354dd324888420d84058e49461ce25e8a23ddf9e3008db48e91","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f0a1393e-391d-420b-a3f5-abedddd5834b"},{"id":"occ_69dd8268ac9ebb537d0a8477","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f338f3be-a6ca-4cd9-a8d5-44b80a63627b","section_id":"sec_73f53de8-0e73-446a-8f6d-0cbb434a19a5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":50,"end":52,"exact":"推論","quote":"これは、CPUクラウドでいう「Webサーバー、DB、キャッシュ、キューを分ける」ような発想を、LLM推論の内部フェーズに適用したものです。","quote_start":0,"quote_end":69,"text_sha256":"52fda3e2e20fa9a5514f021b6dccc57fea7c57d91fcf29d5f264951cf3925966","block_sha256":"52fda3e2e20fa9a5514f021b6dccc57fea7c57d91fcf29d5f264951cf3925966","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f338f3be-a6ca-4cd9-a8d5-44b80a63627b"},{"id":"occ_11154749e50eda1163a1f91f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f3b5173f-1948-4cd9-91ae-5ccc224effff","section_id":"sec_ef062e6d-3775-429a-af3d-593eebfdedfa","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"なので、**GPU低利用率問題は、CPU需要、ネットワーク需要、セキュリティ需要、クラウド管理ソフト需要、推論最適化需要を同時に押し上げるテーマ**です。","quote_start":0,"quote_end":77,"text_sha256":"38703d173bbf4574b1e6cc39573eca842a463e05f82d789a72ecc5a7d92087f2","block_sha256":"38703d173bbf4574b1e6cc39573eca842a463e05f82d789a72ecc5a7d92087f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f3b5173f-1948-4cd9-91ae-5ccc224effff"},{"id":"occ_cd68acf33f2f032f1deaf0a7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f4ed24dc-5e65-4c71-aba5-a48f25599746","section_id":"sec_2c3bae8a-7a17-469a-b63b-2faea427c6f5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":63,"end":65,"exact":"推論","quote":"FU / Model FLOPs Utilization**。  \nこれは「理論ピーク性能のうち、モデル学習・推論に有効に使われた割合」です。","quote_start":8,"quote_end":79,"text_sha256":"38df9f1cb1557e9c2b0778906fb253988b9fecaf0016da0ac95c7bff63029f56","block_sha256":"38df9f1cb1557e9c2b0778906fb253988b9fecaf0016da0ac95c7bff63029f56","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f4ed24dc-5e65-4c71-aba5-a48f25599746"},{"id":"occ_ba416f78dbda11d5d3d607b9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f5140a03-f7bc-4235-b572-73bed20b9a3b","section_id":"sec_eee898d1-c2cb-4447-b7e5-fb9e261cab0a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"ただし、これはLLM推論に限った話です。  \n画像生成、動画生成、VLA、学習、マルチテナント安全共有まで含めると、まだ成熟度は下がります。","quote_start":0,"quote_end":70,"text_sha256":"7e284e7c6e1434057f0b905e4ac16eb489c3e55a711d6ba021827631d50162c8","block_sha256":"7e284e7c6e1434057f0b905e4ac16eb489c3e55a711d6ba021827631d50162c8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f5140a03-f7bc-4235-b572-73bed20b9a3b"},{"id":"occ_5d6e7785180abd987e64917b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f8293695-228d-47dc-9cb7-974202d9815c","section_id":"sec_681b6fd1-07d7-4683-973d-dc04eb7e16a2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"GPUクラウドとは、クラウド上でGPUサーバーを借り、AI学習、AI推論、画像生成、動画生成、科学計算、ロボティクス/VLA学習などを行う仕組みです。","quote_start":0,"quote_end":75,"text_sha256":"3cd7cbead642b6e16db7e2e4289109bfe4ea010f66e3af7885e6f964d1680119","block_sha256":"3cd7cbead642b6e16db7e2e4289109bfe4ea010f66e3af7885e6f964d1680119","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f8293695-228d-47dc-9cb7-974202d9815c"},{"id":"occ_2a98519cf7d6a0fac96a49b5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f8a631eb-ead2-4581-b580-2f76f7044e95","section_id":"sec_c36d4c47-1622-4afc-81a9-fc71567aa0f3","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":44,"end":52,"exact":"KV cache","quote":"> **仮想GPU、GPU-aware Kubernetes、LLMジョブスケジューラ、KV cacheルーティング、token単位課金、GPU confidential computing、サイドチャネル対策**","quote_start":0,"quote_end":107,"text_sha256":"2e5e7b6d4bf70641f6223d5cdf7042710df70f3a05a947076aeade5ebe31dee8","block_sha256":"2e5e7b6d4bf70641f6223d5cdf7042710df70f3a05a947076aeade5ebe31dee8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f8a631eb-ead2-4581-b580-2f76f7044e95"},{"id":"occ_c76a0b796c11b647d6d15442","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_f8a87769-61a1-4d18-8b23-5be142c309e8","section_id":"sec_d327044c-0584-4ee3-8b00-ff41c96c0411","layer":"code","character_id":null,"count":3,"matched_aliases":["KV cache","decode","prefill"],"evidence":{"text_basis":"markdown","start":4,"end":12,"exact":"KV cache","quote":"```\nKV cacheを整理する\nbatchを組み直す\nprefill専用GPUを増やす\ndecode専用GPUを増やす\n低優先度ジョブを遅らせる\n小型モデルへfallbackする\nLPU/TPU/GPUを切り替える\n``","quote_start":0,"quote_end":112,"text_sha256":"afbc4dc276c46613b2f6d7a3038252120efad94ce00f8f2f419e8dcaaa5d3959","block_sha256":"afbc4dc276c46613b2f6d7a3038252120efad94ce00f8f2f419e8dcaaa5d3959","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_6cb909ec-1291-4cbb-a768-f72123829d08/#blk_f8a87769-61a1-4d18-8b23-5be142c309e8"},{"id":"occ_ec902635c23b0244623ab4f9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_6cb909ec-1291-4cbb-a768-f72123829d08","work_id":"wrk_72dcc676-6c79-475a-9f86-c4f174e32af2","block_id":"blk_fd258adb-1178-4adf-8da4-41910c857779","section_id":"sec_a7efb4db-4fd8-48f3-8c3d-f7588df3d8dc","layer":"body","character_id":null,"count":4,"matched_aliases":["KV 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NIC帯域\n* PCIeスイッチの段数\n* leaf-spineの混雑\n* リンク障害\n* HBM容量\n* KVキャッシュ\n* メモリコピー\n* 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HBMへ置くことが難しい。使用頻度の高いデータはHBMへ、次に使う可能性の高いデータはDRAMやCXLプールへ、さらに低頻","quote_start":0,"quote_end":104,"text_sha256":"94f2d76bed971f6c19ae38d292b8f0330168e2c9dde1149d9d035a193e3e3227","block_sha256":"94f2d76bed971f6c19ae38d292b8f0330168e2c9dde1149d9d035a193e3e3227","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_70df7bb7-127f-4402-9374-f897f873bafb/#blk_0a64bbb1-5829-4ff4-b56b-0e298b0f8c9c"},{"id":"occ_90e585e4293a6ce06cd60a73","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_70df7bb7-127f-4402-9374-f897f873bafb","work_id":"wrk_bd032fe1-66fd-4ecd-a7db-e046051c8671","block_id":"blk_2e931f4f-b381-45ac-b7a5-9214333a2fcf","section_id":"sec_9fb15af7-5866-4947-9f7c-1e2fd3c0e62f","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":133,"end":140,"exact":"KVキャッシュ","quote":" DRAM | RDIMM、MRDIMM、CXL、CPUサーバー |\n| NAND / eSSD | RAG、KVキャッシュ、チェックポイント、動画生成 |\n| 車載メモリー | ADAS、SDV、長期認証、供給安定性 |\n| 地域供給網 | 中国内製化、米国近接生産、東南アジア分散 |\n| 電力と施設 | 300MW級キ","quote_start":78,"quote_end":240,"text_sha256":"f08b44ba1b61a6d970217de6b2d6ce8f1100bf27779aab99b022f1c4113300b2","block_sha256":"f08b44ba1b61a6d970217de6b2d6ce8f1100bf27779aab99b022f1c4113300b2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_70df7bb7-127f-4402-9374-f897f873bafb/#blk_2e931f4f-b381-45ac-b7a5-9214333a2fcf"},{"id":"occ_95e95f1b6dd78c7631c389d8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_328d67e7-d55d-4831-821e-0ff0967b7448","section_id":"sec_170951f2-bad9-4ce9-aecd-683d94a978e7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"ここでは短期的な推論単価より、技術先行、安全保障、市場支配の価値が優先される。","quote_start":0,"quote_end":39,"text_sha256":"7b9d66006bf0681db3abea8eebd4fa228fc9a52f8d9f65652e758da9dc1a14d6","block_sha256":"7b9d66006bf0681db3abea8eebd4fa228fc9a52f8d9f65652e758da9dc1a14d6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_328d67e7-d55d-4831-821e-0ff0967b7448"},{"id":"occ_244b6a7a80e3702733a0751e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_3b6ed3c1-dcce-40d4-b062-efb7d9e2e1b1","section_id":"sec_9e51f881-1f48-4224-aeb7-8b7069f50b19","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":1,"end":3,"exact":"推論","quote":"が推論需要になる。","quote_start":0,"quote_end":9,"text_sha256":"296454ce8b85ca9a7c88299e670ae5d6d28220d85aa775ae76f3c304d8a03203","block_sha256":"296454ce8b85ca9a7c88299e670ae5d6d28220d85aa775ae76f3c304d8a03203","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_3b6ed3c1-dcce-40d4-b062-efb7d9e2e1b1"},{"id":"occ_ae95691d488fecd27e9e8ad3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_5cf8279a-5b0d-48a4-8939-fac8f39de7ec","section_id":"sec_256dfbe6-853a-452c-a0bf-74e5ccaa180c","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":476,"end":478,"exact":"推論","quote":"益化」\n「設備負担」\n「循環依存」\n「供給過剰リスク」\nを各10点で評価してください。\n\n事実、経営陣の主張、推論を明確に分離し、重要数値と発言時期を記載してください。\n```","quote_start":421,"quote_end":509,"text_sha256":"aead2175a3c29a19fc5e4ccfb0e947730cc9aa5838b4ee07b69879f7b966a9e4","block_sha256":"aead2175a3c29a19fc5e4ccfb0e947730cc9aa5838b4ee07b69879f7b966a9e4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_5cf8279a-5b0d-48a4-8939-fac8f39de7ec"},{"id":"occ_06d2ebb50b4b66ac2272c04c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_7ae5808b-928f-4453-ba60-2e48efa03d07","section_id":"sec_c1d1635a-97fd-4be3-a21b-7d75a7d1dfb1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":69,"end":71,"exact":"推論","quote":"GPUレンタル価格\n- 中古市場価格\n- 旧世代の電力当たり性能\n- 減損\n- 在庫評価損\n- 低価格・バッチ推論需要\n- 新世代導入後の旧世代予約率","quote_start":14,"quote_end":89,"text_sha256":"38ed9f3096c82a32d52821ad9f3887865e7b688e6706472a870b53d4aa56df57","block_sha256":"38ed9f3096c82a32d52821ad9f3887865e7b688e6706472a870b53d4aa56df57","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_7ae5808b-928f-4453-ba60-2e48efa03d07"},{"id":"occ_5d2c755f271b87019f1417c4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_8008b9cc-551d-4c41-ab25-fc0793b1c14f","section_id":"sec_6e0d26a1-950e-42e4-9e15-afa4c1db315a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論単価が10分の1になっても、利用回数が100倍になれば総計算需要は10倍になる。","quote_start":0,"quote_end":42,"text_sha256":"0ddf7d9dff0b47286ff4668d671523ddc930985c1c302f464e4c751356475ea1","block_sha256":"0ddf7d9dff0b47286ff4668d671523ddc930985c1c302f464e4c751356475ea1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_8008b9cc-551d-4c41-ab25-fc0793b1c14f"},{"id":"occ_9aa1b309fd1e36e4d7b0c046","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_8602a4a6-355a-4ecd-a12d-3ae0069d5b7d","section_id":"sec_b291f9db-4adf-4e44-877d-23c817eb77cf","layer":"code","character_id":null,"count":2,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":37,"end":46,"exact":"inference","quote":"```\nfrontier model price performance\ninference cost per token trend\nopen-weight enterprise adoption\ncustom ASIC share AI inference\nGPU versus TPU ","quote_start":0,"quote_end":146,"text_sha256":"9095dc058b1af565bd31184020a883e659b4729b7f376efabe8dc10b1cb36b51","block_sha256":"9095dc058b1af565bd31184020a883e659b4729b7f376efabe8dc10b1cb36b51","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_8602a4a6-355a-4ecd-a12d-3ae0069d5b7d"},{"id":"occ_712acfc57834034d8a03c206","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_8dc5a3c7-ea63-4dee-9c98-6cfe3f5a7553","section_id":"sec_178350bd-26ca-499c-8551-e477403319a3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"- より大きなモデル\n- より長い推論\n- より多くの強化学習\n- 合成データの生成\n- 多数のAI研究実験\n- 高性能な動画・音声生成\n- 常時稼働エージェント","quote_start":0,"quote_end":81,"text_sha256":"9e92cad6992db2adeac5e08c71c0255ed47de4cf7fe3bfdbca07b0c4769a7835","block_sha256":"9e92cad6992db2adeac5e08c71c0255ed47de4cf7fe3bfdbca07b0c4769a7835","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_8dc5a3c7-ea63-4dee-9c98-6cfe3f5a7553"},{"id":"occ_e5c100e2faee7a40784132cd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_9718cb42-5cf1-4b17-b117-1182d0fedebb","section_id":"sec_5d3d387f-48fe-44ca-9bb5-f3d7ca2f446c","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":349,"end":351,"exact":"推論","quote":"備購入へ | 株式、社債、出資、クラウド契約が計算購入へ |\n| 単価低下 | 通信費低下が利用量を増加 | 推論費低下が用途・エージェント数を増加 |\n| 過剰投資リスク | 需要到来前に回線を造りすぎる | 収益化前にAIDCを造りすぎる |\n```","quote_start":294,"quote_end":421,"text_sha256":"5b198f97230c3b3e843182a98cb9b95eeac3cb45b1c38e0c757d11bc03239298","block_sha256":"5b198f97230c3b3e843182a98cb9b95eeac3cb45b1c38e0c757d11bc03239298","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_9718cb42-5cf1-4b17-b117-1182d0fedebb"},{"id":"occ_eaf2fe2eabf95cfe1121d441","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_a8a7cf2c-52af-4535-a84d-b874f72f6adf","section_id":"sec_69df97cc-d027-43a7-a534-d2c83c2d9661","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"## 第3段階　推論単価を下げられる企業","quote_start":0,"quote_end":20,"text_sha256":"56af62d5622b681052edd00701dc31ddf03d29c3c0e760563679f5f0bea84aec","block_sha256":"56af62d5622b681052edd00701dc31ddf03d29c3c0e760563679f5f0bea84aec","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_a8a7cf2c-52af-4535-a84d-b874f72f6adf"},{"id":"occ_9268875d4c847b067ce3ba55","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_de8644b9-69e6-43f2-8448-3076e73d9cd1","section_id":"sec_170951f2-bad9-4ce9-aecd-683d94a978e7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"- 最大規模の学習\n- 強化学習\n- 自動AI研究\n- 科学シミュレーション\n- 国家機密処理\n- 長時間推論","quote_start":0,"quote_end":55,"text_sha256":"79902f3fc6e045da7848234b5bed4525e8e661f07d93457c46bddb969a12c3d5","block_sha256":"79902f3fc6e045da7848234b5bed4525e8e661f07d93457c46bddb969a12c3d5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_716e29e3-ca33-4e0a-89d8-a4c146c91139/#blk_de8644b9-69e6-43f2-8448-3076e73d9cd1"},{"id":"occ_341104eb7c793b9282d8358a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_716e29e3-ca33-4e0a-89d8-a4c146c91139","work_id":"wrk_6b2d4c96-c0d4-4d57-8d9e-9077ea538a79","block_id":"blk_e60f352b-82ff-4a14-bd03-8be6ef1ade06","section_id":"sec_ab0038a9-f702-4044-b057-fbd624bde8ca","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"- 推論単価の急落\n- モデルのコモディティ化\n- 旧GPUの陳腐化\n- 減価償却費の増加\n- 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 \n推論では、全部を最速メモリに置かなくてもよい場面があります。重みやデータの一部を、**HBMより大容量でSSDより高速な層**に持てれば、コストと性能のバランスが良くなる可能性があります。SanDiskの","quote_start":0,"quote_end":123,"text_sha256":"703d3acab2a4b30508ea03f31335cb50745e356ad82ea2cf98899c5a2d644285","block_sha256":"703d3acab2a4b30508ea03f31335cb50745e356ad82ea2cf98899c5a2d644285","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_1ed152f6-b7d7-41c5-819b-92a773997a75"},{"id":"occ_54d867115e2b558c15fabbc7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_24d4eeea-8464-41e1-927c-5205737f8e53","section_id":"sec_0651644f-44b6-4cf9-a986-c1beff6ab5ca","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":29,"end":31,"exact":"推論","quote":"SanDiskとSK hynixは、AIが学習中心から**推論中心**へシフトする中で、推論は継続稼働するため、**帯域、容量、電力制約がきつい**と説明しています。つまり、AIサービスが実運用され続けるほど、「HBMだけでは足りない」「でもSSDだけでは遅い」","quote_start":0,"quote_end":131,"text_sha256":"e062ce3435936652c4e9153d4bc95fa381bce046aedd1fe176bffe089c4a05e1","block_sha256":"e062ce3435936652c4e9153d4bc95fa381bce046aedd1fe176bffe089c4a05e1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_24d4eeea-8464-41e1-927c-5205737f8e53"},{"id":"occ_356c2319bb9fd06aff4d2a1f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_33f4f0d0-53cd-4f9e-a27e-33f3449187d5","section_id":"sec_41eddada-5da0-4e9b-9edf-67a20a7a1d9e","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"以下は、上記の“供給ラグ”と“推論主導の需要”を前提にしたシナリオ整理です（将来推定であり、確度はイベント依存。供給側イベントは事実ソース、価格・需給の帰結は推論として明示します）。","quote_start":0,"quote_end":91,"text_sha256":"907ee726739ae3e30de40c784d9f9be3c1967664c2ce683bf6c708241bbbda67","block_sha256":"907ee726739ae3e30de40c784d9f9be3c1967664c2ce683bf6c708241bbbda67","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_33f4f0d0-53cd-4f9e-a27e-33f3449187d5"},{"id":"occ_9dfbc1f3b9b25b4da92323f9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_3753aee4-6720-4f3b-baaf-c64100169cc3","section_id":"sec_0651644f-44b6-4cf9-a986-c1beff6ab5ca","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"### AI推論では速さだけでなく容量と電力も問題になる","quote_start":0,"quote_end":28,"text_sha256":"110030a5c36ba77865c92fe1a286f7038d90b624ac33342797af36c5f0f0b77a","block_sha256":"110030a5c36ba77865c92fe1a286f7038d90b624ac33342797af36c5f0f0b77a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_3753aee4-6720-4f3b-baaf-c64100169cc3"},{"id":"occ_f335167be305bfcc15bf2c88","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_3fd67ee1-1f7a-4af4-9091-539ba38103a7","section_id":"sec_56aa7c03-c319-4d00-aefa-a0dfef1c82fe","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":93,"end":95,"exact":"推論","quote":" 16ダイスタックで **512GB**\n- HBM4に近いフットプリント/電力/スタック高さを狙う\n- AI推論向けに設計","quote_start":38,"quote_end":100,"text_sha256":"31d39e077e9c26d7599ce0c6ed402f0c4ea810ff5f050e353cf9310fc68a179f","block_sha256":"31d39e077e9c26d7599ce0c6ed402f0c4ea810ff5f050e353cf9310fc68a179f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_3fd67ee1-1f7a-4af4-9091-539ba38103a7"},{"id":"occ_12541c66c3b6c0c89fdb5a7f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_5777c6e5-e3ab-42d8-83e7-fc692b262d00","section_id":"sec_6bfa0a5a-4823-46ad-ae76-3435f6ef0c4b","layer":"body","character_id":null,"count":4,"matched_aliases":["inference","推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"需要は、従来のPC/スマホ中心から、データセンター・AI（特に推論）へ重心が移っていることがサイクル形状を変えています。SanDiskはHBF標準化の発表で、AI産業が「学習（training）から推論（inference）へ」シフトし、推論は常時稼働でメモリ帯域・","quote_start":0,"quote_end":133,"text_sha256":"cf0f006f4d7dffce9645bea238f529d7851a7eb102e10d9aa4f6e466882858c6","block_sha256":"cf0f006f4d7dffce9645bea238f529d7851a7eb102e10d9aa4f6e466882858c6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_5777c6e5-e3ab-42d8-83e7-fc692b262d00"},{"id":"occ_49b61463312ae60cf0b5a70a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_57b97d96-f593-4e49-960f-6b20c4d74145","section_id":"sec_518dd53a-87e7-477e-ae4d-04bf71b86b07","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"商用化ステータスに関して、2025年8月の発表は「HBFメモリ初サンプルは2026年下半期」「HBF搭載のAI推論デバイスは2027年初旬のサンプル」を目標とし、SK hynixとの標準化協業（MOU）を示しました。2026年2月には、OCP配下でHBF標準化の専用ワークストリームを立ち上げると発表し、「HBMと","quote_start":0,"quote_end":157,"text_sha256":"d6794e664339771b966197212c6268e5d78e480e3607902c4484f5a8efe6df41","block_sha256":"d6794e664339771b966197212c6268e5d78e480e3607902c4484f5a8efe6df41","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_57b97d96-f593-4e49-960f-6b20c4d74145"},{"id":"occ_220897a6b251cdb1d11a378c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_8da33b22-7951-4331-8a73-09dec2e002d3","section_id":"sec_41eddada-5da0-4e9b-9edf-67a20a7a1d9e","layer":"code","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"```\ngantt\n  title NAND需給シナリオ（2026–2029：イベントは公開情報、帰結は推論）\n  dateFormat  YYYY-MM\n  axisFormat  %Y-%m\n\n  section ベース（最頻）\n  価格高止まり・供給タイト（LTA/企業SSD配分増） : active","quote_start":0,"quote_end":154,"text_sha256":"af639da7dbf5ae87d563f5cf190a1336697a9045235701c37cc29509a3441c06","block_sha256":"af639da7dbf5ae87d563f5cf190a1336697a9045235701c37cc29509a3441c06","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_8da33b22-7951-4331-8a73-09dec2e002d3"},{"id":"occ_bef6d5ba7a8dd3dcb3f02e87","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_92dde06b-37cf-497c-afa7-8e6e7d8be9ef","section_id":"sec_518dd53a-87e7-477e-ae4d-04bf71b86b07","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":144,"end":146,"exact":"推論","quote":"、配置最適化、OS/ランタイム）」が典型です。SanDisk自身も、HBMより高レイテンシ/大ページであっても推論で成立し得る、という趣旨を注記で述べています。","quote_start":89,"quote_end":169,"text_sha256":"80a57175a5db647ead490645f7b4e03ee3ae00374d44c35c93acc5b216558a4e","block_sha256":"80a57175a5db647ead490645f7b4e03ee3ae00374d44c35c93acc5b216558a4e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_92dde06b-37cf-497c-afa7-8e6e7d8be9ef"},{"id":"occ_1ac44ffba664c8d7482b644a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_9b214c52-b2f4-4942-b88b-04c52bad4aaa","section_id":"sec_1fd694df-d691-4e9b-bb28-c522fd673dba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":95,"end":97,"exact":"推論","quote":"なくHBFで埋める」方向に変わり得ます。SanDiskはHBFを“HBMとSSDの間の新階層”と明示しており、推論のメモリ帯域/容量不足を狙い撃ちしています。したがって、SanDiskの“中期オプション価値”は（1）NAND/SSDサイクルの上振れ、（2）HBFによる“新しいNAND需要カテゴリ創出”の二層になり","quote_start":40,"quote_end":197,"text_sha256":"4600ed6776dbfb34fa55f03a0e83591b57148e1576829dd6a5301789340c616f","block_sha256":"4600ed6776dbfb34fa55f03a0e83591b57148e1576829dd6a5301789340c616f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_9b214c52-b2f4-4942-b88b-04c52bad4aaa"},{"id":"occ_303d16267702e73c7d1d813b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_a09aac13-1491-4512-a50d-3603d0689fcd","section_id":"sec_518dd53a-87e7-477e-ae4d-04bf71b86b07","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":185,"end":187,"exact":"推論","quote":"学習済み重み読み出し」を想定したシミュレーションで“容量無制限のHBM”に対して2.2%差に収まる、と説明し、推論のメモリ・ウォール対策として位置づけています。","quote_start":130,"quote_end":210,"text_sha256":"47a3c4e12c6d1880b891b9dd19e670619a8b6c7da22c6fb70fba461ddd46c89a","block_sha256":"47a3c4e12c6d1880b891b9dd19e670619a8b6c7da22c6fb70fba461ddd46c89a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_a09aac13-1491-4512-a50d-3603d0689fcd"},{"id":"occ_16a0669e271487a9960ce0a4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_b372bea6-49f4-4d7c-912f-b7bc6e5f9c64","section_id":"sec_916815c8-dcf0-4c67-8ab4-fb95117b6700","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":117,"end":119,"exact":"推論","quote":"th Flash）\\*\\*です。  \nHBFが本当に立ち上がれば、SanDiskは「NAND市況株」から「AI推論時代の新しいメモリ階層を提案する会社」へ評価軸が変わる可能性があります。反対にHBFが空振りすると、結局はNAND市況株として見られやすいです。 ([Sandisk](https://www.sand","quote_start":62,"quote_end":219,"text_sha256":"eaf4d0a1df0774fdf29d49f7fb505ee69b675c9b0d3cd8b47b8dc9f7c33bd7e5","block_sha256":"eaf4d0a1df0774fdf29d49f7fb505ee69b675c9b0d3cd8b47b8dc9f7c33bd7e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_b372bea6-49f4-4d7c-912f-b7bc6e5f9c64"},{"id":"occ_dc5826aab844bc69a2b5a58c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_c2fead22-ede9-4f14-a1f1-cfbaacd282e4","section_id":"sec_1496461b-63cf-4314-b908-47bc11377953","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":109,"end":111,"exact":"推論","quote":"用を進める段階です。SanDiskは、**2026年後半に最初のHBFサンプル、2027年初頭にHBF搭載AI推論デバイスのサンプル**を目標にしています。 ([Sandisk](https://www.sandisk.com/in-id/company/newsroom/press-releases/2025/","quote_start":54,"quote_end":211,"text_sha256":"0dc61aa75d5a17ffb1c2e2ae41a72575cd93e45030c073040002419c2250bf22","block_sha256":"0dc61aa75d5a17ffb1c2e2ae41a72575cd93e45030c073040002419c2250bf22","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_c2fead22-ede9-4f14-a1f1-cfbaacd282e4"},{"id":"occ_943cf12bdc2079dbde59549a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_c528e802-a8b4-4578-bbe7-633a12c52e43","section_id":"sec_518dd53a-87e7-477e-ae4d-04bf71b86b07","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":113,"end":115,"exact":"推論","quote":"を含む点を強調しています。 これは、DRAM/HBMが本質的に揮発・リフレッシュを要することへの対置として、“推論の常時稼働”で効くTCO要素（電力・冷却・容量当たりコスト）を取りに行く設計思想です。","quote_start":58,"quote_end":158,"text_sha256":"666cbd71ffc36534a86d67c260a7596a2ac4b136ed962c4f4b7c86f31bb352d2","block_sha256":"666cbd71ffc36534a86d67c260a7596a2ac4b136ed962c4f4b7c86f31bb352d2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_c528e802-a8b4-4578-bbe7-633a12c52e43"},{"id":"occ_0ed983edb156f95872c7870c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_da53654b-529b-409c-8179-aca4647375fa","section_id":"sec_1fd694df-d691-4e9b-bb28-c522fd673dba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":"I/データセンターのワークロード観点でのSSDの役割は、大きく「永続ストレージ」「キャッシュ/階層化」「学習/推論の周辺データ（ログ、スナップショット、チェックポイント、ベクトルDB等）」です。Kioxiaは、ベクトル検索をSSD上で行いDRAM依存を下げるAiSAQを発表しており、これは“DRAMが高価/不足に","quote_start":1,"quote_end":158,"text_sha256":"1dce9ac69c56e7be1a63329e89097fb2f24f1f70f00278929e52e7a3766a4269","block_sha256":"1dce9ac69c56e7be1a63329e89097fb2f24f1f70f00278929e52e7a3766a4269","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_da53654b-529b-409c-8179-aca4647375fa"},{"id":"occ_9d8b3bd89e3f756aebaecb4a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_f4f1b476-409c-4c53-8325-80b717e8a028","section_id":"sec_6bfa0a5a-4823-46ad-ae76-3435f6ef0c4b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":102,"end":104,"exact":"推論","quote":"能TLC SSD販売拡大に言及しています。citeturn23view0 Kioxiaの統合報告書でも、推論システムがユーザー/サービス数の増加で拡大し、SSDがログやスナップショット等に使われる図示があり、AI時代のSSD需要増を示唆します。","quote_start":47,"quote_end":172,"text_sha256":"f1e6861f1a79cec8098371c667d8882f203a28bcbf7a37b2d2e67c916a699593","block_sha256":"f1e6861f1a79cec8098371c667d8882f203a28bcbf7a37b2d2e67c916a699593","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_f4f1b476-409c-4c53-8325-80b717e8a028"},{"id":"occ_c362a1b6eda43d6a69629cd9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_f5f0d628-e6c3-4036-a09b-576c2f019095","section_id":"sec_3e3eaca0-67fc-49c7-b446-0e7a5fef6d13","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":78,"end":80,"exact":"推論","quote":"サーバ向けへ投資・キャパ振替→一般NAND/Client向け供給が相対的に絞られる」圧力があり、需要側で「AI推論の常時稼働化」と「エンタープライズSSDへの容量配分増」が同時進行している、という構図が主要シナリオです。これは「消費者向け中心の時代に効いた“供給増→価格下落→需要刺激”」よりも、需給がロングターム","quote_start":23,"quote_end":180,"text_sha256":"0d3b35dac0509338cd122a70defdb1ce8538519df79cbedec028d4975ecfa70c","block_sha256":"0d3b35dac0509338cd122a70defdb1ce8538519df79cbedec028d4975ecfa70c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2/#blk_f5f0d628-e6c3-4036-a09b-576c2f019095"},{"id":"occ_7e0a397f2a26d70c5a2e6752","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_7d6d4b5c-60bd-44d9-a7c5-ea8b8feb6dd2","work_id":"wrk_903d8c2f-b74b-40ab-ae34-1de2fa31df84","block_id":"blk_f6740ce4-e319-468d-ace6-e5015cda1605","section_id":"sec_dab1d38e-cc85-4319-98bc-0c1d3c32ff71","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"- OCPで標準化が進む\n- GPU/AIアクセラレータ周辺の新しいメモリ階層として認知される\n- AI推論のメモリ不足を埋める現実解になる\n- 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SSD、チェックポイント保存、大容量データレイクを提案できる。","quote_start":25,"quote_end":135,"text_sha256":"8c1bdfaa69c77659004adef3270f94c2ac18c81bc70af1a7397367122303616d","block_sha256":"8c1bdfaa69c77659004adef3270f94c2ac18c81bc70af1a7397367122303616d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_89eb6a8e-70dd-4f87-9da4-213e9ac3f499/#blk_5af99c6a-93de-4228-ab71-3866206c9d36"},{"id":"occ_a6a68dc3ee3d4a33a6091d96","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_89eb6a8e-70dd-4f87-9da4-213e9ac3f499","work_id":"wrk_27247b5b-cd30-433f-9e72-ddf9cc560a98","block_id":"blk_86ba70ef-51dc-4ff2-9e32-dc5396d833a9","section_id":"sec_de5652bb-53e5-4fa8-ab4a-b917ff941cda","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":153,"end":160,"exact":"KVキャッシュ","quote":"ー traffic を削減 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Content**になり得るとしており、FY28からRevenue開始を予定している。([MarketBeat](https://www.","quote_start":0,"quote_end":136,"text_sha256":"cf9f95d4fdcadbf60de18bbd8be7cd33a5284be7565264779ad7bb7de5916be3","block_sha256":"cf9f95d4fdcadbf60de18bbd8be7cd33a5284be7565264779ad7bb7de5916be3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_8e69c4b3-4a90-4a4f-82f9-c2a31d24c009/#blk_ce10ffbe-fe0e-46b1-8c98-367b86939973"},{"id":"occ_e921dbdd53ac0d815d095a6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_8eb03ce9-cad9-4ba7-91d6-90dffd2302fa","work_id":"wrk_82866dd9-73ac-495c-82f9-5873005352f3","block_id":"blk_0c58d023-d49f-47b9-b4ce-919b79ac890c","section_id":"sec_d4fd9d0a-c0a7-4213-87f5-be1fe0dddceb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":" プロンプト\n- モデルの回答\n- トークン使用量\n- ツール呼び出し\n- RAG検索履歴\n- エージェントの推論経路\n- APIの応答時間\n- セキュリティイベント\n- AIが実行した業務操作\n- 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使用したデータ\n- 推論費用\n- 操作履歴\n- 取り消しボタン","quote_start":13,"quote_end":89,"text_sha256":"c093ffd123fd520c03ee43fa4b4793ea25cd9db76e91875876883015d6365d16","block_sha256":"c093ffd123fd520c03ee43fa4b4793ea25cd9db76e91875876883015d6365d16","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_8f8ffd4e-00d7-47a6-9906-ce0fa86b3835/#blk_e262c385-82eb-4a7d-86a1-69cd14de9938"},{"id":"occ_4ee773ee19ced6f61e89229e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_8f8ffd4e-00d7-47a6-9906-ce0fa86b3835","work_id":"wrk_eaab3954-2ccc-46a2-902f-fbbd679471b9","block_id":"blk_ff42ef92-e46f-419d-9009-8aacc0161e3c","section_id":"sec_f0e23c26-9a59-4722-97b5-a71cb6ea1e80","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":11,"end":18,"exact":"KVキャッシュ","quote":"これは重みだけの値で、KVキャッシュ、実行時バッファ、通信、量子化スケール、システム予約領域を含まない。","quote_start":0,"quote_end":52,"text_sha256":"81c445fc236d109aba3a1e275a8258aee3d94df0ee3c55dbf18e2f7a756f2422","block_sha256":"81c445fc236d109aba3a1e275a8258aee3d94df0ee3c55dbf18e2f7a756f2422","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_8f8ffd4e-00d7-47a6-9906-ce0fa86b3835/#blk_ff42ef92-e46f-419d-9009-8aacc0161e3c"},{"id":"occ_af872a73eb144ded07a06987","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_02205559-8cdd-4be8-945e-c122cf043298","section_id":"sec_c693fbbb-c491-42ce-958a-6d5597b75aab","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"NVIDIAの強さは、GPU単体ではなく、CUDA、ネットワーク、ライブラリ、推論基盤、ロボティクス、シミュレーションまで含むスタックにあります。AIエージェントが増えるほど、推論需要が増えます。Physical AIが広がるほど、AIはテキストや画像の外へ出て、機械や工場や車へ入","quote_start":0,"quote_end":141,"text_sha256":"970225aa56270626e4b6535ab6e09b53fec10ab993cf96e2d3ca0b20e83c323a","block_sha256":"970225aa56270626e4b6535ab6e09b53fec10ab993cf96e2d3ca0b20e83c323a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_02205559-8cdd-4be8-945e-c122cf043298"},{"id":"occ_4636b31089670b725fb933bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_137e7917-0972-4ab9-b3ca-d2fffad59145","section_id":"sec_7c6d1cd5-0b7e-4e1a-8c5b-01eedd37b53a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":169,"end":171,"exact":"推論","quote":"mos 3は、AIが物理世界を理解するための世界モデルである。\nAlpamayo 2とHyperionは、車を推論するロボットへ変える基盤である。","quote_start":114,"quote_end":187,"text_sha256":"edc24f2b19e94ca1ce24b13c70db1dc4214a555e32dab5d8926aff0d83aa8134","block_sha256":"edc24f2b19e94ca1ce24b13c70db1dc4214a555e32dab5d8926aff0d83aa8134","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_137e7917-0972-4ab9-b3ca-d2fffad59145"},{"id":"occ_71200d0909d92b5e41cb0d1d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_1a606cca-ee40-4f1d-99e8-b1cb9d497f2c","section_id":"sec_7287b396-74f6-4e93-8cc0-4783281166ce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"Alpamayo 2は、自動運転車にその推論層を与えようとする。高レベルの運転判断、つまり「譲る」「車線変更する」「止まる」「合流する」といったMeta-Actionを出し、その理由もChain-of-Causationとして残す。","quote_start":0,"quote_end":116,"text_sha256":"fdcda8c679f9f94a744b7eef91ed5d3e7bb748982059ca0adc796168e1b4969a","block_sha256":"fdcda8c679f9f94a744b7eef91ed5d3e7bb748982059ca0adc796168e1b4969a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_1a606cca-ee40-4f1d-99e8-b1cb9d497f2c"},{"id":"occ_dcfdac6facbd05415446ef26","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_36bc9179-e028-4c7d-b0fd-585a3728c5de","section_id":"sec_7287b396-74f6-4e93-8cc0-4783281166ce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"Alpamayo 2 Superは、自動運転車向けの推論型VLAモデルである。VLAとは、Vision-Language-Actionの略で、見る、意味を理解する、行動するという流れを扱う。","quote_start":0,"quote_end":95,"text_sha256":"8f419cd334fecbaba7606a8f31956422a3f987f23aee481800c0cc00cfd915a4","block_sha256":"8f419cd334fecbaba7606a8f31956422a3f987f23aee481800c0cc00cfd915a4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_36bc9179-e028-4c7d-b0fd-585a3728c5de"},{"id":"occ_2dca39996beb3481b94e8350","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_52f0d3ef-e061-4892-8519-b9191ab5b289","section_id":"sec_6552ff8f-11c0-4b72-98d5-c62a521c1751","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":78,"end":80,"exact":"推論","quote":"られている。\n- AI工場は、Tokenを大量生産するデータセンターとして定義できる。\n- AIエージェントは推論需要を増やし、AI工場の稼働を押し上げる。\n- Physical AIは、ロボット、自動運転、工場など現実世界へAIを広げる。","quote_start":23,"quote_end":143,"text_sha256":"651202719ce2bb30514ec4bf5cd0dca88bf7a77d0596f13beb7be31dcc26c2b2","block_sha256":"651202719ce2bb30514ec4bf5cd0dca88bf7a77d0596f13beb7be31dcc26c2b2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_52f0d3ef-e061-4892-8519-b9191ab5b289"},{"id":"occ_ccd0e5e7a9a57641fd7fb99e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_56114c3c-156b-4789-a124-c5498cf84565","section_id":"sec_becbcb0a-d8f3-441f-8b27-2ffd3029f801","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"- AIエージェントの実利用が推論需要をどれだけ押し上げるか。\n- NVIDIAのソフトウェア基盤がハードウェア販売以外の堀として強まるか。\n- AI PCやエッジ推論がクラウド需要を補完するか、分散させるか。\n- Physical ","quote_start":0,"quote_end":117,"text_sha256":"4e3b98481359b79eedc33ec05b5f945d42c606ef9ab887c34307fdb7247b4e17","block_sha256":"4e3b98481359b79eedc33ec05b5f945d42c606ef9ab887c34307fdb7247b4e17","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_56114c3c-156b-4789-a124-c5498cf84565"},{"id":"occ_6edb4e3debd6d05fddbd9adc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_56a5a8e6-aab9-439e-a519-65dc8c86f65e","section_id":"sec_7287b396-74f6-4e93-8cc0-4783281166ce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":74,"end":76,"exact":"推論","quote":"判断をしたのか」が分からないブラックボックスでは、規制や社会受容の壁を越えにくい。Alpamayo 2のような推論型モデルは、判断の因果関係を示しやすくすることで、自動運転の検証・改善・規制対応を進めやすくする。","quote_start":19,"quote_end":125,"text_sha256":"466a7ee7a95aed1d0f58b0cf2ca6fa717f67f86ad6c4404219b091aa0b3640ea","block_sha256":"466a7ee7a95aed1d0f58b0cf2ca6fa717f67f86ad6c4404219b091aa0b3640ea","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_56a5a8e6-aab9-439e-a519-65dc8c86f65e"},{"id":"occ_9b4ac574407414c0d1cd10d0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_7166f9c0-dab4-4dd4-9390-8f29fa06aab0","section_id":"sec_84153983-b503-4ece-8633-e76f4763a9b1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":75,"end":77,"exact":"推論","quote":"の動画生成、予測、合成データ生成、プロンプト追従、制御性が中心だった。Cosmos 3では、それがさらに進み、推論、世界生成、行動生成が統合される。","quote_start":20,"quote_end":94,"text_sha256":"2ec989e176a7e2c8913fe360df3384f49c0860af0feb0cff55d44cbba53c7174","block_sha256":"2ec989e176a7e2c8913fe360df3384f49c0860af0feb0cff55d44cbba53c7174","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_7166f9c0-dab4-4dd4-9390-8f29fa06aab0"},{"id":"occ_9313a0e8f0866f4a8ef5e571","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_7c204d54-477e-43c6-a0ae-06d6fb5dbb1a","section_id":"sec_7e11748a-828d-4119-8a47-9e26fa680e55","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"提示する。もちろんこれは、需要が十分にあり、稼働率が高く、電力と冷却を確保できるという前提がある。しかし、AI推論が本当に収益性を持つなら、AI工場は従来のコストセンターではなく、直接売上を生む生産設備になる。","quote_start":27,"quote_end":132,"text_sha256":"f2b4c2dc94fdd335565e58169e275629aae1b3756f5f359743a7ffe94c42ab93","block_sha256":"f2b4c2dc94fdd335565e58169e275629aae1b3756f5f359743a7ffe94c42ab93","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_7c204d54-477e-43c6-a0ae-06d6fb5dbb1a"},{"id":"occ_8e3b612f6d6728e20d05bcad","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_7f25a743-cff3-4c2e-b910-dfc125a37026","section_id":"sec_7287b396-74f6-4e93-8cc0-4783281166ce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"こうした判断には、状況の意味理解と推論が必要になる。","quote_start":0,"quote_end":26,"text_sha256":"5693c24a9a4be7c99e87a34b39326c23a6c5644644ec651a34fc4d30cbeca7ed","block_sha256":"5693c24a9a4be7c99e87a34b39326c23a6c5644644ec651a34fc4d30cbeca7ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_7f25a743-cff3-4c2e-b910-dfc125a37026"},{"id":"occ_52d49570e68e7ddd3d175b80","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_9a68f8cd-6408-4ca8-88db-1e7382b420d9","section_id":"sec_b5e793a3-ea46-4776-8389-6321b9d81b0d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":42,"end":44,"exact":"推論","quote":"AIエージェントは、単にLLMを一回呼び出して終わりではない。一つの指示から、検索、推論、ツール使用、コード実行、データベース参照、メモリ管理、複数モデル呼び出し、セキュリティ確認、結果生成まで、何百、何千ステップの処理を行う可能性がある。","quote_start":0,"quote_end":120,"text_sha256":"341ed0fd16db0e149572be03a68cbdf59e9b4f99d66db7b6feca684e12632312","block_sha256":"341ed0fd16db0e149572be03a68cbdf59e9b4f99d66db7b6feca684e12632312","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_9a68f8cd-6408-4ca8-88db-1e7382b420d9"},{"id":"occ_09da9f2a4c70c53e72c8040b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_c0d73d66-8d73-4608-9a0d-f5f160c78b5b","section_id":"sec_b5e793a3-ea46-4776-8389-6321b9d81b0d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":53,"end":55,"exact":"推論","quote":"Hopper時代は、AIモデルを訓練するGPUの時代だった。\nGrace Blackwell時代は、大規模推論を効率よく回すラックの時代だった。\nVera Rubin時代は、AIエージェントを動かすAI工場全体の時代である。","quote_start":0,"quote_end":112,"text_sha256":"d990651ee82e14bc6781f2d292afe481d2b2e067cd00d8a94d56a03406f19ee8","block_sha256":"d990651ee82e14bc6781f2d292afe481d2b2e067cd00d8a94d56a03406f19ee8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_c0d73d66-8d73-4608-9a0d-f5f160c78b5b"},{"id":"occ_0613a5be849d5a0559560284","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_90fb7b96-f10b-43df-a612-0c23e8a50740","work_id":"wrk_0c4c3acf-0221-4d96-87a4-771da7ba1e3a","block_id":"blk_c922bb85-69cb-447e-9cc2-504ff9b9ed13","section_id":"sec_7287b396-74f6-4e93-8cc0-4783281166ce","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"## 13. Alpamayo 2：自動運転車は「推論するロボット」になる","quote_start":0,"quote_end":37,"text_sha256":"4b6cc0537c0ad19b7764a828354d8c19e4f37b2ee9d796a2b8ecfcd289ed795d","block_sha256":"4b6cc0537c0ad19b7764a828354d8c19e4f37b2ee9d796a2b8ecfcd289ed795d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_90fb7b96-f10b-43df-a612-0c23e8a50740/#blk_c922bb85-69cb-447e-9cc2-504ff9b9ed13"},{"id":"occ_434f03655541bf1c1ad819c3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a","work_id":"wrk_c22379bc-7081-4f8f-a3e9-4418f450cf96","block_id":"blk_24de29cc-38b5-4b81-9a73-53c452255f09","section_id":"sec_14e47507-b1bb-4bef-bee5-e29ea1e25711","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":330,"end":332,"exact":"推論","quote":"ータセンター\n│\n└─ TeraFab / 半導体供給構想\n   ├─ AIチップ\n   ├─ Tesla向け推論チップ\n   ├─ SpaceX/xAI向けAIデータセンターチップ\n   └─ 将来の軌道上compute向け半導体\n```","quote_start":275,"quote_end":395,"text_sha256":"0b4705500d2f9c12f8f093009c11b59f1fb4545e0c30500c1680243538a115e8","block_sha256":"0b4705500d2f9c12f8f093009c11b59f1fb4545e0c30500c1680243538a115e8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a/#blk_24de29cc-38b5-4b81-9a73-53c452255f09"},{"id":"occ_164e5c50be175f5fec5d040d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a","work_id":"wrk_c22379bc-7081-4f8f-a3e9-4418f450cf96","block_id":"blk_25ee18c4-ae46-47f2-bf2b-c56ebff95391","section_id":"sec_e510b27b-ac3b-4bb7-a5e3-56729ac980b9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":37,"end":39,"exact":"推論","quote":"Tesla AIが「車やロボットを動かすAI」だとすれば、xAIは「言語、推論、検索、会話、生成、エージェント」を担うAIです。","quote_start":0,"quote_end":64,"text_sha256":"1b42eb47c194d4bb9edf32788a2c2875faffb4f971fbfd6d1772c0a7a804de55","block_sha256":"1b42eb47c194d4bb9edf32788a2c2875faffb4f971fbfd6d1772c0a7a804de55","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a/#blk_25ee18c4-ae46-47f2-bf2b-c56ebff95391"},{"id":"occ_477beb4b4d8d4927dd5bc07d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a","work_id":"wrk_c22379bc-7081-4f8f-a3e9-4418f450cf96","block_id":"blk_5207fdf9-205d-4a24-b9a3-56e9029e2b49","section_id":"sec_5c7091bf-9251-425f-87d5-662a2f8bf43c","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"```\nAIエージェント需要が爆発\n↓\n推論トークン需要が急増\n↓\nAIデータセンター不足が深刻化\n↓\nSpaceX / xAIがcomputeを外販\n↓\nGrok、X、API、AIエージェント収益が伸びる\n↓\nStarlinkと宇宙compu","quote_start":0,"quote_end":122,"text_sha256":"1053c37ff2e58c75d82abd5f79fb56156615c0d9bc08a9990f28ec4c7ee4095d","block_sha256":"1053c37ff2e58c75d82abd5f79fb56156615c0d9bc08a9990f28ec4c7ee4095d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a/#blk_5207fdf9-205d-4a24-b9a3-56e9029e2b49"},{"id":"occ_c629079d376ad5da05166ad3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a","work_id":"wrk_c22379bc-7081-4f8f-a3e9-4418f450cf96","block_id":"blk_c951f451-fe76-4fc1-b69f-790d4fc752e4","section_id":"sec_5c7091bf-9251-425f-87d5-662a2f8bf43c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"- チップ\n- 電力\n- データセンター\n- 通信\n- 推論基盤\n- エージェント\n- ユーザー接点\n- 物理世界への実装","quote_start":0,"quote_end":61,"text_sha256":"7dc6d2c4b73bd3ffafca786dd481bc2b4057f21a00ba26eadd39374b6b00e88c","block_sha256":"7dc6d2c4b73bd3ffafca786dd481bc2b4057f21a00ba26eadd39374b6b00e88c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a/#blk_c951f451-fe76-4fc1-b69f-790d4fc752e4"},{"id":"occ_916c97389171598901f8a6d2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a","work_id":"wrk_c22379bc-7081-4f8f-a3e9-4418f450cf96","block_id":"blk_d46e1c03-30ff-4b57-89a2-7e741013ceab","section_id":"sec_fcae4a11-267f-4617-8297-32eb74ebfb76","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":"``\nTesla\n→ FSD、Optimus、Robotaxiにチップが必要\n\nxAI\n→ Grok、LLM、推論、エージェントにチップが必要\n\nSpaceX\n→ Starlink、宇宙AI、AI compute外販にチップが必要\n\nTeraFab\n→ それら全てにチップを供給する\n```","quote_start":1,"quote_end":146,"text_sha256":"e209144fde045b2c2371e0abc9d5b0d0fb5dada2949f286723e2d0d6c4a64192","block_sha256":"e209144fde045b2c2371e0abc9d5b0d0fb5dada2949f286723e2d0d6c4a64192","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_91468364-fe9d-4ab5-a6dc-f203e89bc64a/#blk_d46e1c03-30ff-4b57-89a2-7e741013ceab"},{"id":"occ_1f6fd9e72e10cf6ef12e3c31","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_2b4e97fb-eeab-45bd-af38-f1a4fbc813fd","section_id":"sec_a3d4c8b6-f108-4f44-8fbc-20b35cfb7609","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":73,"end":75,"exact":"推論","quote":"Iの価格支配力を削り、開発者エコシステムを獲得し、主権AIを求める国々に代替基盤を提供し、中国発のモデル形式、推論基盤、ツールチェーン、評価文化を世界に広げる戦略である。米国のGPU輸出規制によって中国はハードウェア面で制約を受けてきたが、その制約が逆に、より効率的で、より安く、よりローカル適応しやすいオープンモ","quote_start":18,"quote_end":175,"text_sha256":"57db528e278ea70f58800987158a910c5df50e37178ebd6da220d1ff5e3c74f2","block_sha256":"57db528e278ea70f58800987158a910c5df50e37178ebd6da220d1ff5e3c74f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_2b4e97fb-eeab-45bd-af38-f1a4fbc813fd"},{"id":"occ_d04605fe4cd7107595afb465","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_35ff260f-2935-4489-87ab-8d760c7e0180","section_id":"sec_fae905aa-c4ba-45e5-b367-aea16669ecf3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":135,"end":137,"exact":"推論","quote":"niBand/Spectrum-Xなどの完成度が高いからです。\nただし、オープンウェイトが普及すると、各国は「推論専用の安い国内ASIC」や「政府クラウド用の低消費電力アクセラレータ」を欲しがるようになります。","quote_start":80,"quote_end":185,"text_sha256":"71e50006729647051fa8e2c48e9260a1f55d963edfcaa2f3193e044437756489","block_sha256":"71e50006729647051fa8e2c48e9260a1f55d963edfcaa2f3193e044437756489","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_35ff260f-2935-4489-87ab-8d760c7e0180"},{"id":"occ_ec11dbbecf23a014b5db64c1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_41451dea-1532-4b15-adff-b2769b8980ba","section_id":"sec_a3d4c8b6-f108-4f44-8fbc-20b35cfb7609","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":43,"end":45,"exact":"推論","quote":"OpenAI、Anthropic、Googleの最上位モデルは、依然として最先端の科学推論、長距離エージェント、複雑な安全性設計、企業向け信頼性で優位を持つ。しかし、それらのモデルは基本的にクローズドであり、利用者はAPIやクラウドサービスを通じてアクセスする。価格変更、利用制限、規約変更","quote_start":0,"quote_end":145,"text_sha256":"906c145aa14649a7f2df1e9260e56dda4d439db2c1c0803d7c36e44efef9881b","block_sha256":"906c145aa14649a7f2df1e9260e56dda4d439db2c1c0803d7c36e44efef9881b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_41451dea-1532-4b15-adff-b2769b8980ba"},{"id":"occ_6178136bd7f1c7247b4d8ec0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_7a654ea4-480c-4851-9c32-9fa42087d2b4","section_id":"sec_c987a51d-3e84-45cc-abc3-208b48105ea8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":339,"end":341,"exact":"推論","quote":"  |\n| AI ASIC      |  **+5〜10%** | **+15〜25%** | GPUの後に推論特化で伸びる           |\n| HBM          |  **+5〜10%** | **+10〜20%** | 中国込みで効く。非中国のみなら+2〜5%     |\n| データセンター","quote_start":284,"quote_end":441,"text_sha256":"c7942deaeb87c6b73c837f8e385b9af2387c8538f8f7e78e4c37b36c31a7f3f9","block_sha256":"c7942deaeb87c6b73c837f8e385b9af2387c8538f8f7e78e4c37b36c31a7f3f9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_7a654ea4-480c-4851-9c32-9fa42087d2b4"},{"id":"occ_584e675bbb66571899476d23","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_8435e9e6-b75d-41b4-bdce-2cbe4b6a84f9","section_id":"sec_39893bbf-0dd3-4a2f-9695-4497069d4b11","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":30,"end":32,"exact":"推論","quote":"つまり、オープンウェイトはモデル価格を下げる一方で、世界中の推論インフラ需要を広げる。モデルの価値が安くなるほど、モデルを動かす物理基盤の価値が上がる。この逆説が、GLM-5.2の本当のインフラ的意味である。","quote_start":0,"quote_end":104,"text_sha256":"57d7b6e980530cc37761ea14fd19e399f7a8d76c56cd154fcbe1504593273a6d","block_sha256":"57d7b6e980530cc37761ea14fd19e399f7a8d76c56cd154fcbe1504593273a6d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_8435e9e6-b75d-41b4-bdce-2cbe4b6a84f9"},{"id":"occ_10b739cb3ff36979f64a3d33","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_c2edd732-a7b2-4b87-a7fc-f1924038bd44","section_id":"sec_a3d4c8b6-f108-4f44-8fbc-20b35cfb7609","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"モデル単体の性能は、少しずつコモディティ化していく。差別化の中心は、推論インフラ、データ、エージェント実行環境、セキュリティ、法制度、電力、冷却、ネットワーク、産業実装へ移る。","quote_start":0,"quote_end":88,"text_sha256":"8735107971bf08db33a80c091f4e10e5b827ae0792af0cf28889f4d4aeb4bd94","block_sha256":"8735107971bf08db33a80c091f4e10e5b827ae0792af0cf28889f4d4aeb4bd94","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_c2edd732-a7b2-4b87-a7fc-f1924038bd44"},{"id":"occ_83e04e7521e9482f5902caa1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_ee4e1b95-d1ae-4d17-a420-ac3ed562b9bb","section_id":"sec_56dd1691-fff8-4093-a7e6-52ce4f4ec3ee","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":534,"end":536,"exact":"推論","quote":"              |\n| 運用      | CUDA/ROCm、Kubernetes/Slurm、推論基盤、セキュリティ人材 |","quote_start":479,"quote_end":549,"text_sha256":"ca063e62e3df4075c7d8df2670646cf5b107cb4e87b77be7af1bf47eb1b70c4b","block_sha256":"ca063e62e3df4075c7d8df2670646cf5b107cb4e87b77be7af1bf47eb1b70c4b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_ee4e1b95-d1ae-4d17-a420-ac3ed562b9bb"},{"id":"occ_b0a0d64c480419bbe864b9b6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_f18ad490-6ab5-4fce-b891-d84902517f7e","section_id":"sec_71900921-e7e0-416a-bacf-5fd88dc2a5c9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"特に重要なのは、これが「フロンティアモデル訓練」だけではなく、**推論・RAG・エージェント・行政AI・教育AI・防衛AI・医療AI**の常時稼働需要として出てくる点です。","quote_start":0,"quote_end":86,"text_sha256":"2e73c478c9cdef04a41f11f019684b12c422cdd92eb0157f51cf22120ab72f64","block_sha256":"2e73c478c9cdef04a41f11f019684b12c422cdd92eb0157f51cf22120ab72f64","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_f18ad490-6ab5-4fce-b891-d84902517f7e"},{"id":"occ_69186eb0f7fde958f9e61a2c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_92ff5158-6c34-4526-ab02-c8db8332951f","work_id":"wrk_ea89805e-b45e-4a9c-b22a-29b168da02ed","block_id":"blk_f4b7e96b-d670-4171-be9d-c1612e6fc5d4","section_id":"sec_39893bbf-0dd3-4a2f-9695-4497069d4b11","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":79,"end":81,"exact":"推論","quote":"ウェイトが広がるほど、国内クラウドや政府クラウドで動かしたい需要が増える。主権AIの論点は、モデルの所有から、推論をどこで、どの電力で、どのデータと結び付けて回すかへ移る。","quote_start":24,"quote_end":110,"text_sha256":"33ac2b21f60f86bad4a769518d5ee446a0555b49b2482568d42e707ba9c0e330","block_sha256":"33ac2b21f60f86bad4a769518d5ee446a0555b49b2482568d42e707ba9c0e330","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_92ff5158-6c34-4526-ab02-c8db8332951f/#blk_f4b7e96b-d670-4171-be9d-c1612e6fc5d4"},{"id":"occ_1dc38ed55e760deb609975b2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8","work_id":"wrk_8f40984a-d96e-441d-8ace-83f20958435a","block_id":"blk_114dc1c7-17c1-44e9-bb91-302d86b8be1f","section_id":"sec_195f2186-f474-4494-b6a9-3632a094a037","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"学習できるのも、推論基盤を作れるのも、世界中にクラウドとして配れるのも、政府や大企業に安全な環境を提供できるのも、結局はMicrosoft、Google、Amazon、Meta、Oracleのような巨大企業である。","quote_start":0,"quote_end":107,"text_sha256":"d3c107024366a74ca2861d921093936cc39ab6a1c0c1b9fa0e4986a9e54725a1","block_sha256":"d3c107024366a74ca2861d921093936cc39ab6a1c0c1b9fa0e4986a9e54725a1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8/#blk_114dc1c7-17c1-44e9-bb91-302d86b8be1f"},{"id":"occ_1c865f626d3b82b18b1112b5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8","work_id":"wrk_8f40984a-d96e-441d-8ace-83f20958435a","block_id":"blk_1bd6c4b8-91c8-4c96-8ed1-5d3c5b44625b","section_id":"sec_669cbc9d-de76-40c4-807d-0e1d83982942","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"一方で、高度な推論、大規模検索、企業DB接続、長期記憶、重いエージェント実行、マルチモーダル処理、専門モデルはクラウドに上がる。","quote_start":0,"quote_end":64,"text_sha256":"e6bf3caea8a611f6cf751d60a1ec135441d9a121562ab37576783d4b1454bd2d","block_sha256":"e6bf3caea8a611f6cf751d60a1ec135441d9a121562ab37576783d4b1454bd2d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8/#blk_1bd6c4b8-91c8-4c96-8ed1-5d3c5b44625b"},{"id":"occ_bc1943e64274c23fef8fb2a5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8","work_id":"wrk_8f40984a-d96e-441d-8ace-83f20958435a","block_id":"blk_25e450fe-7c79-4224-82dc-a8e8deee3f76","section_id":"sec_22c99273-2a63-411c-8482-7f086a5881cd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":136,"end":138,"exact":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 \nAPI使用量も減る。  \nモデル企業の売上も削られる。","quote_start":0,"quote_end":54,"text_sha256":"e110fed5bb61d032799915464a1e2320105792b80afd619a8d6fde1b1f279e3b","block_sha256":"e110fed5bb61d032799915464a1e2320105792b80afd619a8d6fde1b1f279e3b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9369ec68-2723-4ee0-81f8-c835d3d59ac8/#blk_fa727902-e89f-4242-b38e-0cd5b0cebc62"},{"id":"occ_7e4fc3890d1f24145b43fa6a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_190b7312-808d-4de7-b5bf-c37a334fc474","section_id":"sec_57d784db-6048-4eb2-93fb-3bf936a4cd4e","layer":"code","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":34,"end":41,"exact":"KVキャッシュ","quote":"```\nRubin GPUラック\nVera Rubin NVL72\nKVキャッシュ管理\nagentic AI\n強化学習環境\nGPU稼働率最大化\np99レイテンシ管理\nNVIDIA AI Enterprise / CUDA / NIM環境\n```","quote_start":0,"quote_end":122,"text_sha256":"2cb91d1b3ded27204ad215ca2f8b98e5a1283a6c0165ed104aa9e4b9165daa8f","block_sha256":"2cb91d1b3ded27204ad215ca2f8b98e5a1283a6c0165ed104aa9e4b9165daa8f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_190b7312-808d-4de7-b5bf-c37a334fc474"},{"id":"occ_c7d0fb053aa26798037cb873","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_1bda8599-462c-486c-a154-878575ec19ed","section_id":"sec_5480626e-0780-45c7-ac94-615bcade6ae1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"## 低消費電力AI推論からデータセンターへ","quote_start":0,"quote_end":22,"text_sha256":"2e382e9550871d2665b62e10dbaf766e626ae98ada5608756a214872d6dc16c2","block_sha256":"2e382e9550871d2665b62e10dbaf766e626ae98ada5608756a214872d6dc16c2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_1bda8599-462c-486c-a154-878575ec19ed"},{"id":"occ_d9317bf392a1443002ba4bf0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_1e5c25dd-e5a9-45e9-98ff-1d9ff7dbdafd","section_id":"sec_3b5832dc-a184-46de-a409-c3c2ed0c6afe","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"LPDDRをCPUに近づけると、メモリ帯域/Wが改善しやすく、AI前処理、RAG、推論制御、サンドボックス、エージェント処理には有利です。  \nただし、従来のDDR DIMM中心サーバーに比べると、メモリ増設性、交換性、構成自由度が変わる可能性があります。","quote_start":0,"quote_end":128,"text_sha256":"c9630efd4e9a7f43bcf4f780847a69210f6f864e8a3a62198aec53d50c7c60b9","block_sha256":"c9630efd4e9a7f43bcf4f780847a69210f6f864e8a3a62198aec53d50c7c60b9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_1e5c25dd-e5a9-45e9-98ff-1d9ff7dbdafd"},{"id":"occ_17ad613953c978bcf905224f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_271e85a2-77fb-4a00-8167-ed8a4e978a7a","section_id":"sec_271f5e2f-1f69-4968-bdcc-80b95ca42379","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":89,"end":91,"exact":"推論","quote":"、RAS、セキュリティ、管理機能**です。  \nまた、Intel AMXやAVX-512により、CPU側のAI推論や行列処理も強化しています。","quote_start":34,"quote_end":105,"text_sha256":"ca41898e3ab76e06bf8c2a2f086677b4abbf336506d7bf80f4a13c37478e4366","block_sha256":"ca41898e3ab76e06bf8c2a2f086677b4abbf336506d7bf80f4a13c37478e4366","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_271e85a2-77fb-4a00-8167-ed8a4e978a7a"},{"id":"occ_57386dc2a009dbc3ccdd0d6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_2d6ec36a-da38-4042-a1b6-93a6f0906180","section_id":"sec_5480626e-0780-45c7-ac94-615bcade6ae1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":89,"end":91,"exact":"推論","quote":"NPU統合**です。  \n将来的にOryon CPU、Hexagon NPU、高速SerDesを組み合わせて、推論特化AIDCへ入る可能性があります。","quote_start":34,"quote_end":109,"text_sha256":"b11525091ecc3b8dbdd98ac6f2074f7e8ee45e75105406a2412f0b62326e1a38","block_sha256":"b11525091ecc3b8dbdd98ac6f2074f7e8ee45e75105406a2412f0b62326e1a38","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_2d6ec36a-da38-4042-a1b6-93a6f0906180"},{"id":"occ_bbd9bd71b7b07973a0c8a168","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_457c0788-d41a-4817-94d2-67cc159714e6","section_id":"sec_5480626e-0780-45c7-ac94-615bcade6ae1","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"Qualcommは現時点では、AIDC向けCPUというより、**AI推論アクセラレータと将来のデータセンターCPU候補**として見るべきです。  \nQualcommはAI200 / AI250でラックスケール推論市場へ参入し、AI200は2026年、AI250は2027年","quote_start":0,"quote_end":136,"text_sha256":"72f7d4edde1643537e898b937c5b82ea30ea49836a6eec9d9361522e8aee8c56","block_sha256":"72f7d4edde1643537e898b937c5b82ea30ea49836a6eec9d9361522e8aee8c56","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_457c0788-d41a-4817-94d2-67cc159714e6"},{"id":"occ_322bfae507788206b39a1ccd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_47facbee-7b97-4f47-beaa-bc165fb146a6","section_id":"sec_9374547f-f098-42cb-9e7b-85fe7e9749e4","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":252,"end":254,"exact":"推論","quote":"ラの保守本流CPU\n\nKunpeng\n  = 中国主権AI基盤のCPU\n\nQualcomm\n  = 低電力AI推論・将来AIDC CPU候補\n```","quote_start":197,"quote_end":271,"text_sha256":"7366246fe51070abfc790d3f2547b52779661a5968db2a4b7752f6645a9550a9","block_sha256":"7366246fe51070abfc790d3f2547b52779661a5968db2a4b7752f6645a9550a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_47facbee-7b97-4f47-beaa-bc165fb146a6"},{"id":"occ_f9fe1a764932db0f43575440","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_50381668-6cf5-46be-90ba-fe4d22203f25","section_id":"sec_a23c9343-6d7a-40ae-af1c-7a53127eaadc","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":102,"end":104,"exact":"推論","quote":" ↓\nツール呼び出し\n  ↓\nコード実行・サンドボックス\n  ↓\nDB / ストレージアクセス\n  ↓\nLLM推論\n  ↓\n結果評価\n  ↓\n次の行動計画\n```","quote_start":47,"quote_end":128,"text_sha256":"e5519681681321ee73c09e6ee9d02e47ce9cf8712927bf3db9f15eba42ed6711","block_sha256":"e5519681681321ee73c09e6ee9d02e47ce9cf8712927bf3db9f15eba42ed6711","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_50381668-6cf5-46be-90ba-fe4d22203f25"},{"id":"occ_8bef24f76f6efb66fbe66228","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_6face411-cde2-4de1-bcda-6de73735d9e2","section_id":"sec_cad56c48-5395-436e-9a82-75887cac8533","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":65,"end":67,"exact":"推論","quote":"\n金融・官公庁\nDB\nVMware / Windows Server\nオンプレ\nx86最適化済みソフト\nCPU推論補助\n```","quote_start":10,"quote_end":73,"text_sha256":"b8a9822d38f57e4c40a222f3c0a9d4f2cc000b4577b038728e94cb7ab317b0a9","block_sha256":"b8a9822d38f57e4c40a222f3c0a9d4f2cc000b4577b038728e94cb7ab317b0a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_6face411-cde2-4de1-bcda-6de73735d9e2"},{"id":"occ_192ba84dacc7f23e43d116a5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_a6ee1aad-d258-4e59-afdc-03362bb2dd35","section_id":"sec_d7d76831-a3ef-48be-8487-e127199d589d","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"```\nクラウド汎用サーバー\nVM / コンテナ\nDB / 分析\nHPC\nAI推論サーバーのホストCPU\nAMD Instinct / Heliosラックの制御\nx86互換が重要な企業環境\n```","quote_start":0,"quote_end":98,"text_sha256":"ca57576a0ca606f69e0aa50befdd17a38d7dcd47f7044ea5cc7509d9663fa24a","block_sha256":"ca57576a0ca606f69e0aa50befdd17a38d7dcd47f7044ea5cc7509d9663fa24a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_a6ee1aad-d258-4e59-afdc-03362bb2dd35"},{"id":"occ_eb58269f3947e4db5ebb5577","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_a89e8aea-3183-4ad2-a9a3-64ee8de7d76c","section_id":"sec_73c88c5c-d00f-4152-a231-fb3b751d4978","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":78,"end":85,"exact":"KVキャッシュ","quote":"単体としての汎用性より、Rubin GPUラック全体の実効性能を最大化するCPUです。GPUを待たせないこと、KVキャッシュやagentic AI制御を回すこと、NVLink-C2CでCPUとGPUを一体化することに価値があります。","quote_start":23,"quote_end":139,"text_sha256":"939788ca91850969a573e7da41c527e4a332bea752f108f418619bf2ac2b90b6","block_sha256":"939788ca91850969a573e7da41c527e4a332bea752f108f418619bf2ac2b90b6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_a89e8aea-3183-4ad2-a9a3-64ee8de7d76c"},{"id":"occ_8055fcae7261b5f6864d8066","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_a960b2f1-8b48-48e7-b002-872d56852e50","section_id":"sec_1635d7ea-f4aa-4fa4-9d14-09326e2abbef","layer":"code","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"```\nGPUへのデータ供給\n推論リクエストのスケジューリング\nKVキャッシュ管理\n強化学習環境の実行\nAIエージェントのツール実行\nGPU-GPU通信の制御\nDPU/NIC/ストレージ制御\nマルチテナント分離\np99レイテンシ管理\n","quote_start":0,"quote_end":117,"text_sha256":"303e0e6424ff38591fc3be6368c8ed0dcdbdff1fefa1891acc610ab2d8a266c5","block_sha256":"303e0e6424ff38591fc3be6368c8ed0dcdbdff1fefa1891acc610ab2d8a266c5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_a960b2f1-8b48-48e7-b002-872d56852e50"},{"id":"occ_d027e987931a3cdb33c36267","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_a9957207-fffe-409d-bf7b-84e7fb857ea1","section_id":"sec_f9bafe6e-b4be-47fb-b715-612ff6462692","layer":"code","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":179,"end":186,"exact":"KVキャッシュ","quote":"VLink-C2C\n   CPUとRubin GPUを高帯域・低遅延でつなぐ\n\n4. AI制御向け設計\n   KVキャッシュ、推論リクエスト、RL環境、ツール実行を高速化\n```","quote_start":124,"quote_end":213,"text_sha256":"4a2b91ba61f96dfe40096063d2769223e6684523d7918cec0d0517fd1c73fb80","block_sha256":"4a2b91ba61f96dfe40096063d2769223e6684523d7918cec0d0517fd1c73fb80","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_a9957207-fffe-409d-bf7b-84e7fb857ea1"},{"id":"occ_7e29e863de7b133ae2fdf02a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_b1638c8d-cd82-4c82-b134-edbdf49cf86a","section_id":"sec_a23c9343-6d7a-40ae-af1c-7a53127eaadc","layer":"body","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"生成AIインフラでは、GPUやAIアクセラレータが主役に見えます。実際、LLMの巨大な行列演算、学習、推論の中心はGPU、TPU、NPU、ASICです。  \nしかし、AIデータセンター、つまりAIDCが「単発推論」から**エージェントAI、RAG、ツール実行、コード実行、強化学習、マルチテナント推論**へ","quote_start":0,"quote_end":153,"text_sha256":"b075b7247a8fc7c2d1831a8ed2fb07ae3ffb5297b0d9ae08bd5214f173bc4ab7","block_sha256":"b075b7247a8fc7c2d1831a8ed2fb07ae3ffb5297b0d9ae08bd5214f173bc4ab7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_b1638c8d-cd82-4c82-b134-edbdf49cf86a"},{"id":"occ_b5798d696ef218680dc05ab7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_bf75a484-198e-4a43-a195-2c3926a24415","section_id":"sec_8dd3e000-30a9-46e4-8f9a-1510e88c3fdb","layer":"code","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":53,"end":60,"exact":"KVキャッシュ","quote":"```\nRubin GPUとのNVLink-C2C\nLPDDR5X/SOCAMMによるGPU HBM補助\nKVキャッシュ管理\n強化学習環境\nagentic AIのサンドボックス実行\nNVIDIAラック全体のGPU稼働率最大化\n```","quote_start":0,"quote_end":116,"text_sha256":"5c0ef6847e367f07483c0e0157f4cb5fb51be7347fedd3f5314942e174f0b85a","block_sha256":"5c0ef6847e367f07483c0e0157f4cb5fb51be7347fedd3f5314942e174f0b85a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4/#blk_bf75a484-198e-4a43-a195-2c3926a24415"},{"id":"occ_98f6d6fd4032459ae684db5a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_93e994ee-fc71-4a2c-868a-e0b16e4b39b4","work_id":"wrk_95f3adb2-9ef8-4c60-8a3b-5ded0268dac4","block_id":"blk_c157daba-d987-47a8-9e83-ded3c9111ab9","section_id":"sec_ed68ba05-8997-4209-854e-f87bece86dfb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"AIエージェント時代には、GPUやAI ASICだけでなく、CPU需要も拡大します。Armは、AIが学習・単発推論からエージェント型ワークロードへ進むと、CPUがreasoning、coordination、data 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([Reuters](https://www.reuters.com/technology/openai-sees-compute-spend-arou","quote_start":218,"quote_end":375,"text_sha256":"25f4f86cafe54747de9aa8d76280c07b70f164971309de9979f31ca73410ec53","block_sha256":"25f4f86cafe54747de9aa8d76280c07b70f164971309de9979f31ca73410ec53","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9af30ed5-ec26-4204-9f65-58324ee5fba5/#blk_3271ccc4-9c28-4793-81f2-0a35f56bb012"},{"id":"occ_06e6f29cce23935fab91b64d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9af30ed5-ec26-4204-9f65-58324ee5fba5","work_id":"wrk_c56e9825-8401-406e-aaae-662904613d26","block_id":"blk_32724a99-317e-4073-abe3-fa8d9becb795","section_id":"sec_93c69a8b-1120-42a5-ae29-2e2c7c6c380e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":49,"end":51,"exact":"推論","quote":"ただし、Anthropicの本当の論点は未来の計算資源です。ここは会社が総額を明示していないので、推論になります。OpenAIのStargateは5000億ドルで10GW、ざっくり**1GWあたり500億ドル**です。この単価をそのまま当てはめるのは乱暴ですが、Anthropicが確保した3.5GWを","quote_start":0,"quote_end":151,"text_sha256":"d94becdf3774c03f8ca98da7a8783f9ea90ed88097097aaf4f849b515ed30023","block_sha256":"d94becdf3774c03f8ca98da7a8783f9ea90ed88097097aaf4f849b515ed30023","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9af30ed5-ec26-4204-9f65-58324ee5fba5/#blk_32724a99-317e-4073-abe3-fa8d9becb795"},{"id":"occ_758d7e53784dfa8bc4074633","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9af30ed5-ec26-4204-9f65-58324ee5fba5","work_id":"wrk_c56e9825-8401-406e-aaae-662904613d26","block_id":"blk_ee23a9b1-f549-4e96-bdba-84f098f4a875","section_id":"sec_1fefdae2-1ff3-4c39-bd7c-362ea91ed895","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":105,"end":107,"exact":"推論","quote":"nはHBM4量産に入り、SandiskとSK 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**300W〜1000W以上**\n- **用途**: クラウドAI、データセンターでの大規模LLM推論\n- **特徴**: 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**1W〜10W** 簡単なAI推論、画像認識   \n**中程度（10W〜50W）** Jetson Orin Nano, Google Coral **10W〜50W** ロボット、組み込みAI   \n**高性能（50W〜300W）*","quote_start":116,"quote_end":273,"text_sha256":"df68a032fc9a817ad36b9b6e22a906a31a0d0de3079f1731ec9e57a92bafd7bc","block_sha256":"df68a032fc9a817ad36b9b6e22a906a31a0d0de3079f1731ec9e57a92bafd7bc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9af8ea2d-dc2b-4fe3-8d67-94ae163703d8/#blk_e5897565-a04c-42a7-9283-150f577f3ce5"},{"id":"occ_0290253809f88ee259cbc3b0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9c10c6f9-4055-4591-a368-74112c253a08","work_id":"wrk_a91b4c62-c6f7-43f1-8bc0-9224e28ea11a","block_id":"blk_8eb52957-0ab1-4238-8367-e02cece6a4f7","section_id":"sec_ea7b999b-06bd-48a4-9da0-2fdeff36b586","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":55,"end":57,"exact":"推論","quote":"そのため、市場参加者がAIの存在は理解していても、一人の利用者が複数のAIエージェントを常時稼働させることで、推論需要がどこまで増えるかまでは十分に織り込めていない可能性がある。","quote_start":0,"quote_end":89,"text_sha256":"2ca91826562293bf546cec795e61d3196c03a73ffc3aab3952dfab9d073b58fd","block_sha256":"2ca91826562293bf546cec795e61d3196c03a73ffc3aab3952dfab9d073b58fd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9c10c6f9-4055-4591-a368-74112c253a08/#blk_8eb52957-0ab1-4238-8367-e02cece6a4f7"},{"id":"occ_e1f1aabe388845e6c0847ab1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9d4ead97-69cb-4ce7-8223-0346945539b2","work_id":"wrk_1392dff6-1a1d-4910-b2f4-beea19fd2c7f","block_id":"blk_777454e2-5e3b-4fc7-b9a0-012e2f7c7e60","section_id":"sec_0fb6bd74-616d-4672-9615-af57c927fb45","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"もしAIモデルの収益化が遅れたり、推論単価が急落したり、より効率的なモデルで必要計算量が想定より下がった場合、データセンター投資の回収期間が長くなります。いわゆる「AIインフラ・バブル」リスクです。","quote_start":0,"quote_end":99,"text_sha256":"aee72b239c89e18fc148650bd67139f35e9be3f3ac9e2be3dedcc0e63e2dc213","block_sha256":"aee72b239c89e18fc148650bd67139f35e9be3f3ac9e2be3dedcc0e63e2dc213","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9d4ead97-69cb-4ce7-8223-0346945539b2/#blk_777454e2-5e3b-4fc7-b9a0-012e2f7c7e60"},{"id":"occ_6c32e8708c9bbe1e6f21c24d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_9d4ead97-69cb-4ce7-8223-0346945539b2","work_id":"wrk_1392dff6-1a1d-4910-b2f4-beea19fd2c7f","block_id":"blk_c804b5e3-3ef4-494c-a9f4-80f31312fdc2","section_id":"sec_747a9624-7d4d-43a5-bb17-0c60a7f63ec2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":114,"end":116,"exact":"推論","quote":"産として持ち、Ampereも買収しています。これにより、将来的には「AIインフラ向けArmサーバーCPU」や「推論向け省電力CPU」との接続も考えられます。([ソフトバンクグループ株式会社][8])","quote_start":59,"quote_end":158,"text_sha256":"2b578e9b97c757f106be8cd6ca9d5d82b806b74fb9b6f1e285bea3d811fb1462","block_sha256":"2b578e9b97c757f106be8cd6ca9d5d82b806b74fb9b6f1e285bea3d811fb1462","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_9d4ead97-69cb-4ce7-8223-0346945539b2/#blk_c804b5e3-3ef4-494c-a9f4-80f31312fdc2"},{"id":"occ_f7f0ac37f75019dc848ff08d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_01a01d85-3236-402a-870f-25d19bfe6377","section_id":"sec_040ed602-09d6-4eca-8212-fe270b38fcb5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":38,"end":40,"exact":"推論","quote":"TPU 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length、speculative decoding、KV cache、network、kernelによって数倍以上変わる。","quote_start":12,"quote_end":102,"text_sha256":"188d9eb54fd525bde511e71b76d7f505d9ffc00895d44080ff69d27a116e6de4","block_sha256":"188d9eb54fd525bde511e71b76d7f505d9ffc00895d44080ff69d27a116e6de4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_08f14c10-8e03-45b0-800c-6f55f057202a"},{"id":"occ_6a709ff3db4805b2ae02ce5d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_1b5ce817-2a71-4bd3-9bbf-a16be1a25b3f","section_id":"sec_040ed602-09d6-4eca-8212-fe270b38fcb5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論ではFLOPSだけ増やしても駄目なのである。","quote_start":0,"quote_end":24,"text_sha256":"d898cc0c597f53c6b697229b454f5431deedf656ecdf10ea37fc217d4743f578","block_sha256":"d898cc0c597f53c6b697229b454f5431deedf656ecdf10ea37fc217d4743f578","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_1b5ce817-2a71-4bd3-9bbf-a16be1a25b3f"},{"id":"occ_2c6175f2a3700139d28d5f57","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_3f2f5caa-70df-49f6-a040-3ca3c8693a81","section_id":"sec_c13f3d94-5964-45ea-aa03-a929306e5a84","layer":"body","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":52,"end":58,"exact":"decode","quote":"そのためここでは実測performanceを予言するのではなく、**4チップのmemory-bound decode能力を同じ条件で比較するためのtoken-equivalent**を作る。","quote_start":0,"quote_end":95,"text_sha256":"2d93b1b1bcb6661a9b9a976eb2e39c18ae0169d85ddd81b237bdd15a0e486199","block_sha256":"2d93b1b1bcb6661a9b9a976eb2e39c18ae0169d85ddd81b237bdd15a0e486199","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_3f2f5caa-70df-49f6-a040-3ca3c8693a81"},{"id":"occ_c99afd6826e1b438d5030dab","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_49081a24-4760-4f64-b8a3-f2ca44781a2d","section_id":"sec_0255c81b-9205-4aa6-9324-98f5358fe314","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"```\nより強いAI\n   ↓\nより良いAI chipを設計\n   ↓\n推論コスト低下\n   ↓\nより大量のAIを実行\n   ↓\nさらに強いAI\n```","quote_start":0,"quote_end":76,"text_sha256":"4bd61ecdb4831079a2c19715b755cf0596c37bd81550427420d26cca0f8f0255","block_sha256":"4bd61ecdb4831079a2c19715b755cf0596c37bd81550427420d26cca0f8f0255","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_49081a24-4760-4f64-b8a3-f2ca44781a2d"},{"id":"occ_40d4d36d1439b7b0cd66cb5e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_54ab66e8-967a-452d-9de4-78516f7a2d84","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"Prefill","quote":"OpenAI自身も、Prefillはcompute 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bandwidthを演算能力に対して極端に厚くしているからだ。","quote_start":0,"quote_end":62,"text_sha256":"a401b36b86844a79590e3541d318b7be55c5196550faa62404bbc3472285d51d","block_sha256":"a401b36b86844a79590e3541d318b7be55c5196550faa62404bbc3472285d51d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_63fdd7b2-f9b1-4739-b945-f76018ee0678"},{"id":"occ_d88628549c8fd15284ddc681","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_7c47333e-5c03-4e2d-ab28-bddd342a1dcb","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":5,"end":13,"exact":"KV cache","quote":"これは特にKV 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cacheを繰り返し参照するため、問題になるのはFLOPSよりも、","quote_start":0,"quote_end":58,"text_sha256":"e08fe84fb9cf9a7c153e28c561184aad80356699769a61ef041ec2ca497b9506","block_sha256":"e08fe84fb9cf9a7c153e28c561184aad80356699769a61ef041ec2ca497b9506","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_84d81373-6789-4420-8f69-b95ace3523c2"},{"id":"occ_c644354ecebe490e1ee76406","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_8a60f58a-5346-41b9-80d6-bff9c5edac31","section_id":"sec_b662da1b-fd3e-4f09-95e1-7f64658595aa","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"**データを動かさず、HBMを使い切り、AI自身にkernelを書かせることで、MW当たりの推論効率を最大化する。**","quote_start":0,"quote_end":59,"text_sha256":"3254ae6791581ff15f06fc283f294d4973ab33e392ba6a3dbcf6672c3c488ead","block_sha256":"3254ae6791581ff15f06fc283f294d4973ab33e392ba6a3dbcf6672c3c488ead","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_8a60f58a-5346-41b9-80d6-bff9c5edac31"},{"id":"occ_af40f96de7cdab65a3abeb3f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_96a38c59-c5fd-47db-b0c1-a24d87473850","section_id":"sec_b662da1b-fd3e-4f09-95e1-7f64658595aa","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":27,"end":36,"exact":"inference","quote":"特にJalapeñoのようなmemory-heavy inference ASICでは、同じ1MWからRubinの約2倍のHBM wafer需要が発生し得る。","quote_start":0,"quote_end":79,"text_sha256":"e7904079e4a2689c2ab08f8d014f5e4bca058d931141e66939f625cde33841e6","block_sha256":"e7904079e4a2689c2ab08f8d014f5e4bca058d931141e66939f625cde33841e6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_96a38c59-c5fd-47db-b0c1-a24d87473850"},{"id":"occ_eee165b1c2e69a13cee112a2","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_a5c8b378-e098-4663-baf1-2d9991e06160","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefillでは入力された大量のtokenを一気に計算するため、Matrix 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ASICがmemory-heavyになるほど、","quote_start":0,"quote_end":36,"text_sha256":"0b4540e0512e186f25a55d254d55b8ba11ce0cc01e2ff641760a4e1337c47d3d","block_sha256":"0b4540e0512e186f25a55d254d55b8ba11ce0cc01e2ff641760a4e1337c47d3d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_c9891a00-4ecb-414e-9a33-a575a4cc641d"},{"id":"occ_60038753830d59878041317c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_cd2fae51-7d95-4ca8-9761-794490c992c3","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":1,"matched_aliases":["KV cache"],"evidence":{"text_basis":"markdown","start":17,"end":25,"exact":"KV cache","quote":"OpenAI公式も、weightやKV cacheを含むmodel stateを明示的に配置し、localに保持できることをJalapeñoの重要な特徴として挙げている。([OpenAI](https://openai.com/index/jalap","quote_start":0,"quote_end":125,"text_sha256":"794b7533a52039aaf0d5782b620cfc2a4fca2f2436d8877ebfbdf80c4cc6bfe1","block_sha256":"794b7533a52039aaf0d5782b620cfc2a4fca2f2436d8877ebfbdf80c4cc6bfe1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_cd2fae51-7d95-4ca8-9761-794490c992c3"},{"id":"occ_0e8316175aa259f001e0a4b3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_d2a386a8-6b93-43fe-8881-fe329f5ba371","section_id":"sec_4247b88b-3255-4ff2-80e4-0720e6cd66cb","layer":"body","character_id":null,"count":3,"matched_aliases":["KV cache","推論"],"evidence":{"text_basis":"markdown","start":49,"end":57,"exact":"KV cache","quote":"**NVIDIA Rubinほど巨大な演算能力やチップ間通信能力を持たせなくても、HBM、演算器、KV cache、ネットワーク、カーネル、モデルそのものを一つの推論システムとして設計すれば、LLM推論ではRubin級あるいはそれ以上の電力効率を狙える。**","quote_start":0,"quote_end":129,"text_sha256":"283b88c7f3765e71a254b42c919cf637b64b8d830a5c5b339ec4b18eb6450b39","block_sha256":"283b88c7f3765e71a254b42c919cf637b64b8d830a5c5b339ec4b18eb6450b39","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_d2a386a8-6b93-43fe-8881-fe329f5ba371"},{"id":"occ_bd1ab8393ee77d7438aff70c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_db9c643f-1e4d-4997-b2f5-920eb37385b8","section_id":"sec_0a17111a-c997-4fe7-9473-59a409060566","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":23,"end":32,"exact":"Inference","quote":"それでも、これらの前提を保守的に置いても、**Inference ASICのmemory-heavy化によって、GPUの市場シェア低下とHBM需要拡大が同時に起こり得る**という結論そのものは変わりにくい。","quote_start":0,"quote_end":103,"text_sha256":"1fc88b89b7fbc83b0b2f7d7e145ef8f2602e54db156fb47036ce379dca2ecfc3","block_sha256":"1fc88b89b7fbc83b0b2f7d7e145ef8f2602e54db156fb47036ce379dca2ecfc3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_db9c643f-1e4d-4997-b2f5-920eb37385b8"},{"id":"occ_affeac500390b54fe703c1f8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_dd9d3ae8-a22d-4ebf-913b-a32f63a34679","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":2,"end":9,"exact":"Prefill","quote":"**Prefill → KVをその場に保持 → Decode**","quote_start":0,"quote_end":32,"text_sha256":"0a59e481d90c56baa0567bcddbb39cb16a9e4d74b261025937194ea886b090d6","block_sha256":"0a59e481d90c56baa0567bcddbb39cb16a9e4d74b261025937194ea886b090d6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_dd9d3ae8-a22d-4ebf-913b-a32f63a34679"},{"id":"occ_82d33eea18f249d9536e8ae3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_f0545578-e5ca-41eb-8e30-13b77aa24759","section_id":"sec_57fb5a48-a7f1-4109-9e0b-79810b24a712","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":2,"end":8,"exact":"Decode","quote":"一方Decodeでは、通常1 tokenずつ生成する。","quote_start":0,"quote_end":27,"text_sha256":"bf157f811552e75cff539c11f367a6c1c92df337f2a656858b77d5b6ad415ee5","block_sha256":"bf157f811552e75cff539c11f367a6c1c92df337f2a656858b77d5b6ad415ee5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_f0545578-e5ca-41eb-8e30-13b77aa24759"},{"id":"occ_9906ea17d5899b0b3c82f210","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_f61a1469-4b91-4a2e-b312-199991a3a79b","section_id":"sec_4247b88b-3255-4ff2-80e4-0720e6cd66cb","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"最も目を引くのは、Jalapeñoが一部のLLM推論条件でNVIDIA Blackwellを大きく上回り、SemiAnalysisが公開済みのRubinデータと比較した場合にも、電力当たりの推論スループットで非常に競争力のある結果を示していることだ。","quote_start":0,"quote_end":125,"text_sha256":"77f40c883291eac2882324b98465a17a5c61d41697ef7f76828bfcd3507fa011","block_sha256":"77f40c883291eac2882324b98465a17a5c61d41697ef7f76828bfcd3507fa011","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_f61a1469-4b91-4a2e-b312-199991a3a79b"},{"id":"occ_127b87918d24bdd3c2f61fa3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a165cefe-658c-456f-b164-71170f172e7c","work_id":"wrk_52bd95ff-8ae9-42ac-a817-cc820320dd76","block_id":"blk_ff5bcd53-e681-439d-8486-fe3d9ecbcd06","section_id":"sec_32fa9297-e703-4f7a-9472-a0d06e181927","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":100,"end":109,"exact":"inference","quote":" \n   本稿の推定。Google非公表。  \n   dense training NVFP4。NVIDIAはinferenceで50PFLOPSも公表している。","quote_start":45,"quote_end":126,"text_sha256":"1a012a12221a888b8933dd5a994c15003a89235796b6a948c49e2d672ebd74ab","block_sha256":"1a012a12221a888b8933dd5a994c15003a89235796b6a948c49e2d672ebd74ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a165cefe-658c-456f-b164-71170f172e7c/#blk_ff5bcd53-e681-439d-8486-fe3d9ecbcd06"},{"id":"occ_f347a82d1dc6ab28a7b9117d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a199924e-5c69-4344-bb57-0e0a36cda082","work_id":"wrk_025fb061-467f-4f76-974c-2b1ecbd5a160","block_id":"blk_2d08e75e-1630-4198-a92f-1f5571371e19","section_id":"sec_4437dec3-f3c2-42fc-95e3-d0b6e14c910f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"## Boardflyは推論向けに距離を縮める","quote_start":0,"quote_end":23,"text_sha256":"b241a1ff94647922dd5b6ac1bc3662973136a8658f0f7d4d78f1f74e8c1c1763","block_sha256":"b241a1ff94647922dd5b6ac1bc3662973136a8658f0f7d4d78f1f74e8c1c1763","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a199924e-5c69-4344-bb57-0e0a36cda082/#blk_2d08e75e-1630-4198-a92f-1f5571371e19"},{"id":"occ_1eca6ba7f7f39ec64d6409fc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a199924e-5c69-4344-bb57-0e0a36cda082","work_id":"wrk_025fb061-467f-4f76-974c-2b1ecbd5a160","block_id":"blk_44d50442-3456-49ae-a91c-44fd9fa0123d","section_id":"sec_a896a50a-c2b0-49ca-b10a-d9494b27cd38","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"* 3Dトーラスで9,600 TPU\n* Boardflyによる推論向け階層接続\n* Virgoで13万4,000 TPU超\n* Pathwaysで100万TPU超","quote_start":0,"quote_end":82,"text_sha256":"c9b600c3d20d458d63706aa9ff43cb78e3e6d90e38f8f442e8109fac1f33171a","block_sha256":"c9b600c3d20d458d63706aa9ff43cb78e3e6d90e38f8f442e8109fac1f33171a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_a199924e-5c69-4344-bb57-0e0a36cda082/#blk_44d50442-3456-49ae-a91c-44fd9fa0123d"},{"id":"occ_91b2ed56d92da5bc689ab336","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_a199924e-5c69-4344-bb57-0e0a36cda082","work_id":"wrk_025fb061-467f-4f76-974c-2b1ecbd5a160","block_id":"blk_85cc8186-c7af-4830-a937-9234af29fa5d","section_id":"sec_74fce932-10b9-49ff-b0e1-77c70e4b9305","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":61,"end":68,"exact":"KVキャッシュ","quote":"待できるのは、Data 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価格低下は短期的にモデル企業の利益率を圧迫し得るが、長期的には推論需要を増やす可能性がある。\n- AIエージェントが商取引へ入るには、本人確認、利用上限、トークン化、不正検知、チャージバックまで必要になる。\n- 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|\n| 重要インフラ | 金融、医療、製造、通信、電力で使えるのか |\n| 国内推論 | 国内データセンターで推論できるのか |\n| 規制変更 | 猶予や例外措置はあるのか |\n| 有事対応 | 有事でも止められない契約や国家間合意を結べるのか 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JalapeñoはNVIDIAを置き換えるというより、OpenAIの推論を専用化する刃ですね。","quote_start":0,"quote_end":57,"text_sha256":"b8ec525e9d1ba9e6e700cbe808c048a2c199d9013d9c1a1c137595119679eebc","block_sha256":"b8ec525e9d1ba9e6e700cbe808c048a2c199d9013d9c1a1c137595119679eebc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_35fde5f8-2914-4a15-bb80-63dbe4feddaa"},{"id":"occ_68b3ee5d40fc713d96402488","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_41e4dd8d-7543-4951-bb15-66ece116fb65","section_id":"sec_5c47fffe-497d-44bc-85e1-1a0843444a7f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"第一に、LLM推論専用設計である。\nGPUのように何でもできる汎用性よりも、OpenAIが実際に毎日動かしているLLM推論のデータ移動、メモリ、ネットワーク、スケジューリングに最適化している。","quote_start":0,"quote_end":96,"text_sha256":"70996c8a7d109dc27b3f08127e8cc63e6ed682b82c61733bd50be4440ed84f62","block_sha256":"70996c8a7d109dc27b3f08127e8cc63e6ed682b82c61733bd50be4440ed84f62","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_41e4dd8d-7543-4951-bb15-66ece116fb65"},{"id":"occ_3e8dcb65b18d2b60091e06db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_50742c5b-959e-40bf-bea9-afc16f58d6e9","section_id":"sec_5c47fffe-497d-44bc-85e1-1a0843444a7f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":85,"end":87,"exact":"推論","quote":"う単純な話ではない。\nNVIDIAを最大の基盤として使いながら、AMDで供給分散し、Broadcomで自社専用推論ASICを持ち、MicrosoftやOracleなどのデータセンターパートナーを使ってAIファクトリーを広げる。これがOpenAIのマルチベンダー戦略である。","quote_start":30,"quote_end":166,"text_sha256":"95fc61d3125b17d438a5909b22bfbca4d2f3ea72f4e0b566013d679c00556190","block_sha256":"95fc61d3125b17d438a5909b22bfbca4d2f3ea72f4e0b566013d679c00556190","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_50742c5b-959e-40bf-bea9-afc16f58d6e9"},{"id":"occ_ccef2cfe66be3c49166175e3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_5fd48011-e998-4ab4-a794-79dc4db4e94d","section_id":"sec_76c2b01f-d26d-480d-99ee-ff8233373150","layer":"body","character_id":null,"count":2,"matched_aliases":["KV 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cache、バッチング、ネットワーク、電力あたりトークンが利いてくる。","quote_start":29,"quote_end":161,"text_sha256":"684f959f35d03a82049b7164a0b017bcf95c3f8ba563539aefb0ac5250c7ee47","block_sha256":"684f959f35d03a82049b7164a0b017bcf95c3f8ba563539aefb0ac5250c7ee47","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_5fd48011-e998-4ab4-a794-79dc4db4e94d"},{"id":"occ_2b048ac0907cf998136c3722","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_60b82666-2c57-442d-a931-e8e181432428","section_id":"sec_5c47fffe-497d-44bc-85e1-1a0843444a7f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":65,"end":67,"exact":"推論","quote":"omのTomahawk系ネットワークシリコンも関係する。\nJalapeñoは、単に演算チップだけでなく、大規模推論クラスタをどう接続するかまで含めてBroadcomの強みを使う。","quote_start":10,"quote_end":99,"text_sha256":"fed3f19f1c06bf8923af91471247e3477f04bfaa3fb4b6f1c455cc8b125eef0a","block_sha256":"fed3f19f1c06bf8923af91471247e3477f04bfaa3fb4b6f1c455cc8b125eef0a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_60b82666-2c57-442d-a931-e8e181432428"},{"id":"occ_4b9707c75328e348dbff071d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_7b154d0b-cc5c-42a5-a981-5cdfa4808624","section_id":"sec_76c2b01f-d26d-480d-99ee-ff8233373150","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"## さらに深める: Jalapeñoは「推論の専用刃」である","quote_start":0,"quote_end":31,"text_sha256":"b991dae63d50bc401bd06ef9bbdbf5c5e5ae27d4285eeb81390727f0108c6d72","block_sha256":"b991dae63d50bc401bd06ef9bbdbf5c5e5ae27d4285eeb81390727f0108c6d72","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_7b154d0b-cc5c-42a5-a981-5cdfa4808624"},{"id":"occ_2c02609926ce216a25f0e1ea","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_89464f98-ff4c-46ed-bb0f-c84f26f3d970","section_id":"sec_0bd48637-e5f0-4b12-87d8-ee895d5d20c1","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":123,"end":130,"exact":"KVキャッシュ","quote":"、all-reduce、all-to-all、MoEのexpert routing、テンソル並列、モデル並列、KVキャッシュ共有などが頻繁に発生する。","quote_start":68,"quote_end":143,"text_sha256":"b4052a30f8e9bcd7b7ca683df79e253a732fbcde5c4889f0e086456a7996e0d8","block_sha256":"b4052a30f8e9bcd7b7ca683df79e253a732fbcde5c4889f0e086456a7996e0d8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_89464f98-ff4c-46ed-bb0f-c84f26f3d970"},{"id":"occ_ef25f6b26ca81021466425cc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_8dc43fb4-26f5-42b4-92bd-909c54066bec","section_id":"sec_5c47fffe-497d-44bc-85e1-1a0843444a7f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"Jalapeñoは、その中の「推論コストを下げるための専用刃」である。","quote_start":0,"quote_end":35,"text_sha256":"5edf097b3ee35f52d6d4dbc1a24bf960cad5f229e29966bfaff107f5c7688f14","block_sha256":"5edf097b3ee35f52d6d4dbc1a24bf960cad5f229e29966bfaff107f5c7688f14","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_8dc43fb4-26f5-42b4-92bd-909c54066bec"},{"id":"occ_3e407755f83ff1053b2d504e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_98b20dbe-c497-4c1d-8e19-f10256a4d581","section_id":"sec_91b5eea7-9d9c-44d5-a67e-1e1fa33a4803","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"もちろんNVIDIA GPUは依然として中心にある。学習、研究、汎用推論、CUDAエコシステム、NVLink、ラックスケールAIシステムの完成度は圧倒的である。しかし、OpenAI、Google、Meta、AWS、Microsoftのような巨大AI需要家は、NVIDIAだ","quote_start":0,"quote_end":136,"text_sha256":"b8841524b0e4d2c506abb48c385273b9b70003ba797608f8c2facbdaae093252","block_sha256":"b8841524b0e4d2c506abb48c385273b9b70003ba797608f8c2facbdaae093252","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_98b20dbe-c497-4c1d-8e19-f10256a4d581"},{"id":"occ_9b0df66957fd010594a518d5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_9f1af310-dbf4-445d-8b49-84060f39983e","section_id":"sec_74a7f3dd-0ad5-48e1-8731-6ad1cfd8c229","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":19,"end":21,"exact":"推論","quote":"Jalapeñoは、OpenAIが自社推論のコストと性能を制御するためのチップである。\nしかし本質は、OpenAIが「モデル企業」から「AIインフラ設計企業」へ進むことにある。","quote_start":0,"quote_end":88,"text_sha256":"e267b3f7709db4d4511b4cd364da1f08a6b506a2ea4fa12e45dedcc9e5cf5300","block_sha256":"e267b3f7709db4d4511b4cd364da1f08a6b506a2ea4fa12e45dedcc9e5cf5300","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_9f1af310-dbf4-445d-8b49-84060f39983e"},{"id":"occ_5a4bcf3af10a4824546edf92","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_b6faad5a-b2ad-49a3-b7a9-3325a4925a60","section_id":"sec_5c47fffe-497d-44bc-85e1-1a0843444a7f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":13,"end":15,"exact":"推論","quote":"基本的な位置付けは、LLM推論向けのカスタムASICだ。\nここで重要なのは、これはNVIDIA GPUを全面的に置き換える汎用チップではないということだ。Jalapeñoは、ChatGPT、Codex、API、将来のエージェント製","quote_start":0,"quote_end":115,"text_sha256":"db4fa5f7ad812680ab85c969b87770481b9bb7bb67eacd1cc88f01a30c3662ba","block_sha256":"db4fa5f7ad812680ab85c969b87770481b9bb7bb67eacd1cc88f01a30c3662ba","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_b6faad5a-b2ad-49a3-b7a9-3325a4925a60"},{"id":"occ_4bad74542c4f3753fb81795d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_b9721bc5-2671-41e8-aae3-18b45e8f337c","section_id":"sec_76c2b01f-d26d-480d-99ee-ff8233373150","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":16,"end":18,"exact":"推論","quote":"Broadcomが強いのは、この推論用ASICをチップ単体で終わらせない点にある。3.5D XDSiPで演算とHBMを近づけ、Tomahawk/JerichoでAI Ethernet fabricを構成し、CPOで光変換をスイッチASI","quote_start":0,"quote_end":118,"text_sha256":"4f5346e03bbfd822a5d095ba0d24a4fbe04fdaf006d5fc83d05bc6e3101fbc03","block_sha256":"4f5346e03bbfd822a5d095ba0d24a4fbe04fdaf006d5fc83d05bc6e3101fbc03","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_b9721bc5-2671-41e8-aae3-18b45e8f337c"},{"id":"occ_65661e9437712eaa9cbd5548","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_c07e248e-36f3-42c7-8fe0-360083c81ca7","section_id":"sec_76c2b01f-d26d-480d-99ee-ff8233373150","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"```text\n学習・研究\n  → GPU / CUDA / NVLinkの汎用基盤\n\n定常的な大規模推論\n  → ワークロード固定\n  → カスタムASICで効率化\n  → ネットワーク・HBM・冷却まで共同設計\n```","quote_start":0,"quote_end":111,"text_sha256":"ce72aa3d4771fcd6e03982404d3789e36d938979b65cceb4a824a8677b2fa9ae","block_sha256":"ce72aa3d4771fcd6e03982404d3789e36d938979b65cceb4a824a8677b2fa9ae","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_abe6b561-f51c-4549-a97f-aab2b725492e/#blk_c07e248e-36f3-42c7-8fe0-360083c81ca7"},{"id":"occ_a9e170b5296176f5f6210c11","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_abe6b561-f51c-4549-a97f-aab2b725492e","work_id":"wrk_f31c79fd-b257-498a-9ee1-d344ac94ffc7","block_id":"blk_de07d0d8-2b99-41d8-b6eb-cbe68c3f0083","section_id":"sec_82458ffe-e38b-47c4-ac89-851ea5271678","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"- JalapeñoはGPU全面代替ではなく、OpenAIの定常推論を効率化するカスタムASICと見るのが自然である。\n- Broadcomの強みは、ASIC、HBM接続、3.5Dパッケージ、Ethernet、CPOを一体で持つ点にある。\n- 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Unit/推論TPU協議など、さまざまな報道・観測案件がある。だが、顧客名が公式に明示されることは多くない。公式に確認しやすいのは、NVIDIAとのNVLink 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 \n外国顧客に提供できるのか。  \n価格は下がるのか。  \nコストは下がるのか。  \n推論需要は利益になるのか。  \nIPO時に1兆ドル評価を正当化できるのか。  \nそして、その企業は国家戦略と市場の両方に耐えられるのか。","quote_start":19,"quote_end":142,"text_sha256":"fe51cfdb55ae00f4f2650532aa4ee8a0692f069d64267296689e17709d5aa85e","block_sha256":"fe51cfdb55ae00f4f2650532aa4ee8a0692f069d64267296689e17709d5aa85e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_aea0fd30-a496-42e0-975e-3de7b0cd01ab/#blk_1184d0c6-938b-4650-9072-b8e480ddcc08"},{"id":"occ_9ffe3211e7178d893c7ac89f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_aea0fd30-a496-42e0-975e-3de7b0cd01ab","work_id":"wrk_4efede78-d9d9-4a37-854a-51a334476949","block_id":"blk_4cb68324-981a-43a2-9b7b-096180dd6f5f","section_id":"sec_8de470f0-40fd-4b4e-8a61-d49e06f0c28e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":58,"end":60,"exact":"推論","quote":"、1兆ドル評価を守りたい。  \n第二に、GPT-5.6や次のエージェント製品の収益力を見せたい。  \n第三に、推論コストやデータセンター投資の回収可能性を説明できるようにしたい。  \n第四に、Microsoft、Oracle、Broadcom、政府契約、AIインフラ構想との関係を整理したい。  \n第五に、今回のよ","quote_start":3,"quote_end":160,"text_sha256":"3d9f8cb9425f6d41f0ad2121b2470253ff5610e6c3997579e052ccbc3eb4ca5d","block_sha256":"3d9f8cb9425f6d41f0ad2121b2470253ff5610e6c3997579e052ccbc3eb4ca5d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_aea0fd30-a496-42e0-975e-3de7b0cd01ab/#blk_4cb68324-981a-43a2-9b7b-096180dd6f5f"},{"id":"occ_5abe25fc79485785deebb261","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_aea0fd30-a496-42e0-975e-3de7b0cd01ab","work_id":"wrk_4efede78-d9d9-4a37-854a-51a334476949","block_id":"blk_b0eef74c-b506-4a21-9125-f70dc04985be","section_id":"sec_8de470f0-40fd-4b4e-8a61-d49e06f0c28e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"公開市場に出れば、OpenAIは毎四半期、売上、損失、設備投資、推論コスト、政府リスク、訴訟リスク、パートナー依存を説明し続けなければならない。","quote_start":0,"quote_end":72,"text_sha256":"166ed371680ca3fb5119680aca4c31ad215c4e41fc9f18f2c9712a2d5c0f6780","block_sha256":"166ed371680ca3fb5119680aca4c31ad215c4e41fc9f18f2c9712a2d5c0f6780","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_aea0fd30-a496-42e0-975e-3de7b0cd01ab/#blk_b0eef74c-b506-4a21-9125-f70dc04985be"},{"id":"occ_b5e8e09188a35b0444903187","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_00568cd5-5e35-4986-b236-97da6c9315be","section_id":"sec_ee722f45-a659-46eb-9520-c834c4d8ef48","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"大規模学習では米国が強い。\n低コスト推論と大量実装では中国が強い。","quote_start":0,"quote_end":33,"text_sha256":"71886792be17f94a818eead21bba67948d8f284cebcd2ea6c42421be133a3e2b","block_sha256":"71886792be17f94a818eead21bba67948d8f284cebcd2ea6c42421be133a3e2b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_00568cd5-5e35-4986-b236-97da6c9315be"},{"id":"occ_cdef6e2043eecf83e7e9f2a5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_1bf8dd2c-b6a1-4b75-a077-ba72fe171bc7","section_id":"sec_1a2d773e-6190-4df4-b474-895df7552366","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"AIの大脳は、言語・推論・計画・コード生成です。\nAIの小脳は、制御・姿勢・動作・反復・低遅延応答です。","quote_start":0,"quote_end":52,"text_sha256":"c4734b20602d2811cbd14f199694dd4b4f7b870b770534ce91822937e9782e60","block_sha256":"c4734b20602d2811cbd14f199694dd4b4f7b870b770534ce91822937e9782e60","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_1bf8dd2c-b6a1-4b75-a077-ba72fe171bc7"},{"id":"occ_4b680700ae94029b64dcfac5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_1cb4c985-14b9-48b7-8c81-4cc92334dfe0","section_id":"sec_d95e3708-1601-4d57-9687-b3620cb63398","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"しかし今後、AI需要の中心は徐々に推論へ移っていきます。","quote_start":0,"quote_end":28,"text_sha256":"8f9317e0a10ac9d2efd63c56516c50001cb5bfbd57c6146bc0a004962923fad7","block_sha256":"8f9317e0a10ac9d2efd63c56516c50001cb5bfbd57c6146bc0a004962923fad7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_1cb4c985-14b9-48b7-8c81-4cc92334dfe0"},{"id":"occ_5ea9d294c5259082fec26381","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_35553bfb-1166-4614-a1ac-785da37f5abf","section_id":"sec_cc7c3ec0-a661-436b-a56d-f4abc27aaf53","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"PU、クラウド、ソフトウェア生態系で強い。\n- 中国は電力、建設、工場量産、エッジAI、ロボット実装、低コスト推論で強い。\n- AI競争は、学習クラスタの競争と、物理世界への大量配備の競争に分かれる。\n- フィジカルAIでは、現場データ、試作速度、部品密度、保守網が重要になる。\n- 今後のAIインフラ競争は、頭脳","quote_start":16,"quote_end":173,"text_sha256":"ad38a703535360e1f2d11862ffe90cf876004951399dda86a2c904c884039d91","block_sha256":"ad38a703535360e1f2d11862ffe90cf876004951399dda86a2c904c884039d91","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_35553bfb-1166-4614-a1ac-785da37f5abf"},{"id":"occ_662335564fdf7570a64547e4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_37a19844-b7dd-432d-a918-59d6d5f9715e","section_id":"sec_6d8087c9-8606-42ac-a7e7-fc70799912a8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":91,"end":93,"exact":"推論","quote":" --- |\n| 大脳 | 基盤モデル、GPUクラスタ、ソフトウェア生態系 | 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| 70 | 90 | 中国 |\n| エッジAI | 75 | 90 | 中国 |\n| 低コスト推論 | 75 | 90 | 中国 |","quote_start":282,"quote_end":356,"text_sha256":"55bcea786bd7d2263169071a1445d0e57f2aca31c58b234785ab863f304d81a8","block_sha256":"55bcea786bd7d2263169071a1445d0e57f2aca31c58b234785ab863f304d81a8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_a133de3a-a063-4d04-a8f0-5afdfcee7540"},{"id":"occ_dd9bb57bffc3ad81cc375f86","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_b5124b01-b2c1-4aa6-9271-519df58ac1ef","section_id":"sec_e872ff86-dafb-426e-9d86-1911e6d8d8f7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":574,"end":576,"exact":"推論","quote":"ッジ |\n| ソフトウェア生態系 | 95 | 70 | CUDA、開発者、OSSで米国優位 |\n| 低コスト推論 | 75 | 90 | 効率モデル＋安価端末＋国産ASIC |\n総合すると、","quote_start":519,"quote_end":615,"text_sha256":"85d955784e0bff1188031b3be99a9988aac12445c5f8da874b4eb7c20c89637c","block_sha256":"85d955784e0bff1188031b3be99a9988aac12445c5f8da874b4eb7c20c89637c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_afc88027-978f-428a-a597-dd5d129dc441/#blk_b5124b01-b2c1-4aa6-9271-519df58ac1ef"},{"id":"occ_4ed952a59b7f22cf72913dab","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_afc88027-978f-428a-a597-dd5d129dc441","work_id":"wrk_9fae1367-ebbb-47c5-ad7b-3700deb207cd","block_id":"blk_f6c6a122-a7a2-47cc-801a-90f790a49a32","section_id":"sec_e872ff86-dafb-426e-9d86-1911e6d8d8f7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":140,"end":142,"exact":"推論","quote":"クラウド収益化 | 米国 |\n| 物理AIの普及速度 | 中国 |\n| ロボット量産 | 中国 |\n| エッジ推論端末 | 中国 |\n| 電力・建設動員 | 中国 |\n| 低コスト大量実装 | 中国 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AMD社は、エッジAI向けにRyzen AI Embeddedを拡張し、TOPS（推論性能）やROCm対応などを打ち出している（量産時期も明記）。  \n  これは「クラウドGPUの争奪」だけでなく、工場・現場での推論（低遅延/データ主権）へ計算資源が分散することを前提にした動きだ。\n-","quote_start":236,"quote_end":393,"text_sha256":"a1e408afad5f3dc03645be5f58f8430fc60dfb949d96ea16f993b0731d73bdb7","block_sha256":"a1e408afad5f3dc03645be5f58f8430fc60dfb949d96ea16f993b0731d73bdb7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_b228986a-43e7-4e2b-8b16-758283d367bc/#blk_e8d5c718-7f8f-4f79-b401-6721fd0748a4"},{"id":"occ_a40fe8666f1da5de6982c4a6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_b228986a-43e7-4e2b-8b16-758283d367bc","work_id":"wrk_b193a2ff-ca61-4efc-864e-0778162ac852","block_id":"blk_ecafb851-ea99-4f99-ac7d-40e884d7bfbf","section_id":"sec_9c25f319-3c75-4406-b763-9ecbe1b9b46b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":448,"end":450,"exact":"推論","quote":"利用可能」と明記している。  \n   OpenAIはデータレジデンシー（地域内保存）と、条件付きの地域内GPU推論オプション拡張を示しているが、Googleの“空隙オンプレ”は調達要件が厳しい領域（防衛・重要インフラ等）で差別化になりやすい。\n3. **供給制約（計算・電力・チップ）がB2Bの約束（SLA/継続性","quote_start":393,"quote_end":550,"text_sha256":"2bce8e963acdf4389303f2b2ea0c6bec37a132a8b1c9a83fdc68d8c15d1980af","block_sha256":"2bce8e963acdf4389303f2b2ea0c6bec37a132a8b1c9a83fdc68d8c15d1980af","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_b228986a-43e7-4e2b-8b16-758283d367bc/#blk_ecafb851-ea99-4f99-ac7d-40e884d7bfbf"},{"id":"occ_63ccedc5c777966fcbb0255f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_b228986a-43e7-4e2b-8b16-758283d367bc","work_id":"wrk_b193a2ff-ca61-4efc-864e-0778162ac852","block_id":"blk_ed1ab4c4-096d-4e1e-b733-73dcec2985e1","section_id":"sec_c411ead6-1554-425b-9e21-172ff81663aa","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":133,"end":135,"exact":"推論","quote":"ex AIを提供し、オフライン環境の需要を取りに来ている。  \nOpenAIはデータレジデンシーと地域内GPU推論オプションを拡張しているが、競争上は「完全に切断された環境」への対応が残る。  \nここでOpenAIが現実的に取れるのは、（i）既存クラウドのソブリン/専有環境（例：Azure Local等）との連携","quote_start":78,"quote_end":235,"text_sha256":"e63c76803e2c6e6e5763e43a9a3d0df5710293429f49f4344fff8c2027654829","block_sha256":"e63c76803e2c6e6e5763e43a9a3d0df5710293429f49f4344fff8c2027654829","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_b228986a-43e7-4e2b-8b16-758283d367bc/#blk_ed1ab4c4-096d-4e1e-b733-73dcec2985e1"},{"id":"occ_2ac035345593dfc34d212b23","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_b3e4b97a-1876-4168-a934-44077c4ff1df","work_id":"wrk_5bed81c7-de62-412f-ad44-5b74f4e4f0c5","block_id":"blk_46561cf8-0199-4ba7-bb33-30e4f54b0123","section_id":"sec_62e96747-4d3e-4346-b520-e5fe2069ea12","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":40,"end":42,"exact":"推論","quote":"**HuaweiのAscend 910C**は、Huaweiが開発した高度なAI推論用プロセッサで、主にディープラーニングやAI計算を高速に処理することを目的としています。このプロセッサは、**DaVinciアーキテクチャ**を基盤にしており、特にAIワークロードに特化した性能を発揮","quote_start":0,"quote_end":142,"text_sha256":"d2175614e8f9214315aa2364a54629cd85a1405374262a064262ad4aaead0b21","block_sha256":"d2175614e8f9214315aa2364a54629cd85a1405374262a064262ad4aaead0b21","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_b3e4b97a-1876-4168-a934-44077c4ff1df/#blk_46561cf8-0199-4ba7-bb33-30e4f54b0123"},{"id":"occ_dd7afdb299e642d315c4ff84","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_b3e4b97a-1876-4168-a934-44077c4ff1df","work_id":"wrk_5bed81c7-de62-412f-ad44-5b74f4e4f0c5","block_id":"blk_50a976ae-32c2-4a97-a264-85f4b1364e68","section_id":"sec_9ede2cb5-971b-4f92-b239-f8abf060b6d6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"### \\1. AI推論能力","quote_start":0,"quote_end":14,"text_sha256":"0747da00e06ea4c161e799f8595845c72b0b5dddf225e57cdda581f50e1fea55","block_sha256":"0747da00e06ea4c161e799f8595845c72b0b5dddf225e57cdda581f50e1fea55","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_b3e4b97a-1876-4168-a934-44077c4ff1df/#blk_50a976ae-32c2-4a97-a264-85f4b1364e68"},{"id":"occ_9b6b7e5d8ded4beabb4a83a0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_b3e4b97a-1876-4168-a934-44077c4ff1df","work_id":"wrk_5bed81c7-de62-412f-ad44-5b74f4e4f0c5","block_id":"blk_8378cb1c-7d8d-43a9-959d-071d50df68c6","section_id":"sec_c93a54c3-63a4-4b1e-b0ae-d440b5752071","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":208,"end":210,"exact":"推論","quote":"す。\n   \n   - **NVIDIA**の高性能GPU（特にA100やH100など）は、AIトレーニングや推論タスクで使用されるもので、米国政府はこれらのチップの中国への販売を制限しました。\n   - **AMD**も同様に、中国に対して一部のGPUの供給を制限しています。\n2. **制裁措置の対象** 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粗利率\n2. 推論コストと訓練コストの扱い\n3. 株式報酬を入れた営業利益\n4. クラウド・計算資源契約の長期コミットメント\n5. クラウドパートナー売上のグロス / 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長文脈、慎重な推論、コードベース理解 | ユーザー基盤、統合力、拡張速度 |\n| 収益化の形 | 高単価B2B、開発現場の深い導入 | 個人から企業までの広い導線 |\n| リスク | 利用規模の拡大速度、計算コスト 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       |\n| リスク    | 粗利、クラウド契約、売上定義             | 計算資源、推論コスト、物理AI投資            |","quote_start":558,"quote_end":638,"text_sha256":"990255974146d290e58ad761087dc8fc55bad9b9c48e4bf03b8a4a75a4dc3c9b","block_sha256":"990255974146d290e58ad761087dc8fc55bad9b9c48e4bf03b8a4a75a4dc3c9b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_be83f7eb-e1d1-464a-ab3f-1463976bb30a/#blk_5229d8b8-e665-4ea5-9d82-3262f44c20dd"},{"id":"occ_e0610c441e24a826a1cad114","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_be83f7eb-e1d1-464a-ab3f-1463976bb30a","work_id":"wrk_dcf18d88-5123-4a4f-aa9b-564dc767f048","block_id":"blk_5971ada9-4ea9-46dd-abb9-8bd114d13f33","section_id":"sec_e38fc6f5-8b1a-44a1-9b2e-340763daf5d6","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":80,"end":82,"exact":"推論","quote":"| --- |\n| 売上の質 | API利用、法人契約、個人課金、クラウド経由売上では利益率が異なる |\n| 推論コスト | 使われるほど赤字になるのか、使われるほど利益が積み上がるのかを分ける |\n| 学習コスト | 次世代モデル開発がどれだけ資本を吸うかを示す |\n| 顧客集中 | 大口法人依存が高いほど成長","quote_start":25,"quote_end":182,"text_sha256":"ff2451a7ad9c919b0f634f66959f3fd28eb2b2696926dc8a5153d27e0a600eff","block_sha256":"ff2451a7ad9c919b0f634f66959f3fd28eb2b2696926dc8a5153d27e0a600eff","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_be83f7eb-e1d1-464a-ab3f-1463976bb30a/#blk_5971ada9-4ea9-46dd-abb9-8bd114d13f33"},{"id":"occ_3f0231a78c8c7496de96f71e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_be83f7eb-e1d1-464a-ab3f-1463976bb30a","work_id":"wrk_dcf18d88-5123-4a4f-aa9b-564dc767f048","block_id":"blk_6d98d70d-f335-4582-9396-38413fe34e19","section_id":"sec_16e8a24f-d04b-4796-9e8d-197b74c4daa1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":66,"end":68,"exact":"推論","quote":"de / Claude Enterprise / API利用が伸びる\n→ 高単価B2B売上が増える\n→ 訓練・推論コストを売上で吸収する\n→ 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\nAnthropicはAWSに巨額の支出を約束している。  \nTrainiumはClaudeの訓練・推論を支える重要チップになる。  \nBedrockはClaudeを企業顧客へ届ける流通経路になる。","quote_start":23,"quote_end":127,"text_sha256":"0d78c78dbad7c8257eb4c720266114079293f4c78770d420868f90cd804924cf","block_sha256":"0d78c78dbad7c8257eb4c720266114079293f4c78770d420868f90cd804924cf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6/#blk_167a6a98-0f3e-421b-973d-0d2eee246eaa"},{"id":"occ_497b4562c8543835e8da7aaf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6","work_id":"wrk_92c272f3-ada3-4a0c-815e-d33c175ffbe0","block_id":"blk_4238b755-76db-4312-b2d7-7a3368b55802","section_id":"sec_a8ad0b2e-ae87-45c4-9e26-a16b79b13e93","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":56,"end":58,"exact":"推論","quote":"国から見れば、これは合理的な発想でもある。  \n中国、ロシア、イラン、北朝鮮などに、最先端のサイバー能力や科学推論能力を持つモデルを自由に使わせるわけにはいかない。味方には売り、敵には渡さず、中間国には条件付きで使わせる。その条件を米国が握る。これは、AI覇権を維持するうえで非常に強い構図である。","quote_start":1,"quote_end":150,"text_sha256":"2f58aa5a8164b3c00fd58ed6c3f0522f5c44b6be53f788e319951d40ea091601","block_sha256":"2f58aa5a8164b3c00fd58ed6c3f0522f5c44b6be53f788e319951d40ea091601","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6/#blk_4238b755-76db-4312-b2d7-7a3368b55802"},{"id":"occ_a2903c00aa9289db1e76f545","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6","work_id":"wrk_92c272f3-ada3-4a0c-815e-d33c175ffbe0","block_id":"blk_936a13b7-f276-464f-b1b3-a9f6ad4baa7d","section_id":"sec_2e3d3f4d-e8aa-4b7e-9d63-28a5c23da85c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"最先端モデルの訓練と推論には、巨額のデータセンター投資が必要である。  \nGPU、ASIC、HBM、ネットワーク、冷却、電力、土地、送電、研究者、セキュリティ評価。  \nこれらのコストは膨大である。","quote_start":0,"quote_end":99,"text_sha256":"3e825dcee6ffefc8eef8d4487af1649c2250d07515a7b9316250916753a57371","block_sha256":"3e825dcee6ffefc8eef8d4487af1649c2250d07515a7b9316250916753a57371","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6/#blk_936a13b7-f276-464f-b1b3-a9f6ad4baa7d"},{"id":"occ_f0b9c5779255d311559bbf98","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6","work_id":"wrk_92c272f3-ada3-4a0c-815e-d33c175ffbe0","block_id":"blk_f13bc603-584d-467b-8673-92691f47339f","section_id":"sec_740b94d6-6323-44b4-810d-f00a04305aa2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":139,"end":141,"exact":"推論","quote":"次のモデルで対抗するのは当然だ。しかも、GPT-5.6がGPT-5.5から意味のある改善を持ち、コーディング、推論、エージェント、視覚、フロントエンド生成で強化されるなら、それは単なる小幅更新ではなく、Fable 5への明確な対抗馬になる。","quote_start":84,"quote_end":204,"text_sha256":"0e5eab01cbd2c6a3b501b59685f47ed4e9f7e2b6894229b966131735c4006424","block_sha256":"0e5eab01cbd2c6a3b501b59685f47ed4e9f7e2b6894229b966131735c4006424","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ca87695f-4cf9-40a1-bc61-6a123a71ffe6/#blk_f13bc603-584d-467b-8673-92691f47339f"},{"id":"occ_4108fff9185f44bce5c4fb85","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cf107e9a-1286-4df3-bd1f-adb77c070354","work_id":"wrk_a1c546ea-ef0c-48c4-aa48-b5bb2fd37499","block_id":"blk_24ddee65-2956-4c84-904f-99848a1b251a","section_id":"sec_9618b865-9de0-4496-a301-84d7a631622e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":13,"end":15,"exact":"推論","quote":"こうした失敗は、単なる言語推論の問題ではない。\n物理世界の摩擦、剛性、誤差、部品公差、センサーずれ、制御遅延の問題である。","quote_start":0,"quote_end":61,"text_sha256":"e608baf17ffa47e9bdc8748cd943cc50cfaebbf3d39d73d3622a20d74560ac32","block_sha256":"e608baf17ffa47e9bdc8748cd943cc50cfaebbf3d39d73d3622a20d74560ac32","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cf107e9a-1286-4df3-bd1f-adb77c070354/#blk_24ddee65-2956-4c84-904f-99848a1b251a"},{"id":"occ_010781517d960e27f0c25dae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cf107e9a-1286-4df3-bd1f-adb77c070354","work_id":"wrk_a1c546ea-ef0c-48c4-aa48-b5bb2fd37499","block_id":"blk_51c68756-4c80-405b-9053-bdecc0984348","section_id":"sec_115648db-57b9-4060-a505-e18396c66b85","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":159,"end":161,"exact":"推論","quote":"l として提案されています。WLA-0は2B active parameters、RTX 5090上で40ms推論、RoboTwin2.0 Cleanで92.94%、RMBenchで56.5%成功率と報告されています。([arXiv][2])","quote_start":104,"quote_end":225,"text_sha256":"ec374b4fa9648413a41ea2517cd39649898e1321ea7ed5bc1511a163afd47961","block_sha256":"ec374b4fa9648413a41ea2517cd39649898e1321ea7ed5bc1511a163afd47961","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cf107e9a-1286-4df3-bd1f-adb77c070354/#blk_51c68756-4c80-405b-9053-bdecc0984348"},{"id":"occ_24673eb167b6bd7632d327ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cf107e9a-1286-4df3-bd1f-adb77c070354","work_id":"wrk_a1c546ea-ef0c-48c4-aa48-b5bb2fd37499","block_id":"blk_545f5272-48b2-4de9-9f42-5aa8df25342e","section_id":"sec_a77ddcab-d0f2-40a8-8790-3cd30ae5a6e1","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":98,"end":100,"exact":"推論","quote":"た。画像を見て、言語指示を理解し、ロボットの行動へ変換する。そこに世界モデル的な予測を加えたWAM、さらに言語推論・世界予測・行動生成を統合するWLAが登場し、ロボットが数十分単位の長期タスクをこなす未来が見え始めている。","quote_start":43,"quote_end":154,"text_sha256":"9c06a1c23920e430cbd4630565ac5973da7ed8b510865b457c6d367ecd6d8d1d","block_sha256":"9c06a1c23920e430cbd4630565ac5973da7ed8b510865b457c6d367ecd6d8d1d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cf107e9a-1286-4df3-bd1f-adb77c070354/#blk_545f5272-48b2-4de9-9f42-5aa8df25342e"},{"id":"occ_342ecf8cfd1f4a7047a05606","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cf107e9a-1286-4df3-bd1f-adb77c070354","work_id":"wrk_a1c546ea-ef0c-48c4-aa48-b5bb2fd37499","block_id":"blk_d15a0cd5-7a8e-4694-87dd-6b56aecf9c9f","section_id":"sec_c8e3ca4e-af4e-4488-887a-b928f6f27cba","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"WLAにはWLAの強みがある。\n特に、未知環境への汎化、リアルタイム推論、言語と行動の統合、長期記憶を伴う作業では、統合モデルの価値は大きい。","quote_start":0,"quote_end":71,"text_sha256":"a1bef118947f475436a6060a4b98ed35089d2703489b541d666190f3c9f78448","block_sha256":"a1bef118947f475436a6060a4b98ed35089d2703489b541d666190f3c9f78448","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cf107e9a-1286-4df3-bd1f-adb77c070354/#blk_d15a0cd5-7a8e-4694-87dd-6b56aecf9c9f"},{"id":"occ_4f4c0aecf3577fa93d5678b8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cf107e9a-1286-4df3-bd1f-adb77c070354","work_id":"wrk_a1c546ea-ef0c-48c4-aa48-b5bb2fd37499","block_id":"blk_fb4b919f-923c-485b-83db-a0d6b1c3602f","section_id":"sec_c8e3ca4e-af4e-4488-887a-b928f6f27cba","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":219,"end":221,"exact":"推論","quote":"ション、合成データ、物理検証を支える\n\nJetson / RTX / Thor / Blackwell\nエッジ推論と学習計算を支える\n```","quote_start":164,"quote_end":234,"text_sha256":"047011be904c37a06dbd0c1bdc5413301814788b2e778618c9aa03f1fe72bd87","block_sha256":"047011be904c37a06dbd0c1bdc5413301814788b2e778618c9aa03f1fe72bd87","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cf107e9a-1286-4df3-bd1f-adb77c070354/#blk_fb4b919f-923c-485b-83db-a0d6b1c3602f"},{"id":"occ_feb28ec162439092d4c94115","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_15082d64-8758-4ffe-af4a-c358533e5aef","section_id":"sec_8e9ee249-1e08-407d-84c0-30ebd0f9a016","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"PrefillからDecodeへ渡す明確なboundaryがある。","quote_start":0,"quote_end":33,"text_sha256":"184819b3ae75e3c521ec80c284b0c8103ee2e214babec7c87280dc99fc4f4389","block_sha256":"184819b3ae75e3c521ec80c284b0c8103ee2e214babec7c87280dc99fc4f4389","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04/#blk_15082d64-8758-4ffe-af4a-c358533e5aef"},{"id":"occ_cc235bd9b3f5fbc0e477907b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_1d8e25b7-3959-429e-8552-d3dced695afd","section_id":"sec_9e31432f-1e0c-40d0-a5b0-f4845da18912","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefill、","quote_start":0,"quote_end":8,"text_sha256":"59fafed8aa3a4a569ee2057d7696ecb48e2fe5fa466d19c1c40068e17d9502a4","block_sha256":"59fafed8aa3a4a569ee2057d7696ecb48e2fe5fa466d19c1c40068e17d9502a4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04/#blk_1d8e25b7-3959-429e-8552-d3dced695afd"},{"id":"occ_ecdd8d287a3d952cb37858d1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_3b54c0ef-1d62-461e-90bc-a8f705cc7e32","section_id":"sec_04848aa6-e2bc-4eaa-b204-de4769dc54c5","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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(arXiv)","quote_start":0,"quote_end":83,"text_sha256":"db356bfeb102541f6139e39e000559dac99cb7e4e18c2c29ad593205e1f7161e","block_sha256":"db356bfeb102541f6139e39e000559dac99cb7e4e18c2c29ad593205e1f7161e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04/#blk_3b54c0ef-1d62-461e-90bc-a8f705cc7e32"},{"id":"occ_ecd3866fe83493f6efd10fae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_4f7fe0cc-8043-4d70-a08c-4b54115abe20","section_id":"sec_9e31432f-1e0c-40d0-a5b0-f4845da18912","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":0,"end":6,"exact":"Decode","quote":"Decodeは生成済みKVを参照しながらtokenを逐次生成する。","quote_start":0,"quote_end":33,"text_sha256":"3bcfc8e940a9ba640eb05549a67f78d29ea9516c038c4000f8fd5a20b5b516d1","block_sha256":"3bcfc8e940a9ba640eb05549a67f78d29ea9516c038c4000f8fd5a20b5b516d1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04/#blk_4f7fe0cc-8043-4d70-a08c-4b54115abe20"},{"id":"occ_f7869e3c3b6d1585c759c219","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_52151da7-e3e3-4e25-989b-ce40c21d4907","section_id":"sec_a4251fda-1e08-4105-b8f4-c3db35786593","layer":"body","character_id":null,"count":1,"matched_aliases":["KV 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cacheを、","quote_start":0,"quote_end":23,"text_sha256":"5ca0b78da940f70c32c2203461a6b4c15333773a4ccdbda698e7b1fa8e465a7e","block_sha256":"5ca0b78da940f70c32c2203461a6b4c15333773a4ccdbda698e7b1fa8e465a7e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04/#blk_52151da7-e3e3-4e25-989b-ce40c21d4907"},{"id":"occ_d26d8e01165c6928d23eef1f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_cfe5b9e3-26a7-41f5-bf9e-934074846f04","work_id":"wrk_32710c09-9463-4267-9e64-b41e736195fc","block_id":"blk_5d8d358b-180d-4e82-b548-4bce35345249","section_id":"sec_9e31432f-1e0c-40d0-a5b0-f4845da18912","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":3,"end":10,"exact":"Prefill","quote":"## 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モデルが中間推論を示すため、出力の理解やデバッグがしやすくなります。\n- **推論力の向上**: 複雑なタスクにおいて単純なエンドツーエンドの予測よりも優れたパフォーマンスを発揮します。\n- 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SSDも、ラック直付けだけなら30億〜100億ドル級だが、周辺ストレージ、チェックポイント、推論ログ、動画生成データまで含めるとさらに大きくなる。","quote_start":7,"quote_end":89,"text_sha256":"df8707df40c40e3ba3cfa452a34977f178e6aa2caebf5529ec09a0781e5b7007","block_sha256":"df8707df40c40e3ba3cfa452a34977f178e6aa2caebf5529ec09a0781e5b7007","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_320eaca4-3875-4fbd-8bb9-9caee4fcd9bf"},{"id":"occ_44180d6ad1763f51667d3d37","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_516fbbf4-a902-4292-bf78-e857b295f2ff","section_id":"sec_031a40f9-b818-4747-9239-a72469347a6d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"**絶ノイア:** 推論が増えるほど、HBM、電力、光、MLCC、電源まで広がっていく。","quote_start":0,"quote_end":44,"text_sha256":"9a05204db24468b6de9ee7fc443ec0b4fae87ca7b2d3ba2b292ea2bfdb46c7af","block_sha256":"9a05204db24468b6de9ee7fc443ec0b4fae87ca7b2d3ba2b292ea2bfdb46c7af","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_516fbbf4-a902-4292-bf78-e857b295f2ff"},{"id":"occ_327d6acd5249fc6cbdafd214","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_52d3cabe-681f-455a-90fc-59d0a8dd581a","section_id":"sec_619e1043-0e1c-41c3-a4f5-f0581fbae27c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":57,"end":59,"exact":"推論","quote":"ため、TPU出荷が数百万〜数千万個へ増えるという話をするなら、中心に置くべきはTPU 8i、または8i系の後継推論TPUである。8t中心に語ると、技術的にはかなり不自然になる。","quote_start":2,"quote_end":90,"text_sha256":"bccc8bcc9d24bcf7fdc13bf7a44d87bdd5d7d927cc34b431d8fd69abbaa740de","block_sha256":"bccc8bcc9d24bcf7fdc13bf7a44d87bdd5d7d927cc34b431d8fd69abbaa740de","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_52d3cabe-681f-455a-90fc-59d0a8dd581a"},{"id":"occ_aaf3ee323e5280fc5b6b7f19","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_5db11282-e4ef-4371-b582-a413cf92139f","section_id":"sec_0e18eb64-9933-4275-851c-0225dc9241d4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"TPU 8tは学習用であり、巨大モデルを鍛えるための工場である。TPU 8iは推論用であり、AIエージェント、長文reasoning、MoE、低遅延サービングを支える発電所である。","quote_start":0,"quote_end":90,"text_sha256":"97c89e77b559aeef569154926dc78271783e6670696af3817f2d752878fc4dc4","block_sha256":"97c89e77b559aeef569154926dc78271783e6670696af3817f2d752878fc4dc4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_5db11282-e4ef-4371-b582-a413cf92139f"},{"id":"occ_7659528e1b17feaaf56a5412","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_6286a5f2-d65e-4195-bcf2-19c9b2babcd6","section_id":"sec_619e1043-0e1c-41c3-a4f5-f0581fbae27c","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"学習用クラスタは巨大だが、使われる場所は限られる。一方、推論はユーザー数、トークン数、AIエージェント数、APIリクエスト数に比例して伸びる。検索、広告、Gmail、Workspace、YouTube、Gemini、Cloud API、コーディング、動画生成、","quote_start":0,"quote_end":130,"text_sha256":"6d9419442d53d8b0352b8281419af2611f9a8f11160cc6f9fdb12c98955156f2","block_sha256":"6d9419442d53d8b0352b8281419af2611f9a8f11160cc6f9fdb12c98955156f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_6286a5f2-d65e-4195-bcf2-19c9b2babcd6"},{"id":"occ_7cc02e9406c4326be4c2426f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_6bafd61c-67b5-4614-a622-5e6e360a0d14","section_id":"sec_d66e9049-6559-4ebb-be89-11d94d5da144","layer":"character","character_id":"zetu_noia","count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":15,"end":17,"exact":"推論","quote":"学習チップは巨大な炉だ。でも、推論チップは街の配電網に近い。毎日、毎秒、無数のリクエストを受けて、遅れず、止まらず、安く返し続けなければならない。","quote_start":0,"quote_end":73,"text_sha256":"dc127a7dba524015b3f82e2164f4fed6328c7804b6f33eb226a2c0be2d7713e4","block_sha256":"dc127a7dba524015b3f82e2164f4fed6328c7804b6f33eb226a2c0be2d7713e4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_6bafd61c-67b5-4614-a622-5e6e360a0d14"},{"id":"occ_857e5452d0a62cbccad727a9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_6c365b20-3c40-481c-b121-3edba58ed2c4","section_id":"sec_f1543817-fc0d-4ce1-96f7-c303e55d266a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":67,"end":69,"exact":"推論","quote":"では、GPUやTPUをまとめて「AI計算資源」として見ることが多かった。しかし、生成AIが学習中心の時代から、推論、AIエージェント、長文reasoning、MoEの時代へ移るにつれ、必要なチップの性格は大きく変わった。","quote_start":12,"quote_end":122,"text_sha256":"d02479ed928d8f2e68368161cb8c10136bc5b2361e978153936fabd1b044a680","block_sha256":"d02479ed928d8f2e68368161cb8c10136bc5b2361e978153936fabd1b044a680","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_6c365b20-3c40-481c-b121-3edba58ed2c4"},{"id":"occ_61a2f5fa6330fcbfd9bee4df","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_6d36c9cf-a9d0-4f37-8375-6554fd9a928e","section_id":"sec_d7bf9997-c67b-4e17-966d-bc5c55552344","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論では、このホップ数がかなり効く。特にMoEでは、トークンごとに呼ばれるExpertが変わる。あるトークンはExpert 3へ、別のトークンはExpert 19へ、次はExpert 7へ飛ぶ。通信先が不","quote_start":0,"quote_end":102,"text_sha256":"0e6a4edc0fc3a1ef9afb60e4942124e1ee37f3cbeaac9edb99085b75058271e4","block_sha256":"0e6a4edc0fc3a1ef9afb60e4942124e1ee37f3cbeaac9edb99085b75058271e4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_6d36c9cf-a9d0-4f37-8375-6554fd9a928e"},{"id":"occ_bbfc614e10faa1086905c62a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_6eacffdd-2f25-4dc2-8b33-b90b0bd8820f","section_id":"sec_50b838b6-7406-4343-a644-2f50caa0bc11","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":54,"end":56,"exact":"推論","quote":"そのため、3,500万個という数字は、TPUパッケージそのものではなく、周辺ASIC、チップレット、低HBM推論ASIC、複数年累計、またはGoogle以外のBroadcom関連ASICまで含む広い数字として見る方が安全だ。","quote_start":0,"quote_end":112,"text_sha256":"5bc4ac4d1629c03f3d04a670c024e82deda35417f8222e3b3fd4a92888ca584b","block_sha256":"5bc4ac4d1629c03f3d04a670c024e82deda35417f8222e3b3fd4a92888ca584b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_6eacffdd-2f25-4dc2-8b33-b90b0bd8820f"},{"id":"occ_b56ea7500887c3e0ca935fda","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_8183280c-a656-4a86-a171-8bfc948b8286","section_id":"sec_acac2f8c-188a-41b5-ba8e-397790fdd6f8","layer":"body","character_id":null,"count":3,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論では、モデルの重みだけでなく、ユーザーごとの長い文脈、過去トークンのKVキャッシュ、MoEのExpert呼び出し、同期処理がボトルネックになる。コンテキストが長くなればなるほど、KVキャッシュは膨らむ","quote_start":0,"quote_end":102,"text_sha256":"27d1747d835132b3bcfc2be82ee679e35a103b7588a2d2c8d072163f76680c62","block_sha256":"27d1747d835132b3bcfc2be82ee679e35a103b7588a2d2c8d072163f76680c62","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_8183280c-a656-4a86-a171-8bfc948b8286"},{"id":"occ_fdd678e2f23012bde04b96a7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_90ec3688-3cb5-41fd-a943-e9e17fcf1301","section_id":"sec_619e1043-0e1c-41c3-a4f5-f0581fbae27c","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":67,"end":69,"exact":"推論","quote":"の読み\n  ↓ HBM 216GB/TPU\n8i中心の読み\n  ↓ HBM 288GB/TPU\n差分\n  ↓ 推論需要、HBM、電力、ラック、光、電源部品の見積もりを変える\n```","quote_start":12,"quote_end":102,"text_sha256":"76bae76a78c710d995a4597f96a9a5a73635d731e3307d25b8a2b651100a2bec","block_sha256":"76bae76a78c710d995a4597f96a9a5a73635d731e3307d25b8a2b651100a2bec","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_90ec3688-3cb5-41fd-a943-e9e17fcf1301"},{"id":"occ_88ab677b6392cc797de2e433","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_a861da7a-330b-4161-8d5c-db8c981b558b","section_id":"sec_a1bb252c-122f-46f4-9de3-a34d0110a0ac","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":50,"end":52,"exact":"推論","quote":"AI時代の競争は、もはやモデル性能だけでは決まらない。どれだけ安く、どれだけ大量に、どれだけ低遅延で推論を提供できるかが重要になる。","quote_start":0,"quote_end":66,"text_sha256":"e83b0c1a57af161be5df118d57fdb092a881d00964a4b78e94793f8577620193","block_sha256":"e83b0c1a57af161be5df118d57fdb092a881d00964a4b78e94793f8577620193","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_a861da7a-330b-4161-8d5c-db8c981b558b"},{"id":"occ_ef902e72cc682df65891d31a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_b0d44a4d-8880-4a7a-a006-baa62f24a022","section_id":"sec_f1543817-fc0d-4ce1-96f7-c303e55d266a","layer":"body","character_id":null,"count":3,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":42,"end":44,"exact":"推論","quote":"学習では、巨大モデルを作るために高い演算性能と大規模クラスタ接続が必要になる。一方、推論では、ユーザーのリクエストに対して低遅延で応答し続ける必要がある。特にAIエージェントでは、1回の回答の中で検索、ツール実行、再推論、確認、出力が何度も繰り返される。ここでは単純なピークFLOPSより","quote_start":0,"quote_end":144,"text_sha256":"1e8e26c88e8d2dc8b799feeb1e1252a8e13e61b4049d8ce1d97ae29ac3192f41","block_sha256":"1e8e26c88e8d2dc8b799feeb1e1252a8e13e61b4049d8ce1d97ae29ac3192f41","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_b0d44a4d-8880-4a7a-a006-baa62f24a022"},{"id":"occ_9d57bcde1892d16361b1e86f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_bc00a5a7-3414-4763-9791-030058d1017a","section_id":"sec_acac2f8c-188a-41b5-ba8e-397790fdd6f8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":37,"end":39,"exact":"推論","quote":"そのためTPU 8iは、「より速く計算するチップ」というより、「より多くの推論を、より低遅延で、より安定してさばくチップ」と見るべきだ。","quote_start":0,"quote_end":68,"text_sha256":"3a9e5ee502b11bbb034f1986f58a2b0d778d1da8fcb294d38d74c82b6cb01c14","block_sha256":"3a9e5ee502b11bbb034f1986f58a2b0d778d1da8fcb294d38d74c82b6cb01c14","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_dbe69fb8-f76d-44e6-b111-aa27e548a204/#blk_bc00a5a7-3414-4763-9791-030058d1017a"},{"id":"occ_00784a88104c5c8726b0d396","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_dbe69fb8-f76d-44e6-b111-aa27e548a204","work_id":"wrk_0252d3aa-e602-4a99-abf9-0b9ec2b5796a","block_id":"blk_d00e61fc-d281-4962-83d0-dd8bc1b78112","section_id":"sec_acac2f8c-188a-41b5-ba8e-397790fdd6f8","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":39,"end":41,"exact":"推論","quote":"| 項目 | TPU 8i |\n| --- | ---: |\n| 主用途 | 推論・サービング・reasoning |\n| ネットワーク | Boardfly |\n| HBM容量 | 288GB |\n| HBM帯域 | 8,601GB/s |\n| オンチップSRAM | 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CacheをGPUの外へ出す","quote_start":0,"quote_end":28,"text_sha256":"f81acfae4779070f4e28eb70d04e4de2f3e764aefce1f179e845cab09272f7ec","block_sha256":"f81acfae4779070f4e28eb70d04e4de2f3e764aefce1f179e845cab09272f7ec","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e2c369be-a992-44a9-bbc1-81ed4928e89f/#blk_f8b0cf00-2976-4340-80c7-037f8811174d"},{"id":"occ_b61b3fa1937d6e4f0dfeb7ca","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e4df2a9f-4a34-4f95-8006-7efec2bba610","work_id":"wrk_3ee013c2-2eea-4150-98f7-f1bf51161cc8","block_id":"blk_4c560252-93d3-4f3b-a14a-db922c164ea3","section_id":"sec_0a6e29f5-5541-44f4-95a2-8895cb72618f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":29,"end":31,"exact":"推論","quote":"**1GPUあたりの性能**\nよりも、\n**1MWあたりの推論性能**\n**1ドルの電力あたりのトークン数**\n**1ラックあたりの冷却可能kW**\nが重要になります。","quote_start":0,"quote_end":84,"text_sha256":"93693edd8e5c918f78739f13f8b5de9539c7455e9721fcbba9b98e278243098f","block_sha256":"93693edd8e5c918f78739f13f8b5de9539c7455e9721fcbba9b98e278243098f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e4df2a9f-4a34-4f95-8006-7efec2bba610/#blk_4c560252-93d3-4f3b-a14a-db922c164ea3"},{"id":"occ_0b9697ee257c52360d97e803","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e4df2a9f-4a34-4f95-8006-7efec2bba610","work_id":"wrk_3ee013c2-2eea-4150-98f7-f1bf51161cc8","block_id":"blk_5dcc972c-37c0-4c55-90d8-f7a628ed3d22","section_id":"sec_305970d5-acfb-4e1c-a92e-bec899baa96c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":59,"end":61,"exact":"推論","quote":"Mは、GPU性能を引き出すための必須部品です。\nGPUがどれだけ速くても、メモリ帯域が足りなければ大規模モデル推論や学習は詰まります。","quote_start":4,"quote_end":71,"text_sha256":"db9f0b1373df49d0f8798bcd75237c0f05776bf79627497c4328e5af7da99187","block_sha256":"db9f0b1373df49d0f8798bcd75237c0f05776bf79627497c4328e5af7da99187","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e4df2a9f-4a34-4f95-8006-7efec2bba610/#blk_5dcc972c-37c0-4c55-90d8-f7a628ed3d22"},{"id":"occ_37a7b27c8b6d877ef4573cb0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_06405c03-f7b3-425c-9eb3-0c23569a7442","section_id":"sec_6f333347-dc71-4e80-8190-b3ab55b9960c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"通常の推論では、同じ重みのまま長く考えられる。Loopieも、反復のたびに重みを学習し直すわけではない。","quote_start":0,"quote_end":52,"text_sha256":"73f0571175dd7711985b70186c8fc79616ed77dce0ae092e4570be96d6a63214","block_sha256":"73f0571175dd7711985b70186c8fc79616ed77dce0ae092e4570be96d6a63214","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_06405c03-f7b3-425c-9eb3-0c23569a7442"},{"id":"occ_83ac0bf967d2799edc1e61e6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_120a100a-c0d3-4187-b782-9c9cbcdcf614","section_id":"sec_d3f38071-8b9e-47b0-bc6c-7322c4a87419","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":62,"end":64,"exact":"推論","quote":"einforcement as a Pretraining Objective**は、文章の続きを予測する前に推論を生成させ、その推論が実際の続きを予測する助けになったかを報酬にする。","quote_start":7,"quote_end":99,"text_sha256":"e47f8540cebe43b3085dcf7099a80a42d6f616154f6dca6421e0aeec7186d7c7","block_sha256":"e47f8540cebe43b3085dcf7099a80a42d6f616154f6dca6421e0aeec7186d7c7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_120a100a-c0d3-4187-b782-9c9cbcdcf614"},{"id":"occ_c2e489ec42099ddc982145a6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_258e6636-11b7-4f70-b0d4-4f0d4f2d6ae5","section_id":"sec_8f229c67-6ddf-48ff-9929-0b7d092a0b77","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"を受け取り、より良い結果につながる選択をしやすくなるよう更新される。DeepSeek-R1は、このようなRLを推論能力の開発に使った公開例だ。([Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-R1))","quote_start":16,"quote_end":151,"text_sha256":"d98ec6c1bdcc4efd014c31088d5a90012f0b9448969a60c31f96c72a641b8559","block_sha256":"d98ec6c1bdcc4efd014c31088d5a90012f0b9448969a60c31f96c72a641b8559","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_258e6636-11b7-4f70-b0d4-4f0d4f2d6ae5"},{"id":"occ_257dbf95af295348b5e0fb05","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_260e7686-8594-4fce-8404-377fdd6637c2","section_id":"sec_338a4350-4862-4ad2-b81c-e42db3151871","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":74,"end":76,"exact":"推論","quote":"でもない。良い候補をほとんど作れない、間違いを見分けられない場合には、試行数だけを増やしても効果が小さくなる。推論時計算の研究でも、問題の難しさによって追加計算の効果は異なる。([arXiv](https://arxiv.org/abs/2408.03314))","quote_start":19,"quote_end":150,"text_sha256":"d51597e311cf33f892c985e27215c7eef6ebcff06b9cb93ae4327f6058016d4f","block_sha256":"d51597e311cf33f892c985e27215c7eef6ebcff06b9cb93ae4327f6058016d4f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_260e7686-8594-4fce-8404-377fdd6637c2"},{"id":"occ_58a039a0d936602ee49e9a6b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_2ae96ecf-ce20-44b4-be6d-51c254341293","section_id":"sec_d3f38071-8b9e-47b0-bc6c-7322c4a87419","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"つまり、事前学習が終わった後にだけRLを行うのではなく、**事前学習の目的の中へ、推論の有用性を評価する仕組みを入れる。**","quote_start":0,"quote_end":62,"text_sha256":"39de6e60687ca41b26442b616de89d5d567f05e183ffc7628b0a0ce2c39c451e","block_sha256":"39de6e60687ca41b26442b616de89d5d567f05e183ffc7628b0a0ce2c39c451e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_2ae96ecf-ce20-44b4-be6d-51c254341293"},{"id":"occ_2ac72285de0faf24209d358b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_306154d4-0ad4-4579-8f0a-b25084b9a628","section_id":"sec_f4a00c01-de89-4cd2-8f99-a45619d1a195","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論とは、学習済みのモデルを使って出力を計算することだ。","quote_start":0,"quote_end":28,"text_sha256":"e7fd629ac744ec95b0cc6033886bf7a77b067cee518d549a65b4568a752e9de5","block_sha256":"e7fd629ac744ec95b0cc6033886bf7a77b067cee518d549a65b4568a752e9de5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_306154d4-0ad4-4579-8f0a-b25084b9a628"},{"id":"occ_6b373c1a01b864e27187ee4f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_409a07f4-be7b-42d6-bb9c-45760ca494dc","section_id":"sec_338a4350-4862-4ad2-b81c-e42db3151871","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"さらに、道具、検索、作業用メモリ、追加の推論計算を使えば、モデル単体より多くの仕事ができる。","quote_start":0,"quote_end":46,"text_sha256":"0a689338e791d373ea4567d97abec76d7326cc897975dbc94aedc529be12b8c2","block_sha256":"0a689338e791d373ea4567d97abec76d7326cc897975dbc94aedc529be12b8c2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_409a07f4-be7b-42d6-bb9c-45760ca494dc"},{"id":"occ_576123a548ec016d606defe7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_58c486fb-0990-44d9-baaf-f17879562afe","section_id":"sec_eea25fd4-757e-41cb-ac8d-71c6330219e5","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":62,"end":69,"exact":"KVキャッシュ","quote":"験を無条件・無期限に使えるわけではない。また、再利用する経験データと、新しい重みでは有効でなくなる可能性があるKVキャッシュは別物である。","quote_start":7,"quote_end":76,"text_sha256":"f45ead0f3ce240ca69e7b0a3db5e97c00642011460b7d2a0d85eac82434f38de","block_sha256":"f45ead0f3ce240ca69e7b0a3db5e97c00642011460b7d2a0d85eac82434f38de","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_58c486fb-0990-44d9-baaf-f17879562afe"},{"id":"occ_5ff24475c15b68378d7d4c1c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_5b9587a7-ef20-4fce-9240-17639d3bc07e","section_id":"sec_c5203b22-399f-44e2-ba2d-eec7a4cbf977","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論時に参照先を先読みしやすい性質を利用し、CPU側メモリから読み出す構成も研究されている。","quote_start":0,"quote_end":46,"text_sha256":"d285e22d857ad23fffe74a387694e45f6ff194d8ec819b29be518adf75ef3449","block_sha256":"d285e22d857ad23fffe74a387694e45f6ff194d8ec819b29be518adf75ef3449","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_5b9587a7-ef20-4fce-9240-17639d3bc07e"},{"id":"occ_a24f36b17dab8c05065163db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_5c3dc35c-872d-4216-865a-1de54e930586","section_id":"sec_3d1a7f8f-6e04-4555-b855-a2035f01c073","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":41,"end":43,"exact":"推論","quote":"一方、事前学習の教材にも解説や対話、問題と解答を含められるため、「知識は事前学習、推論は追加学習」と完全に分かれるわけではない。","quote_start":0,"quote_end":64,"text_sha256":"c31b889cf044dbbfb3f5ab6457fedcd17ae906fbbf00c35f6cf06be11eb836a5","block_sha256":"c31b889cf044dbbfb3f5ab6457fedcd17ae906fbbf00c35f6cf06be11eb836a5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_5c3dc35c-872d-4216-865a-1de54e930586"},{"id":"occ_b0033931972292e72a18d2dd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_5d97728f-3388-432e-9d70-8f6516239d3b","section_id":"sec_f4a00c01-de89-4cd2-8f99-a45619d1a195","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":5,"end":7,"exact":"推論","quote":"## 5．推論、文脈、学習――「答えが変わった」だけでは、重みが変わったとは限らない","quote_start":0,"quote_end":42,"text_sha256":"98209302e99265fe9875cd89774ed3f3145a37f7685504f134257516d08fcf8d","block_sha256":"98209302e99265fe9875cd89774ed3f3145a37f7685504f134257516d08fcf8d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_5d97728f-3388-432e-9d70-8f6516239d3b"},{"id":"occ_0057c471d12102d65facaacf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_62fbde88-4c19-41da-9bed-81105af74a78","section_id":"sec_fe96decf-cb58-43fb-94c8-1d5335067e8f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"特に、教師が生徒の既知の履歴へ確率を付ける場合、長文を最初から逐次生成する必要はない。したがって、**推論が増えることと、すべての処理で大きなKVを長時間保持することは同じではない。**([Thinking Machines Lab](https://thinkingmachines.ai/blog/on","quote_start":0,"quote_end":153,"text_sha256":"775b58f5b7bb55859424fd168cd67bbf6b8856363e697814d093cbeffa0a3f80","block_sha256":"775b58f5b7bb55859424fd168cd67bbf6b8856363e697814d093cbeffa0a3f80","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_62fbde88-4c19-41da-9bed-81105af74a78"},{"id":"occ_36691c7dc9c7ea7c39d18bcb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_66fa418f-57a7-4fcc-ba63-839ec700baca","section_id":"sec_edb22b3d-74eb-4fd0-8bf4-6640ce7eb61d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":76,"end":78,"exact":"推論","quote":"計し、同じハードウェア条件での実測学習時間を基準に比較している。厳密に理論FLOPsを一致させた比較ではなく、推論時の効率の体系的な検証は今後の課題としている。([arXiv](https://arxiv.org/html/2607.16051v2))","quote_start":21,"quote_end":147,"text_sha256":"cec51ff852400c6485dbb71677f5f85af9cbd68ed783a836058d4b4055270b95","block_sha256":"cec51ff852400c6485dbb71677f5f85af9cbd68ed783a836058d4b4055270b95","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_66fa418f-57a7-4fcc-ba63-839ec700baca"},{"id":"occ_2e524da03979699afa3e384b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_7e57e2b9-dc90-4b0a-a1fe-4e0c1b774b99","section_id":"sec_80fee48c-7835-46cd-8610-3bef78b0e00f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"この循環が進めば、学習と推論は、互いを呼び出す関係になる。ただし、両者の区別がなくなるのではない。","quote_start":0,"quote_end":49,"text_sha256":"7d3c80b419ac1847fa06e0037c0599444f5cd255f02b67947043a48636b1b370","block_sha256":"7d3c80b419ac1847fa06e0037c0599444f5cd255f02b67947043a48636b1b370","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_7e57e2b9-dc90-4b0a-a1fe-4e0c1b774b99"},{"id":"occ_0d8ea2026a4dba36e14d3688","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_92f23f1b-4a5f-4a18-bcd9-40cedc515a2d","section_id":"sec_fe96decf-cb58-43fb-94c8-1d5335067e8f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"## 31．学習のために推論し、必要なら推論のためにも学習する","quote_start":0,"quote_end":31,"text_sha256":"9f26cd9996074fc64d19410e56dfc11d4d611f331652e34613ab90814b2fbcfa","block_sha256":"9f26cd9996074fc64d19410e56dfc11d4d611f331652e34613ab90814b2fbcfa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_92f23f1b-4a5f-4a18-bcd9-40cedc515a2d"},{"id":"occ_28c49723509af84326f3167b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_973dac4b-6495-4214-b2c5-082bea2bcd7f","section_id":"sec_910f4dc6-0781-4232-8161-aa75bd2b2928","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":14,"end":16,"exact":"推論","quote":"これは重み本体だけの概算だ。推論には文脈の状態や作業領域が必要で、学習には勾配や更新を管理する状態も加わる。","quote_start":0,"quote_end":54,"text_sha256":"52b43f4b07312907fb0476996bef8076f72cb8893d2c56f3e001d339d2021b95","block_sha256":"52b43f4b07312907fb0476996bef8076f72cb8893d2c56f3e001d339d2021b95","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_973dac4b-6495-4214-b2c5-082bea2bcd7f"},{"id":"occ_69253d5f522c6d18e236eaf4","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_97adf40c-dcac-4dc9-8b99-cc8de373ea55","section_id":"sec_f0638cb4-bc28-4d8b-891e-268b51d48d43","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":52,"end":54,"exact":"推論","quote":"DeepSeekMath-V2は検証者を鍛え、PARLは複数エージェントの協調を学ぶ。RLPは事前学習へ推論の評価を入れ、SEALは自分を改善する教材づくりを学ぶ。","quote_start":0,"quote_end":82,"text_sha256":"787134e6b762830ee531c298366d2a798d8fca3bf300a8678675f81ef1b313ca","block_sha256":"787134e6b762830ee531c298366d2a798d8fca3bf300a8678675f81ef1b313ca","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_97adf40c-dcac-4dc9-8b99-cc8de373ea55"},{"id":"occ_166fa6e7119462a37b118d60","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_98a1250b-9f45-48d2-a8e3-fd36bf5d7143","section_id":"sec_d3f38071-8b9e-47b0-bc6c-7322c4a87419","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":32,"end":34,"exact":"推論","quote":"数学問題の正解やコードのテストだけでなく、通常の文章データからも推論を学ばせる方向である。([NVIDIA](https://research.nvidia.com/labs/adlr/RLP/))","quote_start":0,"quote_end":99,"text_sha256":"e018d090e4af84d121283401b22e4370d568c8a00f5299ed46d9e398875f5cfe","block_sha256":"e018d090e4af84d121283401b22e4370d568c8a00f5299ed46d9e398875f5cfe","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_98a1250b-9f45-48d2-a8e3-fd36bf5d7143"},{"id":"occ_77af70108126c89352a7f509","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_c3988a81-ea72-4cf6-a6e1-357009d2abee","section_id":"sec_f4a00c01-de89-4cd2-8f99-a45619d1a195","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":43,"end":50,"exact":"KVキャッシュ","quote":"現在のTransformer型モデルでは、過去の文脈について計算した一部の情報を、**KVキャッシュ**として保持する。これにより、次のトークンを生成するたびに、過去の同じ計算をすべて繰り返さずに済む。([Hugging Face](https://huggingface.co/docs/trans","quote_start":0,"quote_end":150,"text_sha256":"23f3a81bc1c0cb9780b8814ec100c9e33705ec8998652b03e82f2239975d3a3c","block_sha256":"23f3a81bc1c0cb9780b8814ec100c9e33705ec8998652b03e82f2239975d3a3c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_c3988a81-ea72-4cf6-a6e1-357009d2abee"},{"id":"occ_604d5b3c8d4fed9181432605","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_c6b5acbd-447b-494d-8117-550c25943fa2","section_id":"sec_fe96decf-cb58-43fb-94c8-1d5335067e8f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":34,"end":36,"exact":"推論","quote":"この中には、教材生成、試行生成、自己批評、教師の確率計算など、多くの推論が入る。","quote_start":0,"quote_end":40,"text_sha256":"0ed84058e9f606aa7955eb82597f67a0299fe002add70962f18395f7dba07f68","block_sha256":"0ed84058e9f606aa7955eb82597f67a0299fe002add70962f18395f7dba07f68","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_c6b5acbd-447b-494d-8117-550c25943fa2"},{"id":"occ_41b947a3666b5bcb69d6e818","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_da2e61de-9636-4a9f-95f5-4c08fcb3545e","section_id":"sec_1e3af1ac-0360-4c0d-9900-4af2a17d5915","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":20,"end":22,"exact":"推論","quote":"## 第Ⅴ部　TTTが成功すれば、学習と推論の関係はさらに変わる","quote_start":0,"quote_end":32,"text_sha256":"b742e0eba3b7044d49092f7c9c700103507a7f73a49a732a410c29b6ca3433a9","block_sha256":"b742e0eba3b7044d49092f7c9c700103507a7f73a49a732a410c29b6ca3433a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_da2e61de-9636-4a9f-95f5-4c08fcb3545e"},{"id":"occ_35936d222af8e073f032229d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_e5e24c6f-5887-4f16-b03e-081da5340883","work_id":"wrk_470cca01-eadf-46c3-bd50-cd79e3b1e197","block_id":"blk_e0df8c13-aefb-404f-8c4c-28d1d58c1cf9","section_id":"sec_d3f38071-8b9e-47b0-bc6c-7322c4a87419","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"### 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AI](https://ai.meta.com/blog/introducing-muse-s","quote_start":0,"quote_end":120,"text_sha256":"d67fafddd42cd898a6403e51f27345e62a1826821783370284909a6b0bdbc6a0","block_sha256":"d67fafddd42cd898a6403e51f27345e62a1826821783370284909a6b0bdbc6a0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_e5e24c6f-5887-4f16-b03e-081da5340883/#blk_f16e9837-e000-48a0-ad6f-a71201025ab4"},{"id":"occ_89f5a828f06bb19d3c7f4a61","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_eaa7004e-c9e4-4bc4-9718-2e518decec70","work_id":"wrk_dfcbc670-103a-4931-8d23-d5f51671c61b","block_id":"blk_53471b6c-2ccc-4b17-ba0e-e101d494dc0e","section_id":"sec_1afdbaf1-5786-4caa-9bb7-1a11027e856f","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":34,"end":43,"exact":"Inference","quote":"OpenAIは2026年6月、Broadcomと共同開発した初の自社Inference 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リアルタイム制御\n- 安全機能\n- 無線通信\n- 長期供給\n- 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chip","quote_start":0,"quote_end":24,"text_sha256":"5425c76addccc93596949757a0f95cd344b3c7e0dee2a982898785beedba790e","block_sha256":"5425c76addccc93596949757a0f95cd344b3c7e0dee2a982898785beedba790e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1/#blk_c719231e-cd66-441d-b47c-e1a6cbc0c8db"},{"id":"occ_9809551871f64ff09d154b68","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1","work_id":"wrk_991ce980-1208-4f4b-8294-25a23882a284","block_id":"blk_db71697e-1286-444d-aa5f-b841fb1a3b02","section_id":"sec_7d7ed8a1-4f6e-4161-8515-7db7713c6748","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":33,"end":42,"exact":"Inference","quote":"## 27．だから最初のxAI Custom AIDC ChipはInferenceが自然","quote_start":0,"quote_end":45,"text_sha256":"797c9e333c6ebc0c91fe890c3cc6a3a6cd88ae985152d224b6cb9898b1ece6cf","block_sha256":"797c9e333c6ebc0c91fe890c3cc6a3a6cd88ae985152d224b6cb9898b1ece6cf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1/#blk_db71697e-1286-444d-aa5f-b841fb1a3b02"},{"id":"occ_ecc727943f19aae3c5abb2ae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1","work_id":"wrk_991ce980-1208-4f4b-8294-25a23882a284","block_id":"blk_dccec31e-54df-4054-8528-2c265b6bb353","section_id":"sec_185ea470-06c0-4099-a181-c261b030795f","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":18,"end":27,"exact":"Inference","quote":"AI5、AI6、それ以降のchipはInferenceに優れ、Trainingにも十分使える","quote_start":0,"quote_end":46,"text_sha256":"fd2bd27297a8363b2b0aa37385a7884cf92dbcdbc8f89e09b5367e5611a87329","block_sha256":"fd2bd27297a8363b2b0aa37385a7884cf92dbcdbc8f89e09b5367e5611a87329","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1/#blk_dccec31e-54df-4054-8528-2c265b6bb353"},{"id":"occ_fc2e5cb24f98092394b1aa92","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ee871b65-2665-47a6-9e3d-f1a3b67b1de1","work_id":"wrk_991ce980-1208-4f4b-8294-25a23882a284","block_id":"blk_f520cb5a-aa2f-4bc9-9180-5c22a87323e9","section_id":"sec_5755b899-5170-423b-9e51-5d0674796557","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":7,"end":16,"exact":"inference","quote":"Custom 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Memory Poolは汎用HBM代替ではなく、KVキャッシュ・共有DRAM・容量階層として普及\n* NVLink、UALink、Ethernet系Scale-upが併存","quote_start":98,"quote_end":214,"text_sha256":"b8670b0306ed36ddaa62168c811d4d6034a45d8b2d4857b1afe8c33aa6dba6ce","block_sha256":"b8670b0306ed36ddaa62168c811d4d6034a45d8b2d4857b1afe8c33aa6dba6ce","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_eed892a0-f77a-496a-bd60-343ac50f5809/#blk_43218f13-0a58-41ad-a822-5b63ce10c4eb"},{"id":"occ_242ee3c8d30d0224b166b206","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_eed892a0-f77a-496a-bd60-343ac50f5809","work_id":"wrk_6bbf0c3f-2903-4e16-86da-9b8b9eae06c6","block_id":"blk_94eb7b74-a2ca-40ba-8310-55da5e875d4a","section_id":"sec_1c532a72-3a38-49b1-897f-315622f00b2e","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":31,"end":38,"exact":"KVキャッシュ","quote":"なお、NVIDIAのBlueField-4 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Poolとは異なりますが、外部の共有コンテキストメモリを使う方向性では近いものです。([NVI","quote_start":0,"quote_end":138,"text_sha256":"d56c18e6c3513337fa3df37b62a1208ddab5142eb31cb9e0fe4a0c73f0fc5c8a","block_sha256":"d56c18e6c3513337fa3df37b62a1208ddab5142eb31cb9e0fe4a0c73f0fc5c8a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_eed892a0-f77a-496a-bd60-343ac50f5809/#blk_94eb7b74-a2ca-40ba-8310-55da5e875d4a"},{"id":"occ_a5de1091ee6002985885be70","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_eed892a0-f77a-496a-bd60-343ac50f5809","work_id":"wrk_6bbf0c3f-2903-4e16-86da-9b8b9eae06c6","block_id":"blk_e95cbb9b-fd72-4765-88f6-145ac0a4c863","section_id":"sec_eeaecdcb-9a24-490a-84f4-7059f2e2360a","layer":"code","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":275,"end":282,"exact":"KVキャッシュ","quote":"・データセンター間を接続\n\nMemory Pool\n  └─ 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Pool\n＝大容量DRAM、KVキャッシュ、共有メモリ、補助階層\n```","quote_start":5,"quote_end":82,"text_sha256":"c812583549fd257df3c5c6fcf83edba877475b6be4a9bd93061cc8da403da713","block_sha256":"c812583549fd257df3c5c6fcf83edba877475b6be4a9bd93061cc8da403da713","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_eed892a0-f77a-496a-bd60-343ac50f5809/#blk_f0160094-7c0f-496f-abba-2e071c72b84a"},{"id":"occ_d59423a1820aa11132ebe2a8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f13a5f0d-0c68-4d12-9656-86661b1e65d8","work_id":"wrk_e9e75464-bed3-4605-8239-b70c2bfeeaf0","block_id":"blk_540796bb-8421-4f1a-889a-ad330d250365","section_id":"sec_29445ee4-88d2-418f-b724-fb99f159fa1a","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":61,"end":68,"exact":"KVキャッシュ","quote":"待できるのは、Data 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8iは大規模学習よりも、推論、サンプリング、MoE、長文脈処理を意識した製品であり、オンチップSRAM、Collectives Acceleration Engine、Boardflyを組み合わせている。([Google 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TPU超\n- 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Boardflyは推論向けに距離を縮める","quote_start":0,"quote_end":23,"text_sha256":"b241a1ff94647922dd5b6ac1bc3662973136a8658f0f7d4d78f1f74e8c1c1763","block_sha256":"b241a1ff94647922dd5b6ac1bc3662973136a8658f0f7d4d78f1f74e8c1c1763","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f13a5f0d-0c68-4d12-9656-86661b1e65d8/#blk_d532998e-0366-4f71-b402-0db02820fa66"},{"id":"occ_0b19ba060e5cef1e36d45986","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f13a5f0d-0c68-4d12-9656-86661b1e65d8","work_id":"wrk_e9e75464-bed3-4605-8239-b70c2bfeeaf0","block_id":"blk_f850d0c2-5096-48d3-9233-1488a9505279","section_id":"sec_f0fcea3d-fd5a-4073-9d00-722489d62553","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"Googleは、学習向けには3Dトーラス、推論・MoE向けにはBoardflyと、ワークロードに応じてscale-up網そのものを分けた。","quote_start":0,"quote_end":69,"text_sha256":"2f634673951e9ab352bda3200d9dc74e87c2e39a5ecf3333538150bb5d0b8006","block_sha256":"2f634673951e9ab352bda3200d9dc74e87c2e39a5ecf3333538150bb5d0b8006","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f13a5f0d-0c68-4d12-9656-86661b1e65d8/#blk_f850d0c2-5096-48d3-9233-1488a9505279"},{"id":"occ_2a7dfc0421459e99bd0fd745","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_0c37d8bc-6757-47a3-bf0c-e5cdc34b193a","section_id":"sec_873fc1e8-7005-4e5b-9e44-bec7f5043b7a","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":108,"end":110,"exact":"推論","quote":"デルを叩くだけでなく、複数モデル・複数エージェントを**動的に組み合わせ**て動かす負荷が増えます。また訓練と推論の境界が曖昧になり（オンライン学習や継続学習の普及）、常時学習を伴うサービスが増えるでしょう。結果として、**データセンターはより多様な処理を同時並行で捌く**ことが要求されます。GPUクラスタも、従","quote_start":53,"quote_end":210,"text_sha256":"ce2ce13a1b91c8294c7e6035a471e94c2ce1f7734ccfa9af9808d3ec9fbfe7f9","block_sha256":"ce2ce13a1b91c8294c7e6035a471e94c2ce1f7734ccfa9af9808d3ec9fbfe7f9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_0c37d8bc-6757-47a3-bf0c-e5cdc34b193a"},{"id":"occ_f2039d32c7f7fd87c334ce23","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_147bbddd-d40c-4a25-a9bf-aad27a0c077e","section_id":"sec_b7cd6113-b8fb-48e8-8dc3-a81bd43a8e7e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":414,"end":416,"exact":"推論","quote":"96コア・12チャネルDDR5**を実装し前世代比でメモリ帯域を75%向上させています。これは生成AIの学習/推論で大量のデータをGPUに送り込む役割のCPUとして理想的です。またNVIDIAのGrace 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V2コアを2ダイ構成でまとめあげ、**900GB","quote_start":359,"quote_end":516,"text_sha256":"14ea7ce2ebc355c60ae9337fe8a5de2f090fd2e008a51fddda6e7523f97219f2","block_sha256":"14ea7ce2ebc355c60ae9337fe8a5de2f090fd2e008a51fddda6e7523f97219f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_147bbddd-d40c-4a25-a9bf-aad27a0c077e"},{"id":"occ_b23f636668c58d61e461016f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_195d13e9-d136-4cca-aee1-8dbba24d89b0","section_id":"sec_f67f5473-ad15-41a7-adfd-ee89a41cbcb9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":83,"end":85,"exact":"推論","quote":"代で2025年比**で**10倍超**の性能拡大を達成する計画であり、これらは大規模AIモデルのトレーニング・推論を支えるデータセンターの中核となるでしょう。裏を返せば、これだけの性能向上を受け止めるインフラ整備（電力・冷却・ネットワーク）が不可欠であり、後述するように電力要件はラック当たり数百kWからメガワット","quote_start":28,"quote_end":185,"text_sha256":"b2fdfaac324caa648b3b3173debdb46be161e8613706192b5cc8e2f4c3a46bc6","block_sha256":"b2fdfaac324caa648b3b3173debdb46be161e8613706192b5cc8e2f4c3a46bc6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_195d13e9-d136-4cca-aee1-8dbba24d89b0"},{"id":"occ_ff09d4b448c94ce0fa8e1d94","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_2c9fa3c2-2b48-4da0-81dc-c876e882d20c","section_id":"sec_873fc1e8-7005-4e5b-9e44-bec7f5043b7a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":125,"end":127,"exact":"推論","quote":"模のHPC需要を喚起し始めています。またAI×クリエイティブ（画像・動画生成）では、膨大なマルチモーダルモデル推論がオンラインサービス上で行われ、YouTubeなどのトラフィックに匹敵する負荷になる可能性もあります。最近話題のAIエージェントゲーム（仮想社会シミュレーション）では、多数のLLMエージェントが同時稼","quote_start":70,"quote_end":227,"text_sha256":"d6dc7b27aea2754ef6702cc8e69f8d22985d5d1fef6569f53643285852d3858d","block_sha256":"d6dc7b27aea2754ef6702cc8e69f8d22985d5d1fef6569f53643285852d3858d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_2c9fa3c2-2b48-4da0-81dc-c876e882d20c"},{"id":"occ_5fe3631f036d431d3f45c529","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_4adf564c-0889-43f0-ab63-f77714630bfb","section_id":"sec_8c6676b7-3b3a-4edd-a798-077cab41f4ea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":167,"end":169,"exact":"推論","quote":"仞）など多数の企業がAIチップを開発しています。BaiduのKunlun 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PFLOPS（FP4精度）とされています。H100と比べ約1.5倍の推論性能向上が見込まれ、特に4ビット精度の演算性能が強化されます。Blackwell世代のGPU（例えばB100/B200といった型番が想定されます）は最大192GBのHBMメモリ搭載でしたが、**Bla","quote_start":44,"quote_end":201,"text_sha256":"21879378f962262dec7f667db51163c9dade541c64d7658a02927e45d5a1c1e5","block_sha256":"21879378f962262dec7f667db51163c9dade541c64d7658a02927e45d5a1c1e5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_b2971450-c35e-412d-90b7-bbd9f5fb4af2"},{"id":"occ_d8cc8d853d011c04d1d11975","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_c192351d-b2ad-4e53-9065-2162321d40cf","section_id":"sec_f67f5473-ad15-41a7-adfd-ee89a41cbcb9","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":114,"end":116,"exact":"推論","quote":"AI演算性能向上と大容量HBMメモリ搭載を推進しています。その背景には、生成AIモデルの巨大化と高精度・高速な推論需要があります。特に4ビット・8ビットといった低精度演算での性能（推論性能）強化、マルチチップ/マルチノードを前提とした大規模インターコネクト（NVLinkやSwitchによるクラスタリング）、CPU","quote_start":59,"quote_end":216,"text_sha256":"26174b6648dfc284b221da4d7f97ab44040418799b39532154fb7aa994dc9a65","block_sha256":"26174b6648dfc284b221da4d7f97ab44040418799b39532154fb7aa994dc9a65","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_c192351d-b2ad-4e53-9065-2162321d40cf"},{"id":"occ_4e2e78930202e39cd41bcc77","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_d8b8361d-96d9-4953-bc4c-a8aed7fd9961","section_id":"sec_b9721051-b457-47bd-9149-6110a35cbcea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":109,"end":111,"exact":"推論","quote":"えて新たな負荷を生んでいます。総括すると、生成AIブームはアルゴリズム効率向上の努力を促しつつも、ネット全体の推論・学習トラフィック増大により**計算資源への需要曲線は依然鋭角的**です。OpenAIのSam Altman氏は\\*\\*「2025年末までに100万GPUを稼働させ、それでも足りないので将来的には1億","quote_start":54,"quote_end":211,"text_sha256":"147b4ed686a6ebc19ad69cf35f5584bd6b9ce93ad21fb49e6dc1840d71b4a554","block_sha256":"147b4ed686a6ebc19ad69cf35f5584bd6b9ce93ad21fb49e6dc1840d71b4a554","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_d8b8361d-96d9-4953-bc4c-a8aed7fd9961"},{"id":"occ_fb3b1bbfd79021c8a345cc53","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_de53787c-b104-4c53-ab8a-5eafda7be6f4","section_id":"sec_b9721051-b457-47bd-9149-6110a35cbcea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":149,"end":151,"exact":"推論","quote":"た「小さく賢くデータさえ増やせば良い」という楽観に対する反論でもあります。実際にはモデル開発競争が激化し、**推論需要も爆発**しているため、研究者・企業はいずれにせよ**前例のない規模の計算設備**を求め始めています。","quote_start":94,"quote_end":204,"text_sha256":"a8bb79c905e90c0934d0e948cf3e40ae40fc6003e5232221a8592ec581cbf46a","block_sha256":"a8bb79c905e90c0934d0e948cf3e40ae40fc6003e5232221a8592ec581cbf46a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_de53787c-b104-4c53-ab8a-5eafda7be6f4"},{"id":"occ_e053037e6b79c1afde7f071a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_e6810c4c-c71e-42ee-82e2-b80aef2272cb","section_id":"sec_873fc1e8-7005-4e5b-9e44-bec7f5043b7a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":144,"end":146,"exact":"推論","quote":"ance GPU (MIG)を提供し、ある程度小さいジョブを同居させ効率を上げています。また需要ピークに合わせ推論専用インスタンス**と**訓練ジョブ**をダイナミックに切替える運用も模索されています。しかしAI応用が広がるスピードは速く、**需要予測を上回る負荷**が発生する事例もしばしば報告されています（Ch","quote_start":89,"quote_end":246,"text_sha256":"10677f33d2cb3b9ce9c13f01ab26d9d31208db9f1d76b3e40bd22bf452f88d55","block_sha256":"10677f33d2cb3b9ce9c13f01ab26d9d31208db9f1d76b3e40bd22bf452f88d55","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_e6810c4c-c71e-42ee-82e2-b80aef2272cb"},{"id":"occ_4d5378c223418d1bef4b8afa","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_e9848a4e-95b3-4c3a-909a-90a9718598df","section_id":"sec_b9721051-b457-47bd-9149-6110a35cbcea","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":262,"end":264,"exact":"推論","quote":"散モデルを大量に動かせばその計算コストは無視できません。合成データを何億件も生成するなら、それ自体が新たな**推論需要**としてデータセンターを占有することになります。","quote_start":207,"quote_end":291,"text_sha256":"89e85ea4fda0830ba388c63b9ae1edd9a6696b411666006b0c9f6e8aad15cc4f","block_sha256":"89e85ea4fda0830ba388c63b9ae1edd9a6696b411666006b0c9f6e8aad15cc4f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_e9848a4e-95b3-4c3a-909a-90a9718598df"},{"id":"occ_2b86c3a8ac8ee4a7dfd7e3ef","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_f1530434-6385-421f-b4dd-4fa92022b8ee","section_id":"sec_6c097663-b854-43b6-8790-2ae1c15322bd","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":51,"end":53,"exact":"推論","quote":"まとめれば、**HBM3E/HBM4の投入**でGPUのメモリ帯域・容量が飛躍し、超巨大モデルの学習・推論が可能となります。一方で**DDR5/6の高性能化**によりCPU側もボトルネックを減らし、GPUクラスタ全体の効率を底上げします。将来的には光技術や新素材メモリも視野に、メモリ技術はAI時代のニーズ","quote_start":0,"quote_end":153,"text_sha256":"fb43348fc4050b13d5def6c572093a2ab528bcf4f0bf6fe1eb02bd789b1855a6","block_sha256":"fb43348fc4050b13d5def6c572093a2ab528bcf4f0bf6fe1eb02bd789b1855a6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_f1530434-6385-421f-b4dd-4fa92022b8ee"},{"id":"occ_4452808fc464e8c50f8edd6f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f17ef624-e9fd-49a4-b061-966093172de4","work_id":"wrk_168418fa-2b60-4053-acfe-b86f6898d033","block_id":"blk_f1a5bd68-f35d-47a4-aa31-55b073aa3f61","section_id":"sec_b7cd6113-b8fb-48e8-8dc3-a81bd43a8e7e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":114,"end":116,"exact":"推論","quote":"では主要クラウドがArm採用を先導することでエコシステムが成熟しつつあります。ArmベースサーバーCPUはAI推論など**スループット重視のタスクで性能/Wattに優れる**ケースが多く、また**高いメモリ帯域やコア数でGPUのデータ待ちを減らす**ことから、生成AIデータセンターにおける採用が今後さらに加速する","quote_start":59,"quote_end":216,"text_sha256":"6b3bfef3f74e5f3eef35a0b96f222a2abcf199281e87f7ee2f81b75a1521d395","block_sha256":"6b3bfef3f74e5f3eef35a0b96f222a2abcf199281e87f7ee2f81b75a1521d395","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f17ef624-e9fd-49a4-b061-966093172de4/#blk_f1a5bd68-f35d-47a4-aa31-55b073aa3f61"},{"id":"occ_f925bed2789182b5dd6dfa10","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_024c8b70-f744-4c69-8618-5aca520539cc","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefill設備：多い\nDecode設備 ：少ない\n```","quote_start":0,"quote_end":34,"text_sha256":"1b9326e30090ea67c5bd1e33f3ecc99e185362e0898c7a1840e059926914ac6e","block_sha256":"1b9326e30090ea67c5bd1e33f3ecc99e185362e0898c7a1840e059926914ac6e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_024c8b70-f744-4c69-8618-5aca520539cc"},{"id":"occ_ea89ba455b67b7563f0c95ec","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_0303363a-7c9c-4fb7-ba1a-f536d8d31ce3","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":39,"end":46,"exact":"Prefill","quote":"```\n現実的な構成\n\nEncoder\nGPU／専用ASIC\n      ↓\nPrefill\nTrainium／GPU\n      ↓\nKV転送\n      ↓\nDecode\nCerebras WSE\n```","quote_start":0,"quote_end":104,"text_sha256":"03ab19f72b15a50d79270801e31c9f3191f7ee66b7839cf78adf42e0871b0570","block_sha256":"03ab19f72b15a50d79270801e31c9f3191f7ee66b7839cf78adf42e0871b0570","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_0303363a-7c9c-4fb7-ba1a-f536d8d31ce3"},{"id":"occ_6af9696b44b2a5fc49cd08f1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_0357b961-6b53-487d-b753-cef65a874f97","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"code","character_id":null,"count":4,"matched_aliases":["Decode","Prefill","推論"],"evidence":{"text_basis":"markdown","start":58,"end":65,"exact":"Prefill","quote":"\n長期の夢\n\n近接HBM\n＋光I/O\n＋複数WSE\n＋21PB/sの分散SRAM\n\n\n現在の現実的な勝ち筋\n\nPrefill／Decode分離\n＋低遅延Decode\n＋OpenAI・AWS向け推論容量\n```","quote_start":3,"quote_end":107,"text_sha256":"141046abe9eb0e1e3524d3c6f2da42ef1d30b1808ac6bf20061207879ec4819d","block_sha256":"141046abe9eb0e1e3524d3c6f2da42ef1d30b1808ac6bf20061207879ec4819d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_0357b961-6b53-487d-b753-cef65a874f97"},{"id":"occ_2e6a8d9be1e912895cd0067b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_04625869-6c33-41d5-8ebf-1d5fa1e15114","section_id":"sec_0875f100-4397-4df8-8237-7c7ac7bc5c36","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":30,"end":37,"exact":"Prefill","quote":"```\nEncode\nGPU・画像ASIC\n      ↓\nPrefill\nTrainium・GPU・別のASIC\n      ↓\nKV転送\n      ↓\nDecode\nCerebras WSE\n```","quote_start":0,"quote_end":102,"text_sha256":"2b999194d9bc83e9af6bf99b05d7b119d3fa604018448f9ffc40e0705bbd7251","block_sha256":"2b999194d9bc83e9af6bf99b05d7b119d3fa604018448f9ffc40e0705bbd7251","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_04625869-6c33-41d5-8ebf-1d5fa1e15114"},{"id":"occ_56150350a83c9efa12ccc3f0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_06010a28-8f38-495d-91b8-368dca42870a","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"Decode","quote":"> **Decode速度へどれだけの追加料金を付けられるか、同時利用者を処理しながら低遅延を維持できるか、そしてその高付加価値を粗利率とキャッシュフローへ変換できるか。**","quote_start":0,"quote_end":86,"text_sha256":"3a01cf4b6516f268344ccedc84ebacc069905f1c342adaa75940cd25eb1bc68b","block_sha256":"3a01cf4b6516f268344ccedc84ebacc069905f1c342adaa75940cd25eb1bc68b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_06010a28-8f38-495d-91b8-368dca42870a"},{"id":"occ_d171b47a3c8a5017816c60bf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_06f8b2ba-e683-4169-bb3b-57921c0a1623","section_id":"sec_0fd41a36-e7bc-44f6-aee0-9b2be9d051aa","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":46,"end":53,"exact":"Prefill","quote":"- 巨大モデルの重みを近接メモリーへ常駐\n- ホットな重みや活性値を分散SRAMへ配置\n- Prefill側からKVキャッシュを光で転送\n- 複数WSEを光ファブリックで接続\n- Weight Streamingを低速な外部DDRではなく近接HBMから実行\n- 長文脈やMoEでも高いDecode速度を維持","quote_start":0,"quote_end":153,"text_sha256":"74bc9a2cb707c960e0ef80ba1e8f636ab3c3a0bd6ad3fa2216b25c76c119e776","block_sha256":"74bc9a2cb707c960e0ef80ba1e8f636ab3c3a0bd6ad3fa2216b25c76c119e776","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_06f8b2ba-e683-4169-bb3b-57921c0a1623"},{"id":"occ_cc632802a89f217d0423ebdb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_0d43843c-3c08-46e5-92dc-bfd4923e4762","section_id":"sec_23706c9b-77f9-4da7-b3d4-f4745a46b974","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","KVキャッシュ"],"evidence":{"text_basis":"markdown","start":13,"end":20,"exact":"KVキャッシュ","quote":"現在のI/Oでは、長文脈のKVキャッシュ転送だけで数百ミリ秒かかり、Cerebrasによる高速Decodeの利点を打ち消す可能性がある。","quote_start":0,"quote_end":68,"text_sha256":"318c1df7fd7667018645e146851f18ccbd64ed603dfa84c61bf99149e651b04b","block_sha256":"318c1df7fd7667018645e146851f18ccbd64ed603dfa84c61bf99149e651b04b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_0d43843c-3c08-46e5-92dc-bfd4923e4762"},{"id":"occ_e52d14ca87f086ced6509580","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_120cedfa-2f28-42e8-acda-3a4bc8b87321","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"**推論が工程ごとに分離されていく時代に、Decodeという最も遅延価値の高い領域を占有することなのである。**","quote_start":0,"quote_end":56,"text_sha256":"e683dc7876ef2d833e30dbc87878c2b5b98250e7c30dcf8951b109dbc3b313d4","block_sha256":"e683dc7876ef2d833e30dbc87878c2b5b98250e7c30dcf8951b109dbc3b313d4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_120cedfa-2f28-42e8-acda-3a4bc8b87321"},{"id":"occ_9d82c0163fc4a00dd20973cb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_181c242d-5617-4e9e-8a9c-1253d5ef77d3","section_id":"sec_be8484e9-3419-494b-b1dc-280c7cdfb30e","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"SGLangは、複数回の生成、RAG、マルチターン会話、AIエージェント、構造化出力を効率化する推論基盤である。","quote_start":0,"quote_end":56,"text_sha256":"e2e795253bc62b884f2d8c9f2771d682db8ba4e7ae44bd28e442b357174edc1e","block_sha256":"e2e795253bc62b884f2d8c9f2771d682db8ba4e7ae44bd28e442b357174edc1e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_181c242d-5617-4e9e-8a9c-1253d5ef77d3"},{"id":"occ_601d0c3aa72ca4ae8871228e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_1ad50b00-0cd6-431a-80e2-3de07310628c","section_id":"sec_5d4aa299-3fa1-4d89-83e4-6c878b1c3d82","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":22,"end":24,"exact":"推論","quote":"それでも、巨大な契約が成立したことは、低遅延推論に対して実際に大きな支払い需要があることを示している。","quote_start":0,"quote_end":51,"text_sha256":"a81f4c6924dda509da24ce11a1669868aa67fb0ca1fd955729fc2f2403ec5cd2","block_sha256":"a81f4c6924dda509da24ce11a1669868aa67fb0ca1fd955729fc2f2403ec5cd2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_1ad50b00-0cd6-431a-80e2-3de07310628c"},{"id":"occ_2d732364bd8d12a86f55c1c3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_1cebdd4b-6493-406c-85a4-e9a7b1b7b598","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":6,"end":13,"exact":"Prefill","quote":"したがって、Prefill／Decode分離は常に優れているわけではない。短い入力、短い出力、低負荷では、同じGPU上で両方を実行した方が速い場合がある。研究でも、厳しいTTFTを優先する場合は統合型、安定したTPOTを重視す","quote_start":0,"quote_end":113,"text_sha256":"266f2608514c07c5cdeaf41d3926688210c188f6d64b90957813f3577fd325e6","block_sha256":"266f2608514c07c5cdeaf41d3926688210c188f6d64b90957813f3577fd325e6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_1cebdd4b-6493-406c-85a4-e9a7b1b7b598"},{"id":"occ_83e83eb2f63f908752f1fa11","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_1fe72252-2457-4a44-ae6d-a3e4e9427f20","section_id":"sec_210a9fa7-3018-4cba-a47f-11389c648f8a","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":19,"end":26,"exact":"KVキャッシュ","quote":"第2階層が加われば、巨大モデルの重みやKVキャッシュを外部MemoryXから毎回送り直すのではなく、近接メモリーへ常駐させ、必要な部分だけをWSEの分散SRAMへ供給できる。","quote_start":0,"quote_end":87,"text_sha256":"447881e7d99e419df79140739035c056a1c1f908eb9af695eb010596a4b952dd","block_sha256":"447881e7d99e419df79140739035c056a1c1f908eb9af695eb010596a4b952dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_1fe72252-2457-4a44-ae6d-a3e4e9427f20"},{"id":"occ_8a34906c95abe8d286357235","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_2a4c2367-73a3-4a23-9eaa-b36ac9f7fda2","section_id":"sec_29c83f03-75c2-4de7-a777-58eca34e2120","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":12,"end":14,"exact":"推論","quote":"Cerebrasは、AI推論全体を1種類のチップで置き換えようとしているのではない。","quote_start":0,"quote_end":42,"text_sha256":"5ba817c41c5d7cb85bd51e1fe776f52d37d5996e4dbd478364723befe0d30a73","block_sha256":"5ba817c41c5d7cb85bd51e1fe776f52d37d5996e4dbd478364723befe0d30a73","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_2a4c2367-73a3-4a23-9eaa-b36ac9f7fda2"},{"id":"occ_55a4c509913d498067187ca6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_2a9ad754-71a1-4480-ad16-faf9dc1de82b","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":3,"end":9,"exact":"Decode","quote":"## Decodeとは何か","quote_start":0,"quote_end":13,"text_sha256":"464a86bf4a3f0f784cc64a1533f95ae9ebd0246c0758f619cc7fd761416c8082","block_sha256":"464a86bf4a3f0f784cc64a1533f95ae9ebd0246c0758f619cc7fd761416c8082","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_2a9ad754-71a1-4480-ad16-faf9dc1de82b"},{"id":"occ_6f43b4a56192fe4e7df2eb54","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_2bdb8e2a-d08b-4a8f-9568-b3eb35a941e2","section_id":"sec_d76b4dc8-b219-4a71-bba4-fd00697a33c7","layer":"code","character_id":null,"count":4,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":19,"end":26,"exact":"Prefill","quote":"```\nAWS案\n\nTrainium Prefill\n      ↓\nCerebras Decode\n\n\nNVIDIA案\n\nBlackwell／Rubin Prefill\n      ↓\nBlackwell／Rubin Decode\n      ↓\nD","quote_start":0,"quote_end":126,"text_sha256":"c6fcb70d71dd067d6f59f6017f21eef4bd452a961acf648fb0384a5293771992","block_sha256":"c6fcb70d71dd067d6f59f6017f21eef4bd452a961acf648fb0384a5293771992","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_2bdb8e2a-d08b-4a8f-9568-b3eb35a941e2"},{"id":"occ_adea46c94ef9ae86fc617e50","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_2ebb4c2d-5e46-4e83-9f54-09cc5859552d","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","Prefill","inference"],"evidence":{"text_basis":"markdown","start":39,"end":46,"exact":"Prefill","quote":"Cerebrasの2026年1～3月期資料にも、AWS Trainium 3がPrefillを行い、Cerebras CS-3がDecodeを実行する「disaggregated inference strategy」が明記されている。両システムはAmazonのカスタムネットワークで接続され","quote_start":0,"quote_end":146,"text_sha256":"c6a59ff350714d11980876de4544be4931072abc7c1783898c446b719ef76f42","block_sha256":"c6a59ff350714d11980876de4544be4931072abc7c1783898c446b719ef76f42","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_2ebb4c2d-5e46-4e83-9f54-09cc5859552d"},{"id":"occ_a53585a9eab8cd73a6adf490","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_2f2ce0a1-b941-48d5-8c56-4cd45b618706","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":72,"end":79,"exact":"Prefill","quote":"| 重視されるもの |\n| Encode | 画像・音声・動画の特徴抽出 | 専用カーネル、並列演算 |\n| Prefill | 入力全体の一括処理 | 行列演算性能、HBM容量 |\n| Decode | 1トークンずつ逐次生成 | メモリー帯域、低遅延、KV管理 |\n```","quote_start":17,"quote_end":155,"text_sha256":"818f910a2a48576f9a03cbc73a89386a5866ac70c38ff02741a0b7707aee507e","block_sha256":"818f910a2a48576f9a03cbc73a89386a5866ac70c38ff02741a0b7707aee507e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_2f2ce0a1-b941-48d5-8c56-4cd45b618706"},{"id":"occ_890ce581c8a3182ef5b50f83","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_302cbf39-49fe-4c40-9391-9ff6f91b85c3","section_id":"sec_5d4aa299-3fa1-4d89-83e4-6c878b1c3d82","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":14,"end":21,"exact":"Prefill","quote":"OpenAI案件については、PrefillとDecodeをどのように分担するかという詳細は公開されていない。","quote_start":0,"quote_end":54,"text_sha256":"ac1f014c78edc5ba16b5941579072c5f055b0b705c8a1be8a794c1cf773f4983","block_sha256":"ac1f014c78edc5ba16b5941579072c5f055b0b705c8a1be8a794c1cf773f4983","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_302cbf39-49fe-4c40-9391-9ff6f91b85c3"},{"id":"occ_f62e77287da3df87faf56e53","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_30d5285d-f59b-4f4a-95d1-7c068eeb3432","section_id":"sec_23706c9b-77f9-4da7-b3d4-f4745a46b974","layer":"body","character_id":null,"count":4,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"PrefillとDecodeを分離する場合、Prefill側で生成されたKVキャッシュをWSE側へ送る必要がある。","quote_start":0,"quote_end":57,"text_sha256":"6a50868c4479b555d909891132ce50e288b957182da0d9e63ae8709a82c28aec","block_sha256":"6a50868c4479b555d909891132ce50e288b957182da0d9e63ae8709a82c28aec","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_30d5285d-f59b-4f4a-95d1-7c068eeb3432"},{"id":"occ_87e0ff817ed8c4090acae4f3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_36a09a38-9adf-483b-9a99-f64bc0647503","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"code","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefillアクセラレーター\n        ↓\n数GB～数十GBのKVキャッシュ\n        ↓\n専用ネットワーク\n        ↓\nDecodeアクセラレーター\n```","quote_start":0,"quote_end":94,"text_sha256":"e9a844d02ecaf777a49e0fcf873a434635691503a8d0ac2cdb936d1692f3b00b","block_sha256":"e9a844d02ecaf777a49e0fcf873a434635691503a8d0ac2cdb936d1692f3b00b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_36a09a38-9adf-483b-9a99-f64bc0647503"},{"id":"occ_c5377d9db68295f95e1df946","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_36b3879a-48fd-4db6-a2a8-307a006e14af","section_id":"sec_da25a197-5f19-4723-933e-815ed5d5089d","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefillの性能は、主に最初の出力が始まるまでの時間であるTTFT、Time to First Tokenに影響する。","quote_start":0,"quote_end":61,"text_sha256":"9b4f4b7fa2904479e1d996d7aa7a4bd9ee246416ffa776fa161aad004c7a7a6b","block_sha256":"9b4f4b7fa2904479e1d996d7aa7a4bd9ee246416ffa776fa161aad004c7a7a6b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_36b3879a-48fd-4db6-a2a8-307a006e14af"},{"id":"occ_2a50ba164ede139d7688245c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_39c77854-c3cb-475a-aaf2-17235d8d4a98","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","推論"],"evidence":{"text_basis":"markdown","start":33,"end":35,"exact":"推論","quote":"> **現在のWSEが持つ圧倒的な分散SRAM帯域と低遅延通信を、推論パイプライン全体ではなく、Decodeという高付加価値工程へ集中させること。**","quote_start":0,"quote_end":75,"text_sha256":"cb22b514b6b00d896335a623c61a68cce23e26a86943bedca02561177a05fda7","block_sha256":"cb22b514b6b00d896335a623c61a68cce23e26a86943bedca02561177a05fda7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_39c77854-c3cb-475a-aaf2-17235d8d4a98"},{"id":"occ_b9c0f1e7e5ebc11955c7cdaf","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_3b9cfc9d-52ae-4d5c-825f-e29d0cd1553c","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":13,"end":19,"exact":"Decode","quote":"KV転送時間が大きければ、Decodeを高速化した利益が相殺される。","quote_start":0,"quote_end":34,"text_sha256":"15db1524117c95a121cd68bd8ee1ed0002ed0814042db4fb01c3cfc05658fede","block_sha256":"15db1524117c95a121cd68bd8ee1ed0002ed0814042db4fb01c3cfc05658fede","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_3b9cfc9d-52ae-4d5c-825f-e29d0cd1553c"},{"id":"occ_2631db20c2a98e2a41b97822","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_3c4d9b0c-bde6-40dd-a376-f1a2fb43953b","section_id":"sec_0875f100-4397-4df8-8237-7c7ac7bc5c36","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":13,"end":19,"exact":"Decode","quote":"次のような異種構成の中で、Decodeの完了時間と応答速度を大幅に改善することである。","quote_start":0,"quote_end":43,"text_sha256":"58af89106e1e213c64ef2bc1ddbaa0f4131f791a3621ec51269ec80e224985ee","block_sha256":"58af89106e1e213c64ef2bc1ddbaa0f4131f791a3621ec51269ec80e224985ee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_3c4d9b0c-bde6-40dd-a376-f1a2fb43953b"},{"id":"occ_d0f05c4a5186a439ba5b73fc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_3cabab20-eea4-4dd4-a954-a6abdfc88f5f","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":6,"end":12,"exact":"Decode","quote":"したがって、Decodeは本質的に逐次処理である。","quote_start":0,"quote_end":25,"text_sha256":"80a708cdfbf89b2c605544b594a675affc24e4edd24604dc999b229cd8f604b4","block_sha256":"80a708cdfbf89b2c605544b594a675affc24e4edd24604dc999b229cd8f604b4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_3cabab20-eea4-4dd4-a954-a6abdfc88f5f"},{"id":"occ_39f0bbb8b16c53aff76c2bc8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_3f265f83-217c-4995-8320-7deda0d894e7","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":17,"end":19,"exact":"推論","quote":"AWSとCerebrasは、LLM推論を次のように分担する構成を発表した。","quote_start":0,"quote_end":37,"text_sha256":"7a0eb76a49d4c0405d5e4c3a30b1f37e3eb3e76fd1174971212ae8957b16e441","block_sha256":"7a0eb76a49d4c0405d5e4c3a30b1f37e3eb3e76fd1174971212ae8957b16e441","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_3f265f83-217c-4995-8320-7deda0d894e7"},{"id":"occ_3e4e01a962690b361dea451f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_41220a46-4d2e-43d1-b5d9-4827b3f928b3","section_id":"sec_23706c9b-77f9-4da7-b3d4-f4745a46b974","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":17,"end":24,"exact":"Prefill","quote":"```\nTrainium／GPUでPrefill\n        ↓\n巨大KVを光転送\n        ↓\nWSEで高速Decode\n```","quote_start":0,"quote_end":70,"text_sha256":"2481cfd3e88e254350d686d8c15b722dd843237c5eb3dbd18e6b817d27613053","block_sha256":"2481cfd3e88e254350d686d8c15b722dd843237c5eb3dbd18e6b817d27613053","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_41220a46-4d2e-43d1-b5d9-4827b3f928b3"},{"id":"occ_8e04295562d1a01b5458545b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_42573ce1-03c5-4bba-b2af-3270405115c8","section_id":"sec_cac8fc60-788b-428f-8aaa-593077b20024","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":35,"end":37,"exact":"推論","quote":"TensorRT-LLMは、NVIDIA GPU向けに強く最適化された推論エンジンである。","quote_start":0,"quote_end":45,"text_sha256":"8f4020649d520e6e6d35abffca709576b178d3be117c805e9c16a8cb3c7e74f4","block_sha256":"8f4020649d520e6e6d35abffca709576b178d3be117c805e9c16a8cb3c7e74f4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_42573ce1-03c5-4bba-b2af-3270405115c8"},{"id":"occ_f2d90e6b9d75fcb1611eebb9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_42b11629-206c-443a-bea1-374a91b3b4d1","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":37,"end":39,"exact":"推論","quote":"そこで使われるのが、TensorRT-LLM、vLLM、SGLangなどの推論エンジンである。","quote_start":0,"quote_end":47,"text_sha256":"e4ec293de525f0d499ea0f898a95913d856bc4c8f37623da550a2065d3b9cf3a","block_sha256":"e4ec293de525f0d499ea0f898a95913d856bc4c8f37623da550a2065d3b9cf3a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_42b11629-206c-443a-bea1-374a91b3b4d1"},{"id":"occ_8d336a1fd263b1d7f2ccfeb9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_47ff58cb-0823-47f5-af93-4591eb57ad6f","section_id":"sec_210a9fa7-3018-4cba-a47f-11389c648f8a","layer":"code","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":93,"end":100,"exact":"KVキャッシュ","quote":"データ\n        ↓\n第2階層\n近接HBM／積層DRAM\n数百GB～1TB超・数十TB/s\nモデル重み・KVキャッシュ\n        ↓\n第3階層\n外部MemoryX\n数十TB～PB級\nモデル保管・チェックポイント\n```","quote_start":38,"quote_end":153,"text_sha256":"e10350862a60fafbbc572b6f5cd4ab4bcb276967b9e29c8ee1671aa4d940c995","block_sha256":"e10350862a60fafbbc572b6f5cd4ab4bcb276967b9e29c8ee1671aa4d940c995","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_47ff58cb-0823-47f5-af93-4591eb57ad6f"},{"id":"occ_49697f428fa726e32ef2c803","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_493e92f7-a019-490d-8b56-d7c044cd35ca","section_id":"sec_64aeac47-2257-47ff-932e-89f9fa29bf59","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":27,"end":29,"exact":"推論","quote":"利益率が低下していることは弱点だが、同時に、供給可能な推論容量を増やすために先行投資しているとも読める。","quote_start":0,"quote_end":52,"text_sha256":"ff624c620a166b05db9a5c24e85c5bbf50dbb5b91d0f04e49d8c1c987918d5bb","block_sha256":"ff624c620a166b05db9a5c24e85c5bbf50dbb5b91d0f04e49d8c1c987918d5bb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_493e92f7-a019-490d-8b56-d7c044cd35ca"},{"id":"occ_d07b4777987f94949fecb4bd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_49d6f337-6517-48a5-98cf-b3c33fd1d8d3","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","Prefill","推論"],"evidence":{"text_basis":"markdown","start":28,"end":35,"exact":"Prefill","quote":"AWSはこの構成を明示的に採用し、Trainium 3をPrefill、Cerebras CS-3をDecodeへ配置する。OpenAIも、AIの応答を高速化する目的で大規模なCerebras推論容量を契約している。([Cerebras](https://investo","quote_start":0,"quote_end":135,"text_sha256":"913046bbfabc6f27cf85bb1332ce9a10831534f28af691b6035edec192c00cf9","block_sha256":"913046bbfabc6f27cf85bb1332ce9a10831534f28af691b6035edec192c00cf9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_49d6f337-6517-48a5-98cf-b3c33fd1d8d3"},{"id":"occ_5844f8867458dcaa3141cce1","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_4e86b009-5b23-45ae-93e5-5102ad89bdc5","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"body","character_id":null,"count":4,"matched_aliases":["Decode","Prefill","推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"> AI推論をEncode、Prefill、Decodeへ分解し、Cerebrasは最も逐次性と低遅延が重要なDecodeを担当する。","quote_start":0,"quote_end":67,"text_sha256":"7ebc7b5712288ad93670bb0d8cfe4ca600894ea70c4d36c806a5dc718835efdc","block_sha256":"7ebc7b5712288ad93670bb0d8cfe4ca600894ea70c4d36c806a5dc718835efdc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_4e86b009-5b23-45ae-93e5-5102ad89bdc5"},{"id":"occ_6275bc6e844f0ff25fde775c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_4ed499bd-6c85-42cd-b102-44963c7e5e7f","section_id":"sec_5d4aa299-3fa1-4d89-83e4-6c878b1c3d82","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"CerebrasはOpenAIと、750MWの高速推論容量を段階的に提供する複数年契約を締結した。","quote_start":0,"quote_end":49,"text_sha256":"4b9cb5501e98ddf012062b64cfbf81e1b0403355a77af213906bb7476220afe1","block_sha256":"4b9cb5501e98ddf012062b64cfbf81e1b0403355a77af213906bb7476220afe1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_4ed499bd-6c85-42cd-b102-44963c7e5e7f"},{"id":"occ_94865595e39878ed325ba9b7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_5296983e-4fd5-40f3-b2bb-ab0e9c9bc6eb","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefill、Decode、KVキャッシュ、複数ユーザーの要求を効率よく処理するには、ハードウェアだけでは足りない。","quote_start":0,"quote_end":59,"text_sha256":"d8a092a0c4d0d966f9f8372b7dc423bdd61c5bc40cdf4e46bed1e26d8ef64f6f","block_sha256":"d8a092a0c4d0d966f9f8372b7dc423bdd61c5bc40cdf4e46bed1e26d8ef64f6f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_5296983e-4fd5-40f3-b2bb-ab0e9c9bc6eb"},{"id":"occ_840dcf70341d59307414ab54","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_547aba34-f2d4-467e-99ff-1a9f65cf5371","section_id":"sec_840be3e6-9166-4149-aa5a-31a52cc51be2","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":21,"end":28,"exact":"KVキャッシュ","quote":"代表技術のPagedAttentionは、KVキャッシュを固定サイズのブロックへ分割し、OSの仮想メモリーのように管理する。連続した巨大領域を事前確保せず、要求の長さに合わせて必要なブロックを追加できるため、HBMの断片化を減らし、同時処理数を増やせる。(","quote_start":0,"quote_end":128,"text_sha256":"ba0cd126c290edbd11a1a381dcf65ef9cbc56c7e34ca6e2bf2eb1151e4c31132","block_sha256":"ba0cd126c290edbd11a1a381dcf65ef9cbc56c7e34ca6e2bf2eb1151e4c31132","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_547aba34-f2d4-467e-99ff-1a9f65cf5371"},{"id":"occ_6ddbfe3f4c8f54a96fc41f49","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_5545e471-ce2e-4455-bcab-9365adde13d0","section_id":"sec_f99ecbf1-dc59-418f-b316-affc93bd2543","layer":"code","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefill ASIC／GPU\n        ↓\n光I/O\n        ↓\n光スイッチ／光ファブリック\n        ↓\nCerebras WSE\n        ↓\n近接HBM\n        ↓\n分散","quote_start":0,"quote_end":111,"text_sha256":"570a39c037acb787b931eee6f9fa41db9c5a022039cb98ec5a419153ef36ecd5","block_sha256":"570a39c037acb787b931eee6f9fa41db9c5a022039cb98ec5a419153ef36ecd5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_5545e471-ce2e-4455-bcab-9365adde13d0"},{"id":"occ_c4da33378fe978e13453c766","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_56f13c11-e9e8-4b41-b325-8c1d1f91a78d","section_id":"sec_c9fa09a5-e069-465d-993b-6b2bfa707f12","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":60,"end":62,"exact":"推論","quote":"ubin\n大容量HBMを持つ汎用AI GPU\n        ＋\nGroq LPU\n大容量SRAMを持つ低遅延推論機\n```","quote_start":5,"quote_end":67,"text_sha256":"608b1785d781afedb937c7d8f312f33ed2b260a3986dec6aded134360466d52e","block_sha256":"608b1785d781afedb937c7d8f312f33ed2b260a3986dec6aded134360466d52e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_56f13c11-e9e8-4b41-b325-8c1d1f91a78d"},{"id":"occ_f77727d93e014905f1ea62dd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_5b99adbe-3d3e-4e10-a2fa-f9c9428f6792","section_id":"sec_6c0daefd-7a05-4226-b0a9-14420d8b1387","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":50,"end":56,"exact":"Decode","quote":"バッチ1のDenseモデルでは、1トークン生成ごとにモデル重みの大部分を読み出すため、メモリー帯域がDecode速度へ強く影響する。","quote_start":0,"quote_end":66,"text_sha256":"83227538493c7f4684eb07e7c4b5f6c3c3c8d95fe907d34fb2628cfe53383af8","block_sha256":"83227538493c7f4684eb07e7c4b5f6c3c3c8d95fe907d34fb2628cfe53383af8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_5b99adbe-3d3e-4e10-a2fa-f9c9428f6792"},{"id":"occ_af969a6234922d8b15c63852","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_64597ae3-9d79-4827-8b63-bba91bf80620","section_id":"sec_95ee009c-29be-462d-93c1-73cf208404e2","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":7,"end":14,"exact":"KVキャッシュ","quote":"実際には演算、KVキャッシュ、Attention、メモリー効率、量子化、データ配置などが加わるため、この数字がそのままトークン間隔になるわけではない。","quote_start":0,"quote_end":75,"text_sha256":"6c4881855fa0f74333f783e595d7c09325bd6f4ae6cee9e69a8851e031bbcff2","block_sha256":"6c4881855fa0f74333f783e595d7c09325bd6f4ae6cee9e69a8851e031bbcff2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_64597ae3-9d79-4827-8b63-bba91bf80620"},{"id":"occ_86c39267fce825bd4e5771a0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_675a4c6d-1ed6-4c1c-8c23-608d42023675","section_id":"sec_23706c9b-77f9-4da7-b3d4-f4745a46b974","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":3,"end":10,"exact":"KVキャッシュ","quote":"## KVキャッシュ転送はどこまで短縮できるか","quote_start":0,"quote_end":23,"text_sha256":"b1934c83b2026662425a0c41ece94599b59da716ca45c192ea6197ac096ede1e","block_sha256":"b1934c83b2026662425a0c41ece94599b59da716ca45c192ea6197ac096ede1e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_675a4c6d-1ed6-4c1c-8c23-608d42023675"},{"id":"occ_daa49d7e7d34e559db0b0f7b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_69a58158-b4ad-435e-9bc2-f989eaa7d9e1","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"code","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":49,"end":56,"exact":"Prefill","quote":"```\nTrainiumの強み\n\n長い入力をまとめて処理\n高い行列演算効率\n大容量メモリー\n大量のPrefillを処理\n\n\nCerebrasの強み\n\nウェハー内通信\n分散SRAM\n低い同期遅延\n高速な逐次生成\n```","quote_start":0,"quote_end":108,"text_sha256":"a067dade9c441069c02e04c0db849d511b607f564a0d704e17924e2eccd864e6","block_sha256":"a067dade9c441069c02e04c0db849d511b607f564a0d704e17924e2eccd864e6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_69a58158-b4ad-435e-9bc2-f989eaa7d9e1"},{"id":"occ_f40a4b6c8e95052a85042c0e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_6ca70870-2096-43e3-81bb-2fc692f37b41","section_id":"sec_0e019c7e-6538-4bcd-90b4-47bbd950f7d4","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":34,"end":41,"exact":"Prefill","quote":"Llama系などのDecoder-onlyモデルでは、文字入力は直接Prefillへ送られる。","quote_start":0,"quote_end":47,"text_sha256":"5ad7de327b78a4cb5bfd2213944216c4d6ba7899e71208ff90634a7930385a23","block_sha256":"5ad7de327b78a4cb5bfd2213944216c4d6ba7899e71208ff90634a7930385a23","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_6ca70870-2096-43e3-81bb-2fc692f37b41"},{"id":"occ_855fb0bfd0dac025c903fa3e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_6e66df13-ddc0-4064-8e03-0812f0758dfc","section_id":"sec_0e019c7e-6538-4bcd-90b4-47bbd950f7d4","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":13,"end":20,"exact":"Prefill","quote":"```\nテキスト\n  ↓\nPrefill\n  ↓\nDecode\n```","quote_start":0,"quote_end":35,"text_sha256":"04ecea6d7fbaad456378b49eb6830f48479ab654ea29c57bb1fe4b1da086f8a3","block_sha256":"04ecea6d7fbaad456378b49eb6830f48479ab654ea29c57bb1fe4b1da086f8a3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_6e66df13-ddc0-4064-8e03-0812f0758dfc"},{"id":"occ_02db3989d5835aebbc6a8fa9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_7134de68-317a-43c5-9b99-be3909f27f9f","section_id":"sec_29c83f03-75c2-4de7-a777-58eca34e2120","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":25,"end":31,"exact":"Decode","quote":"## ――44GB SRAMの弱点から見えてくる「Decode専用機」という別の勝ち筋","quote_start":0,"quote_end":43,"text_sha256":"bff6e4546142803618a5fd75fd2214f4fd98042f10994c0bf4b8f8cb81ba25c5","block_sha256":"bff6e4546142803618a5fd75fd2214f4fd98042f10994c0bf4b8f8cb81ba25c5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_7134de68-317a-43c5-9b99-be3909f27f9f"},{"id":"occ_7e82c3f6db20b2571f5e8d55","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_729464a1-730e-41f3-924e-686626eb309d","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"code","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":38,"end":45,"exact":"Prefill","quote":"```\nユーザーの入力\n      ↓\nAmazon Trainium 3\nPrefillを担当\n      ↓\nKVキャッシュを転送\n      ↓\nCerebras CS-3\nDecodeを担当\n      ↓\n回答を高速生成\n```","quote_start":0,"quote_end":119,"text_sha256":"1a61bf2b235916d1d33db062b9b9398678a4acee1f3bcaa143070e30cf273d74","block_sha256":"1a61bf2b235916d1d33db062b9b9398678a4acee1f3bcaa143070e30cf273d74","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_729464a1-730e-41f3-924e-686626eb309d"},{"id":"occ_669c63d8555fbd7e9c2b3246","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_7312f195-7bcf-4cdc-a485-2d4f5df4a6f7","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":4,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"PrefillとDecodeを同じGPUで混在させると、大きなPrefill処理が進行中のDecodeへ割り込み、回答の表示間隔が不安定になることがある。両工程を別々のGPUやアクセラレーターへ配置することで、干渉","quote_start":0,"quote_end":107,"text_sha256":"0e2c9bd5396b660671d39855d0e011341e4c37b3d595f28f132ceef83a03d6ed","block_sha256":"0e2c9bd5396b660671d39855d0e011341e4c37b3d595f28f132ceef83a03d6ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_7312f195-7bcf-4cdc-a485-2d4f5df4a6f7"},{"id":"occ_93c8dfc064ba60854e58eb7b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_736ca1c3-b324-40cd-9881-57df769c360e","section_id":"sec_64aeac47-2257-47ff-932e-89f9fa29bf59","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":96,"end":98,"exact":"推論","quote":"ードウェア収益\n\n\n現在のCerebras\n\nWSEを自社・提携データセンターへ設置\n        ↓\n高速推論容量をクラウドとして販売\n        ↓\n継続的なサービス収益\n```","quote_start":41,"quote_end":135,"text_sha256":"aa16e7441f653ad69925da0b548335fc87e65bebc4cfe0e8b4b42a687da66398","block_sha256":"aa16e7441f653ad69925da0b548335fc87e65bebc4cfe0e8b4b42a687da66398","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_736ca1c3-b324-40cd-9881-57df769c360e"},{"id":"occ_f8c482c4fb0ceb673b1ef327","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_75c49df3-ba56-461d-b02f-7632319cd208","section_id":"sec_c9fa09a5-e069-465d-993b-6b2bfa707f12","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"Prefill","quote":"> HBM系アクセラレーターでPrefillや容量問題を処理し、SRAM系アクセラレーターでDecodeを高速化する","quote_start":0,"quote_end":58,"text_sha256":"bf82702cf1355a102072f42afe2141a9df8a22e1b06e7841c61eb8bcfbaf41dd","block_sha256":"bf82702cf1355a102072f42afe2141a9df8a22e1b06e7841c61eb8bcfbaf41dd","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_75c49df3-ba56-461d-b02f-7632319cd208"},{"id":"occ_f9f956fbba9be24ddceb5777","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_78128ddc-deef-4d66-86ef-32fab51a9d35","section_id":"sec_0e019c7e-6538-4bcd-90b4-47bbd950f7d4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":10,"end":12,"exact":"推論","quote":"将来のマルチモーダル推論基盤では、","quote_start":0,"quote_end":17,"text_sha256":"8d74bd1a0bd09f54f760285a35ae0cb56eaca743b93b9d763069419585050b6e","block_sha256":"8d74bd1a0bd09f54f760285a35ae0cb56eaca743b93b9d763069419585050b6e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_78128ddc-deef-4d66-86ef-32fab51a9d35"},{"id":"occ_9c0fb2e8a4424b3e450b88be","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_7a3f4800-7fd3-44ff-9a9d-c6b1519a9f2b","section_id":"sec_0e019c7e-6538-4bcd-90b4-47bbd950f7d4","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":27,"end":34,"exact":"Prefill","quote":"```\nEncoder専用プール\n        ↓\nPrefill専用プール\n        ↓\nDecode専用プール\n```","quote_start":0,"quote_end":65,"text_sha256":"847aab84c6f1cd7ec711459e8aebc33f56bd35e0046022532a2ce2395604f05c","block_sha256":"847aab84c6f1cd7ec711459e8aebc33f56bd35e0046022532a2ce2395604f05c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_7a3f4800-7fd3-44ff-9a9d-c6b1519a9f2b"},{"id":"occ_2fab4a92ca3971f759c9e82d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_7da145f0-e62b-4cc2-921f-52ed061fc4c7","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"KVキャッシュ","quote":"- モデル重みを参照する\n- KVキャッシュを読む\n- Attentionを計算する\n- 複数チップ間で同期する\n- 次のトークンを選択する","quote_start":0,"quote_end":70,"text_sha256":"3fc10c01e4f0ca3c4b242dacfca2d56d627f2ed550e3abd35148e6179b5359d1","block_sha256":"3fc10c01e4f0ca3c4b242dacfca2d56d627f2ed550e3abd35148e6179b5359d1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_7da145f0-e62b-4cc2-921f-52ed061fc4c7"},{"id":"occ_2005c43cdac1c087629d4786","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_7ddd47e8-1d9f-4cb7-9f40-f4bc2529312c","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":21,"end":23,"exact":"推論","quote":"```\n学習済みモデル\n       ↓\n推論エンジン\nバッチ化・KV管理・量子化・並列化\n       ↓\nGPU／ASIC\n       ↓\nユーザーへの回答\n```","quote_start":0,"quote_end":85,"text_sha256":"96f0b1ff027616567e5567850d6e1c5b886edd323f705310598c71f9fb48b648","block_sha256":"96f0b1ff027616567e5567850d6e1c5b886edd323f705310598c71f9fb48b648","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_7ddd47e8-1d9f-4cb7-9f40-f4bc2529312c"},{"id":"occ_17acf06d19212859e6661bfc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_8601d5e2-c318-4442-b685-977156e7a6db","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論エンジンはAIモデルそのものではない。","quote_start":0,"quote_end":21,"text_sha256":"776b2f1333215e05b9656a7b0ea29246a2c6f776f02ce6416f78d706617b9195","block_sha256":"776b2f1333215e05b9656a7b0ea29246a2c6f776f02ce6416f78d706617b9195","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_8601d5e2-c318-4442-b685-977156e7a6db"},{"id":"occ_850beb36c923818480e9c81c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_8710fa5b-cab8-4107-81b7-bee6fba7156e","section_id":"sec_da25a197-5f19-4723-933e-815ed5d5089d","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","Prefill","推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"LLM推論は、大きくPrefillとDecodeへ分けられる。","quote_start":0,"quote_end":31,"text_sha256":"91361b4fb3994d25c7642d38e7429922892174b3c51b559da7e9e31cacbf3331","block_sha256":"91361b4fb3994d25c7642d38e7429922892174b3c51b559da7e9e31cacbf3331","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_8710fa5b-cab8-4107-81b7-bee6fba7156e"},{"id":"occ_4f95bd7c612d21108e169802","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_88738cc2-2d7b-4828-8122-b1b298ff7c8f","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":0,"end":6,"exact":"Decode","quote":"Decodeの性能は、TPOT（Time Per Output Token）やITL（Inter-Token Latency）として測られる。","quote_start":0,"quote_end":71,"text_sha256":"890295fc86dd08ad01b659e59c2ca54e73692c2c7fd560444e4ec18976077daf","block_sha256":"890295fc86dd08ad01b659e59c2ca54e73692c2c7fd560444e4ec18976077daf","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_88738cc2-2d7b-4828-8122-b1b298ff7c8f"},{"id":"occ_441b7f2dd4dd07e6f5e8b790","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_8b34c3dd-a0aa-470e-aa29-8a495119eca2","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":2,"end":8,"exact":"Decode","quote":"**Decodeだけで明確な性能差を作れれば、異種アクセラレーター基盤の一部として生き残れる。**","quote_start":0,"quote_end":49,"text_sha256":"bdf8cb06ac5221a44db64c8975f6a4c30f6042ecd43365281c758a0676e12635","block_sha256":"bdf8cb06ac5221a44db64c8975f6a4c30f6042ecd43365281c758a0676e12635","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_8b34c3dd-a0aa-470e-aa29-8a495119eca2"},{"id":"occ_f1d49e66eebf71b6a137a48e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_8d52e4af-7516-417b-9b3b-0b92400045f6","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":0,"end":2,"exact":"推論","quote":"推論エンジンの主な仕事は、","quote_start":0,"quote_end":13,"text_sha256":"90f5d8daaf58a6e383980ec674e81111d4457b3d9c48eb1b34700c78c3d0f90d","block_sha256":"90f5d8daaf58a6e383980ec674e81111d4457b3d9c48eb1b34700c78c3d0f90d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_8d52e4af-7516-417b-9b3b-0b92400045f6"},{"id":"occ_c2b5fced09928deb678a267a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_9280e648-2691-4613-99df-5e38a8729732","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":25,"end":31,"exact":"Decode","quote":"**巨大な異種AIインフラの中で、時間的価値の高いDecode工程だけを獲得する需要が存在する**ということである。","quote_start":0,"quote_end":58,"text_sha256":"66b0bfaa1138bf8e067c44512c4fa4ca1a36e8e8bc9eb43673970d6eb8ac1159","block_sha256":"66b0bfaa1138bf8e067c44512c4fa4ca1a36e8e8bc9eb43673970d6eb8ac1159","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_9280e648-2691-4613-99df-5e38a8729732"},{"id":"occ_2cb51463cedf79de5e68ad9d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_92831978-bab1-4d99-8d3c-69b5038ca64a","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"```\n巨大な推論システムの中で\n最も遅延が問題になる工程を\nWSEへ切り出すこと\n```","quote_start":0,"quote_end":45,"text_sha256":"c948a65e5ef275eec8aacc2a1d33ed8936b7b8ee3fb8041616b12579805a5766","block_sha256":"c948a65e5ef275eec8aacc2a1d33ed8936b7b8ee3fb8041616b12579805a5766","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_92831978-bab1-4d99-8d3c-69b5038ca64a"},{"id":"occ_5e8c9d3565b2601c258e8c9b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_99198ecc-5407-4cac-81b3-7a02880ca895","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":0,"end":6,"exact":"Decode","quote":"Decodeでは、トークンを1個生成するたびに、","quote_start":0,"quote_end":24,"text_sha256":"a9050c2d9e51380db92bdc140e7b7e7eff8f80356c2431dda98ea8db8325de3c","block_sha256":"a9050c2d9e51380db92bdc140e7b7e7eff8f80356c2431dda98ea8db8325de3c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_99198ecc-5407-4cac-81b3-7a02880ca895"},{"id":"occ_6dece96930bf61290252e31e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_9b1cb2b3-0be7-4ea2-a2db-6d9e9a376889","section_id":"sec_0875f100-4397-4df8-8237-7c7ac7bc5c36","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":4,"end":10,"exact":"Decode","quote":"第三に、Decodeだけへ設備を集中することで、1ユーザー当たりの生成速度やエージェントの処理完了時間を短縮できる。","quote_start":0,"quote_end":58,"text_sha256":"31e8ad4fbba430a215aafc3fc06c96fda0095181f0b3da2c0643d8ad272c7b4d","block_sha256":"31e8ad4fbba430a215aafc3fc06c96fda0095181f0b3da2c0643d8ad272c7b4d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_9b1cb2b3-0be7-4ea2-a2db-6d9e9a376889"},{"id":"occ_313edd5ad3b7adbaed43ab97","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_9d6f4ff3-2539-4999-82c9-78454c021876","section_id":"sec_da25a197-5f19-4723-933e-815ed5d5089d","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":3,"end":10,"exact":"Prefill","quote":"## Prefillとは何か","quote_start":0,"quote_end":14,"text_sha256":"39e05e30ed1cc1407f27c604ee2bfba3c731818228c8e3806cbe80f0c9a58b1f","block_sha256":"39e05e30ed1cc1407f27c604ee2bfba3c731818228c8e3806cbe80f0c9a58b1f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_9d6f4ff3-2539-4999-82c9-78454c021876"},{"id":"occ_3ffa111314a73f23c064e60c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a08068f3-5986-4c3d-817c-7cf042e57190","section_id":"sec_d76b4dc8-b219-4a71-bba4-fd00697a33c7","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":36,"end":43,"exact":"KVキャッシュ","quote":"さらにNVIDIAも、TensorRT-LLM、Dynamo、NIXL、KVキャッシュ再利用、Prefill／Decode分離を進化させている。","quote_start":0,"quote_end":72,"text_sha256":"3897b379f37100d18942ffa8e393eb5a6bac3bf09f7a4eab2a6cded89cc24147","block_sha256":"3897b379f37100d18942ffa8e393eb5a6bac3bf09f7a4eab2a6cded89cc24147","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a08068f3-5986-4c3d-817c-7cf042e57190"},{"id":"occ_9d0c4c43cca4f378ce8ebe90","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a37b928e-b8d5-46f3-ab21-6e4d48839663","section_id":"sec_da25a197-5f19-4723-933e-815ed5d5089d","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefillは、ユーザーが入力した文章全体を最初に処理する工程である。","quote_start":0,"quote_end":36,"text_sha256":"3c05b29b0ce0526107ce50b0d000872083bf2f14da88258ddaf8c2d03aa3045c","block_sha256":"3c05b29b0ce0526107ce50b0d000872083bf2f14da88258ddaf8c2d03aa3045c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a37b928e-b8d5-46f3-ab21-6e4d48839663"},{"id":"occ_641cf528ee4b3d0b496d35e9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a38e84eb-34db-4e33-821c-1cfcedcc9e5f","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":24,"end":26,"exact":"推論","quote":"## 結論――Cerebrasは万能機ではなく、推論パイプラインの専用機になる","quote_start":0,"quote_end":39,"text_sha256":"08ea215549203afeeaf1ac2666a9176bc52fd36d4c5814c44484b382c9586568","block_sha256":"08ea215549203afeeaf1ac2666a9176bc52fd36d4c5814c44484b382c9586568","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a38e84eb-34db-4e33-821c-1cfcedcc9e5f"},{"id":"occ_944ddf2640b0a0f47f764315","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a4066abc-1616-48d3-9885-987b09672561","section_id":"sec_0875f100-4397-4df8-8237-7c7ac7bc5c36","layer":"code","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":48,"end":54,"exact":"Decode","quote":"```\nCerebras\n\n巨大なWSE\n＋ 分散SRAM\n＋ オンウェハーNoC\n＋ 超低遅延Decode\n```","quote_start":0,"quote_end":58,"text_sha256":"ab0ff6f75ff01290582be7e0a458bd88d8ce4565c23e5a34426ae3cd33448e41","block_sha256":"ab0ff6f75ff01290582be7e0a458bd88d8ce4565c23e5a34426ae3cd33448e41","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a4066abc-1616-48d3-9885-987b09672561"},{"id":"occ_ffe2bc52ffb3910d65ce8fae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a56676d8-67b5-40e2-a746-c569ce4c6cd7","section_id":"sec_0875f100-4397-4df8-8237-7c7ac7bc5c36","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":25,"end":27,"exact":"推論","quote":"第五に、ハードウェアを一括販売するのではなく、高速推論容量を継続的なクラウドサービスとして販売できる。","quote_start":0,"quote_end":51,"text_sha256":"504d6d05f2ec25276930f886628cee7ead4a11c387d127d1352c3e85e7b1dbf8","block_sha256":"504d6d05f2ec25276930f886628cee7ead4a11c387d127d1352c3e85e7b1dbf8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a56676d8-67b5-40e2-a746-c569ce4c6cd7"},{"id":"occ_2a33be9f8e280ab8b95337dd","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a664e6f5-66d4-4406-8f82-e12cc7e49880","section_id":"sec_0e019c7e-6538-4bcd-90b4-47bbd950f7d4","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":78,"end":85,"exact":"Prefill","quote":"Encoder\n特徴ベクトル・埋め込みへ変換\n        ↓\nテキストトークンと結合\n        ↓\nPrefill\n        ↓\nDecode\n```","quote_start":23,"quote_end":106,"text_sha256":"e9d7a7de13dc9395f7dd559c4e206703f87e09228e2e9a5c142e1c47b5df3032","block_sha256":"e9d7a7de13dc9395f7dd559c4e206703f87e09228e2e9a5c142e1c47b5df3032","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a664e6f5-66d4-4406-8f82-e12cc7e49880"},{"id":"occ_cd38fbb85ce3d2c989796786","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a66eb79a-6d79-4464-8d74-762f18b757df","section_id":"sec_c9fa09a5-e069-465d-993b-6b2bfa707f12","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":64,"end":66,"exact":"推論","quote":"ra Rubin NVL72の構成で、288GB・22TB/sのHBM4を持つRubin GPUに加え、低遅延推論向けのGroq 3 LPUを組み合わせる方針を示している。公式仕様ではGroq 3 LPXラックは128GBのSRAM、40PB/sのメモリー帯域、640TB/sのラック内scale-up帯域を持つと","quote_start":9,"quote_end":166,"text_sha256":"34996a4f23bb00603959acc4188e46115b948b9fa793d9c85a66414359728e1a","block_sha256":"34996a4f23bb00603959acc4188e46115b948b9fa793d9c85a66414359728e1a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a66eb79a-6d79-4464-8d74-762f18b757df"},{"id":"occ_415b4d16a6e0ea7bc28a3ad5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a6eaf29f-507d-44c0-9d26-31424c33ee69","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":5,"end":12,"exact":"Prefill","quote":"## なぜPrefillとDecodeを分離するのか","quote_start":0,"quote_end":26,"text_sha256":"4c53680ecbdc514848e95bfeec5e3f1156fbae40e09c30693e7a05d5d1268830","block_sha256":"4c53680ecbdc514848e95bfeec5e3f1156fbae40e09c30693e7a05d5d1268830","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a6eaf29f-507d-44c0-9d26-31424c33ee69"},{"id":"occ_e5f715afe733d474b4c98342","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_a8d823a6-3f73-478d-b5ee-2b7072c62581","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefill側で作ったKVキャッシュを、Decode側へ転送しなければならない。","quote_start":0,"quote_end":41,"text_sha256":"aa16699580c2fd3a4b1c5d8a0857b4e3ea59c03874101d01742a2d911d163995","block_sha256":"aa16699580c2fd3a4b1c5d8a0857b4e3ea59c03874101d01742a2d911d163995","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_a8d823a6-3f73-478d-b5ee-2b7072c62581"},{"id":"occ_9814ef845d9126c8e5714716","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_aac092d2-4a1f-4194-830e-7ad9010e0394","section_id":"sec_da25a197-5f19-4723-933e-815ed5d5089d","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"Prefillでは数百から数万の入力トークンをまとめて計算できる。","quote_start":0,"quote_end":33,"text_sha256":"cbe40b90d3a9e8924fed261b7bda5879499410eb0c05fce2c63cd6e58425f313","block_sha256":"cbe40b90d3a9e8924fed261b7bda5879499410eb0c05fce2c63cd6e58425f313","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_aac092d2-4a1f-4194-830e-7ad9010e0394"},{"id":"occ_cc10fc91012b2c9222dcb737","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_ac0dfebe-0837-407a-ae40-bfcc285855db","section_id":"sec_be8484e9-3419-494b-b1dc-280c7cdfb30e","layer":"body","character_id":null,"count":4,"matched_aliases":["Decode","KVキャッシュ","Prefill","推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"これらの推論エンジンがPrefillやDecodeを実行し、Dynamoのような上位のオーケストレーターが要求の配置を決め、NIXLのような転送ライブラリーがKVキャッシュを実際に運ぶ。","quote_start":0,"quote_end":93,"text_sha256":"e83a77c3b5afd0442c22c8b198553d2371f702501cabd8502480188880623adc","block_sha256":"e83a77c3b5afd0442c22c8b198553d2371f702501cabd8502480188880623adc","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_ac0dfebe-0837-407a-ae40-bfcc285855db"},{"id":"occ_e50962e14cd122b4df174b55","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_b1ff3d56-89a1-45f0-81ae-eef74559dd6c","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":25,"end":32,"exact":"KVキャッシュ","quote":"- メモリー帯域\n- 通信遅延\n- 同期回数\n- KVキャッシュ管理\n- トークン間の待ち時間","quote_start":0,"quote_end":47,"text_sha256":"4bee708967659ec676fa2a9d90934e3c0373b0573dd261d826a5cf570d013939","block_sha256":"4bee708967659ec676fa2a9d90934e3c0373b0573dd261d826a5cf570d013939","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_b1ff3d56-89a1-45f0-81ae-eef74559dd6c"},{"id":"occ_c773d439df2ee8d3dde05560","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_b49c93f3-12f3-4000-a0cf-f966ace47373","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":9,"end":16,"exact":"Prefill","quote":"CerebrasはPrefillもDecodeも学習もすべて担当する必要がない。","quote_start":0,"quote_end":40,"text_sha256":"6792aa2910f16518d9dc91af928ac75c132df18e046b603cf8fc4850846aab18","block_sha256":"6792aa2910f16518d9dc91af928ac75c132df18e046b603cf8fc4850846aab18","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_b49c93f3-12f3-4000-a0cf-f966ace47373"},{"id":"occ_63991b301f85d440a6642505","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_bc036c7c-44e0-4f09-a785-92827858928e","section_id":"sec_f7e43701-dd90-4c7f-be8e-e0e9dfd4b793","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":6,"exact":"Decode","quote":"Decodeは、Prefillで作ったKVキャッシュを利用しながら、回答を1トークンずつ生成する工程である。","quote_start":0,"quote_end":54,"text_sha256":"2f204c2ae6a0593b120d034499a91c9d38e9c1b8219c02363a7820a457c0adf3","block_sha256":"2f204c2ae6a0593b120d034499a91c9d38e9c1b8219c02363a7820a457c0adf3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_bc036c7c-44e0-4f09-a785-92827858928e"},{"id":"occ_71a1c2aae820878abea4fd37","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_bc6a6d27-40dd-4cc4-bdfe-8451771854e4","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":36,"end":38,"exact":"推論","quote":"> WSE単体のメモリー構造だけを見れば勝ち目は狭い。  \n> しかし、推論を工程別に分離する時代には、WSEの極端な低遅延性そのものが専用アクセラレーターとしての価値になる。","quote_start":0,"quote_end":88,"text_sha256":"bc95f1362d8a4b706e598ae7fc8fead32d67afc17ede6d5f49cf2983a93ebba4","block_sha256":"bc95f1362d8a4b706e598ae7fc8fead32d67afc17ede6d5f49cf2983a93ebba4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_bc6a6d27-40dd-4cc4-bdfe-8451771854e4"},{"id":"occ_09c79bf9fb167260c5b8a74e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_c10c810a-3390-42bd-8aec-036621308eac","section_id":"sec_29c83f03-75c2-4de7-a777-58eca34e2120","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","推論"],"evidence":{"text_basis":"markdown","start":5,"end":7,"exact":"推論","quote":"**LLM推論を複数の工程へ分解し、その中でも低遅延が特に重要なDecodeを担当する専用アクセラレーター**として、自社の位置を確立しようとしているのである。","quote_start":0,"quote_end":80,"text_sha256":"a2d94a57054eff95fc6c3218dc2127602ad05c5ddc3146f4a6738731cc24a7f8","block_sha256":"a2d94a57054eff95fc6c3218dc2127602ad05c5ddc3146f4a6738731cc24a7f8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_c10c810a-3390-42bd-8aec-036621308eac"},{"id":"occ_0b137ebe1fd7053f3b6598ab","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_c489def6-b472-4fdb-b705-56a55f1fc182","section_id":"sec_be8484e9-3419-494b-b1dc-280c7cdfb30e","layer":"body","character_id":null,"count":1,"matched_aliases":["KVキャッシュ"],"evidence":{"text_basis":"markdown","start":52,"end":59,"exact":"KVキャッシュ","quote":"代表技術のRadixAttentionは、複数の要求に共通するシステムプロンプトや会話履歴を見つけ、そのKVキャッシュを再利用する。","quote_start":0,"quote_end":66,"text_sha256":"821867c148b149439a5f41d39af140e0f91ec10275b9955f632eac358ee076e3","block_sha256":"821867c148b149439a5f41d39af140e0f91ec10275b9955f632eac358ee076e3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_c489def6-b472-4fdb-b705-56a55f1fc182"},{"id":"occ_ba85aa0fc3e1d8c88851e2db","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_c85cd098-f418-4154-9a3e-c94394dab5f6","section_id":"sec_1884114a-2540-493d-ad0c-b6fc80b6c253","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":61,"end":67,"exact":"Decode","quote":"ebrasがNVIDIAへ勝つというより、NVIDIA、Trainium、各社ASICと共存しながら、**高速Decodeという高付加価値工程を獲得する戦略**である。","quote_start":6,"quote_end":90,"text_sha256":"a5c25bac7a04bbbbf13a31322942f8f8f30bc624e4e477ea309c45109eadbeee","block_sha256":"a5c25bac7a04bbbbf13a31322942f8f8f30bc624e4e477ea309c45109eadbeee","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_c85cd098-f418-4154-9a3e-c94394dab5f6"},{"id":"occ_95f73367da240e8aedac8566","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_c919bcad-a792-4e51-bffd-34c893a6b7bc","section_id":"sec_4fa93586-6946-4576-b2e4-3fc4bbaa28f6","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":0,"end":7,"exact":"Prefill","quote":"PrefillはTrainium、GPU、各社ASICが担当し、大容量HBMで入力とKVキャッシュを処理する。Cerebrasは、その後の逐次生成を担当し、応答時間、エージェントの反復速度、コード生成の完了時間を短","quote_start":0,"quote_end":107,"text_sha256":"e4a3af99b30f3ddf3074820c20733255a173c6c2aade399d7b79328a67be0bd8","block_sha256":"e4a3af99b30f3ddf3074820c20733255a173c6c2aade399d7b79328a67be0bd8","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_c919bcad-a792-4e51-bffd-34c893a6b7bc"},{"id":"occ_dadd7d26370a77c061fb27b9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_cead89d4-1089-418f-bef6-8c3ce93af907","section_id":"sec_152cfd42-2e93-4575-8e2f-a12bcf4b25d5","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"特に、小バッチ推論、巨大なKVキャッシュ、長文脈、大規模MoE、不規則なメモリーアクセスでは、モデル重みや状態を大容量HBMへ常駐できるGPU・ASICが有利になりやすい。","quote_start":0,"quote_end":86,"text_sha256":"8e673e72d33a3ecb44403416a380f1f4f6a4805d3ec4d445aa83cea1b8337c96","block_sha256":"8e673e72d33a3ecb44403416a380f1f4f6a4805d3ec4d445aa83cea1b8337c96","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_cead89d4-1089-418f-bef6-8c3ce93af907"},{"id":"occ_48c0177d5ab6e083fcfe568a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_cef59f0b-0664-4d0f-9644-2d31b96c99d5","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":18,"end":25,"exact":"KVキャッシュ","quote":"- 多数の要求を動的にまとめる\n- KVキャッシュを効率よく配置する\n- 終了した要求をバッチから外す\n- 新しい要求を途中から追加する\n- モデルを複数GPUへ分割する\n- FP8、FP4、INT8、INT4などへ量子化する\n- 投機的デコードを行","quote_start":0,"quote_end":125,"text_sha256":"50c87521b11dac1615b8ccf8c65ed6fc6421e3cfbe52c100baa09d1cd84ff2fa","block_sha256":"50c87521b11dac1615b8ccf8c65ed6fc6421e3cfbe52c100baa09d1cd84ff2fa","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_cef59f0b-0664-4d0f-9644-2d31b96c99d5"},{"id":"occ_1244d2d897d5809c114cd0ae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_dbcc4e39-87fb-40e8-bcde-61fa847e4523","section_id":"sec_be8484e9-3419-494b-b1dc-280c7cdfb30e","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":7,"end":14,"exact":"Prefill","quote":"共通部分を毎回Prefillし直さずに済むため、RAG、エージェント、共通プロンプトの多いサービスで効果を発揮する。([arXiv](https://arxiv.org/abs/2312.07104?utm_source=cha","quote_start":0,"quote_end":114,"text_sha256":"392faaf17a5fa31922a6ac1fac47a00793a5bb49c7aa565ea1b1f9cea3046a77","block_sha256":"392faaf17a5fa31922a6ac1fac47a00793a5bb49c7aa565ea1b1f9cea3046a77","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_dbcc4e39-87fb-40e8-bcde-61fa847e4523"},{"id":"occ_e6f52749792816192d436723","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_e1a7ff24-78f5-427e-acf0-cf9a804b7543","section_id":"sec_95ee009c-29be-462d-93c1-73cf208404e2","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","推論"],"evidence":{"text_basis":"markdown","start":11,"end":17,"exact":"Decode","quote":"これはWSEを、低遅延Decode専用機から、より広い長文脈、MoE、大型モデル推論へ拡張する上で大きな意味を持つ。","quote_start":0,"quote_end":58,"text_sha256":"52429284572f116c3ce07c992ee95799e9a64f253d834a60c2bb6088655a67a9","block_sha256":"52429284572f116c3ce07c992ee95799e9a64f253d834a60c2bb6088655a67a9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_e1a7ff24-78f5-427e-acf0-cf9a804b7543"},{"id":"occ_4a2a4305022ea3fbcc3538fe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_eb2f0b51-1e51-48bf-a937-ce8f966f5a0d","section_id":"sec_5d4aa299-3fa1-4d89-83e4-6c878b1c3d82","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":76,"end":78,"exact":"推論","quote":"SIC、複数のクラウド、Cerebrasなどを組み合わせる。Cerebrasはその中で、**応答速度を重視する推論容量**として採用されている。","quote_start":21,"quote_end":93,"text_sha256":"3faa224d2f6fe7ed2046e4a0825f9e74810eecdfe5290f6d16060826ffc66379","block_sha256":"3faa224d2f6fe7ed2046e4a0825f9e74810eecdfe5290f6d16060826ffc66379","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_eb2f0b51-1e51-48bf-a937-ce8f966f5a0d"},{"id":"occ_084ba690da8b144871761ad7","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_ec8e7647-9b6f-43d4-b392-292f913b1ad8","section_id":"sec_840be3e6-9166-4149-aa5a-31a52cc51be2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":31,"end":33,"exact":"推論","quote":"vLLMは、導入の容易さとメモリー効率を重視したオープンソース推論エンジンである。","quote_start":0,"quote_end":41,"text_sha256":"2554c660ee34e4843bd76e2d5a60e7b27ca2edbc0983334e931f0b55b5d2afbb","block_sha256":"2554c660ee34e4843bd76e2d5a60e7b27ca2edbc0983334e931f0b55b5d2afbb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_ec8e7647-9b6f-43d4-b392-292f913b1ad8"},{"id":"occ_fb76b8159bf15077fa25330a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_ed5b6e9d-3f2d-48b8-a94c-00ad6956fcc7","section_id":"sec_cac8fc60-788b-428f-8aaa-593077b20024","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":65,"end":72,"exact":"KVキャッ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           ↓\n各層のKey・Valueを生成\n            ↓\nKVキャッシュを作成\n            ↓\n最初の出力トークンを決める\n```","quote_start":57,"quote_end":154,"text_sha256":"359aff4688a4afb508a6f16eb9862ce9e4b7e8003c4009a130596cb3c8a0f1be","block_sha256":"359aff4688a4afb508a6f16eb9862ce9e4b7e8003c4009a130596cb3c8a0f1be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_efe569b4-d433-4434-9bad-c452dae7c392"},{"id":"occ_181e0b876c5f51c19a30f441","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f0aa52c7-cd78-4bfb-b7d5-cee90a25645b","section_id":"sec_f99ecbf1-dc59-418f-b316-affc93bd2543","layer":"body","character_id":null,"count":3,"matched_aliases":["Decode","KVキャッシュ","Prefill"],"evidence":{"text_basis":"markdown","start":21,"end":28,"exact":"Prefill","quote":"近接メモリーだけでは、複数WSEの接続や、PrefillシステムからDecodeシステムへのKVキャッシュ転送問題は残る。","quote_start":0,"quote_end":61,"text_sha256":"3e5f89b727bca8bd87ae50bd7537a070d4bda682d274d62c5994e6722781e510","block_sha256":"3e5f89b727bca8bd87ae50bd7537a070d4bda682d274d62c5994e6722781e510","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f0aa52c7-cd78-4bfb-b7d5-cee90a25645b"},{"id":"occ_68e5e809ce13e23112617b14","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f3e42c9c-14d3-4ed2-8ba7-82cb87e51897","section_id":"sec_7cc13830-f551-4535-b386-3528507a1952","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":3,"end":5,"exact":"推論","quote":"## 推論エンジンは何をしているのか","quote_start":0,"quote_end":18,"text_sha256":"8848c941a21ebe94734fd33bf93772b9d01dcb9bd8c610e2a46c94c2f39788c1","block_sha256":"8848c941a21ebe94734fd33bf93772b9d01dcb9bd8c610e2a46c94c2f39788c1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f3e42c9c-14d3-4ed2-8ba7-82cb87e51897"},{"id":"occ_308700509ffded96f8dedab5","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f6d93e02-a661-431c-af35-a0bd0218882e","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"code","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":4,"end":11,"exact":"Prefill","quote":"```\nPrefill設備：少ない\nDecode設備 ：多い\n```","quote_start":0,"quote_end":34,"text_sha256":"062fe776c683ab7fb07f13be0ea28cee174604dcd6faf406e151056e96706b9b","block_sha256":"062fe776c683ab7fb07f13be0ea28cee174604dcd6faf406e151056e96706b9b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f6d93e02-a661-431c-af35-a0bd0218882e"},{"id":"occ_e362a6b8caa024a306457abc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f8187cc9-af4f-4c6b-9ad4-6b264840f1b4","section_id":"sec_02e4a9ca-aefd-467c-85c8-bb94bff16843","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":67,"end":74,"exact":"Prefill","quote":"ではない。HBMはローカルメモリーであり、光I/Oは外部装置へ接続する経路である。それでも、遠隔HBMプール、Prefillアクセラレーター、別のWSEとの接続を高速化する中核技術になり得る。","quote_start":12,"quote_end":108,"text_sha256":"03c125d477835ce259d00ddc052d117fa503af87d928197478e1e8ec7a38dfa0","block_sha256":"03c125d477835ce259d00ddc052d117fa503af87d928197478e1e8ec7a38dfa0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f8187cc9-af4f-4c6b-9ad4-6b264840f1b4"},{"id":"occ_6783b840a23ee268bcbbb2a0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f9543d25-8270-47d3-9b43-62ec2848ae2a","section_id":"sec_5d4aa299-3fa1-4d89-83e4-6c878b1c3d82","layer":"body","character_id":null,"count":2,"matched_aliases":["Decode","Prefill"],"evidence":{"text_basis":"markdown","start":9,"end":16,"exact":"Prefill","quote":"> OpenAIもPrefillを別チップ、DecodeをCerebrasへ分けている","quote_start":0,"quote_end":43,"text_sha256":"b1a1fa77c9e47cb8318f88f5eedf99507dc94f21e8a8f95668aa333fd23fb883","block_sha256":"b1a1fa77c9e47cb8318f88f5eedf99507dc94f21e8a8f95668aa333fd23fb883","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f9543d25-8270-47d3-9b43-62ec2848ae2a"},{"id":"occ_3abf3b3873c6dce5ff826913","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_f9ee4519-0ac2-4bc4-b9dd-f628694331e5","section_id":"sec_9b635b0c-07c5-4823-9acb-8377cca246c5","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":8,"end":10,"exact":"推論","quote":"コード生成や長い推論では、入力より出力が長くなる。","quote_start":0,"quote_end":25,"text_sha256":"8fa0e94c15b85164cbbf9672fdfae4c406019c8ed1e08d7805e9242fb884ae62","block_sha256":"8fa0e94c15b85164cbbf9672fdfae4c406019c8ed1e08d7805e9242fb884ae62","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_f9ee4519-0ac2-4bc4-b9dd-f628694331e5"},{"id":"occ_8be35d4e8bf2612e89f03092","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_fe87d78a-93bf-46ef-8021-d3df87b9f6c6","section_id":"sec_0fd41a36-e7bc-44f6-aee0-9b2be9d051aa","layer":"code","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":269,"end":276,"exact":"Prefill","quote":"\n          │\n          │ 光scale-up接続\n          ↓\n別のWSE／Prefill ASIC／共有メモリー\n```","quote_start":214,"quote_end":292,"text_sha256":"daba8c5b57d1881fc83c9d1c584aeae106646823a2391177ec79be7f05275398","block_sha256":"daba8c5b57d1881fc83c9d1c584aeae106646823a2391177ec79be7f05275398","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_fe87d78a-93bf-46ef-8021-d3df87b9f6c6"},{"id":"occ_7273f2d0e4d2aa8d8684243d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332","work_id":"wrk_d32481f4-3a5a-40d8-a817-86b115c8c4f4","block_id":"blk_ff5af08a-10e4-4b1f-a131-50fa38afb151","section_id":"sec_6a46bb3c-683b-4286-8b8b-7e69c042dddb","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":18,"end":24,"exact":"Decode","quote":"## Amazon案件で明確になったDecode専用戦略","quote_start":0,"quote_end":28,"text_sha256":"842d36fb8e5275be3aec23b437420ac66c45d96b5ddf2c7f236370efb93c5850","block_sha256":"842d36fb8e5275be3aec23b437420ac66c45d96b5ddf2c7f236370efb93c5850","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f3a5d7c9-6dfa-4c38-8bf2-a37063801332/#blk_ff5af08a-10e4-4b1f-a131-50fa38afb151"},{"id":"occ_938a776fc77d2dfcfd65f976","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_0000fc36-825d-42c9-91bc-ab0827cbb908","section_id":"sec_cfe54c7a-fb28-4e0b-a7a8-8772cfdf480b","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":105,"end":107,"exact":"推論","quote":"・データセンター寄り** だということです。Groq 自身が LPU を data centers での低遅延推論、GroqCloud、on-prem solutions として展開しているので、現時点では **車載の常時制御チップ** より **近傍サーバー側の生成AI推論器** と見る方が自然です。 ([Gr","quote_start":50,"quote_end":207,"text_sha256":"ce1546a88e43e207b6868560540667d42755f89493f07a0462917e745bd5eedb","block_sha256":"ce1546a88e43e207b6868560540667d42755f89493f07a0462917e745bd5eedb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_0000fc36-825d-42c9-91bc-ab0827cbb908"},{"id":"occ_b1e58eeb23d01ecf67d4818b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_03b9797b-60bc-449c-8fcf-01baab352a36","section_id":"sec_1a939ca6-d324-48ca-ab56-4cbc4b72c019","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":131,"end":133,"exact":"推論","quote":"のような基盤半導体株は 0～12カ月で数字が出やすい**です。AI-RANが立ち上がらなくても、AI工場や分散推論向けのGPU/NIC/光が先に売れるからです。 ([NVIDIA Blog](https://blogs.nvidia.com/blog/telecom-ai-grids-inference/?utm","quote_start":76,"quote_end":233,"text_sha256":"cfa2e2baa84e53da2991d43a189a658bded313069a38938da7a03318dfbaaac3","block_sha256":"cfa2e2baa84e53da2991d43a189a658bded313069a38938da7a03318dfbaaac3","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_03b9797b-60bc-449c-8fcf-01baab352a36"},{"id":"occ_3438564fe781456fc9eba566","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_0d931e4b-34f8-4d51-8173-ad9b11e1f5e7","section_id":"sec_c35d93ba-c225-4661-a385-81d447be6f60","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":23,"end":25,"exact":"推論","quote":"**MECエッジ層**  \nここはRAN近傍で推論を受け持つ層で、超低遅延・高帯域・無線状態の活用が効く。AI-RANの収益化ポイントはここで大きく、単なる回線販売ではなく、「必要なときに必要な品質でAIを動かせる接続」を売れるようになる。AI-RA","quote_start":0,"quote_end":125,"text_sha256":"de7b52a5a659a4f1abf30c7b44ca65c57538e53f274e28e179617a71542bdcc6","block_sha256":"de7b52a5a659a4f1abf30c7b44ca65c57538e53f274e28e179617a71542bdcc6","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_0d931e4b-34f8-4d51-8173-ad9b11e1f5e7"},{"id":"occ_1e4b2956bb810b2d53c05dc8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_1907f049-f1d5-43a2-886d-97baad6c9604","section_id":"sec_15c379c2-27b8-4e1e-9897-795e5aef8b2b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":134,"end":136,"exact":"推論","quote":"L for NG-RAN & 5G-Advanced towards 6G** として、RAN内のデータ収集や推論支援の標準化を進めています。NokiaもAI-RANの狙いとして、**AIネイティブなアプリに必要な guaranteed ultra-low latency と massive uplink cap","quote_start":79,"quote_end":236,"text_sha256":"84c2cef73c3d0f943f7eeb64d810201aad205606b5300b91feaaf8c1d50a24b9","block_sha256":"84c2cef73c3d0f943f7eeb64d810201aad205606b5300b91feaaf8c1d50a24b9","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_1907f049-f1d5-43a2-886d-97baad6c9604"},{"id":"occ_2c2305f0ff0b0519a34d044c","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_19f0b695-bb86-4266-be7c-93caceb42759","section_id":"sec_41ae5a1c-5378-4b4c-8970-d2871d8a64b4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":48,"end":50,"exact":"推論","quote":"私の考えでは、AI-RAN の核心は **「基地局にAIを載せること」ではなく、「通信網を“分散推論インフラ”へ変えること」** です。  \nその意味で、AI-RANは単なるRAN高度化ではありません。無線、エッジ、データセンター、オーケストレーション、セキュリティ、認証、課金、SLAが1つの面にな","quote_start":0,"quote_end":150,"text_sha256":"14716a0c80289e03e3b094b4f0fceb5a797f124ef270fbccde27eabe1831f811","block_sha256":"14716a0c80289e03e3b094b4f0fceb5a797f124ef270fbccde27eabe1831f811","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_19f0b695-bb86-4266-be7c-93caceb42759"},{"id":"occ_a8ee291741052bb8aa646ffe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_1a9a938d-9fa5-49ca-84a9-fc86ed6ce109","section_id":"sec_83b2f7ee-35bf-4772-a402-b5c55311c87f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":96,"end":98,"exact":"推論","quote":"た方がよいです。しかも実際には、1個のスマホみたいな箱が全部やるというより、**制御用の小さい計算機と、認識・推論用の少し強い計算機が同居する** ことが多いです。NVIDIAも Jetson Thor を「physical AI and robotics 向けのコンパクトな高性能計算基盤」として出していて、エッ","quote_start":41,"quote_end":198,"text_sha256":"c8bc5f8fd960d382c280864a27a6d95fe113e7fde810dc316e5473fb2bc5f0cb","block_sha256":"c8bc5f8fd960d382c280864a27a6d95fe113e7fde810dc316e5473fb2bc5f0cb","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_1a9a938d-9fa5-49ca-84a9-fc86ed6ce109"},{"id":"occ_9b7cef4bcd35c468717c0b40","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_2f431d0f-282c-46e3-a50c-8f881f56b328","section_id":"sec_e1d96629-dfc1-4d25-a2bb-4344ba666950","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":386,"end":388,"exact":"推論","quote":"すいので、自動運転車より“近くの外部脳”に頼りやすいわけです。後半は上記実証と製造向けエッジ基盤の説明に基づく推論です。 ([Siemens](https://www.siemens.com/ja-jp/products/industrial-edge/))","quote_start":331,"quote_end":460,"text_sha256":"1a4b74e6275ded11aa89c20ecebd8290f7216a170721c79484651f60ebf4867a","block_sha256":"1a4b74e6275ded11aa89c20ecebd8290f7216a170721c79484651f60ebf4867a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_2f431d0f-282c-46e3-a50c-8f881f56b328"},{"id":"occ_bd5ddb033aa7c76540ec1257","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_393f1146-2cfb-477d-af53-83fe27d7ce4b","section_id":"sec_c35d93ba-c225-4661-a385-81d447be6f60","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":72,"end":74,"exact":"推論","quote":"ここは工場、倉庫、発電所、病院のように、止められない現場のための層だ。私設5Gや有線産業ネットワーク、ローカル推論、データ保全、OT連携が主役になる。SiemensはAI-readyな産業用データセンターにNVIDIAの加速計算とPalo AltoのAI向けサイバー防御を組み込む構成を打ち出しており、受益者は産業","quote_start":17,"quote_end":174,"text_sha256":"ddcd1a399475ca1de5cb570e3e2815cecfe3f6632736e97ecf0542070523280b","block_sha256":"ddcd1a399475ca1de5cb570e3e2815cecfe3f6632736e97ecf0542070523280b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_393f1146-2cfb-477d-af53-83fe27d7ce4b"},{"id":"occ_8160056a9641e24394afda98","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_41796208-2044-4462-a1a1-e2842f59db3d","section_id":"sec_2f911c6e-635c-4c8d-a2c5-c209474d1256","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":177,"end":186,"exact":"inference","quote":"static scheduling と deterministic execution を持つ **fast inference** のための構成として説明していること、Qualcomm が NPU を **on-device / low-power / always-on** に寄せていること、Mobileye が EyeQ","quote_start":122,"quote_end":286,"text_sha256":"78000f1237e1165404050bf6540a9f3b054190294b0bc70b271d86080d5350d2","block_sha256":"78000f1237e1165404050bf6540a9f3b054190294b0bc70b271d86080d5350d2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_41796208-2044-4462-a1a1-e2842f59db3d"},{"id":"occ_5e8f1e27bb4e3604c96a2e3f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_492fd22d-8f2b-4a54-9ad6-420b86fc7573","section_id":"sec_ea9025dc-9e1d-4cbe-a2e9-3b3054691130","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":2,"end":4,"exact":"推論","quote":"**推論サーバーの脳** は Groq と、一部の NVIDIA、AWS、Google です。ここは車やロボットの中ではなく、MEC、オンプレ、クラウドで重い推論を回す側です。 ([Groq](https:/","quote_start":0,"quote_end":104,"text_sha256":"a476ee6ee753e741e003b7ecc47eb9251d7d94fe9dcc6b7b806b83c14667dea1","block_sha256":"a476ee6ee753e741e003b7ecc47eb9251d7d94fe9dcc6b7b806b83c14667dea1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_492fd22d-8f2b-4a54-9ad6-420b86fc7573"},{"id":"occ_734e79c1c45d24a4e9684487","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_4943ed23-4e3e-4fb9-9ca3-14ca95d4b0b6","section_id":"sec_3b265602-31bc-4756-a29f-21ab4a228fa4","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":109,"end":111,"exact":"推論","quote":"**、Q2見通しが **$10.7B** です。ただしこの中にはAI networkingも入るため、全部を“推論ASIC”には入れません。それでも、Broadcomのcustom AI accelerators、Marvellのcustom XPU / XPU attach、AWSのTrainium/Infer","quote_start":54,"quote_end":211,"text_sha256":"c41cde04af7a37b10c1362bb42827ec665e9d906cb52b506fa4c4e072275b0c5","block_sha256":"c41cde04af7a37b10c1362bb42827ec665e9d906cb52b506fa4c4e072275b0c5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_4943ed23-4e3e-4fb9-9ca3-14ca95d4b0b6"},{"id":"occ_d0c30c47924e584651e62efc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_4a2e5964-206c-4862-b341-7f9a43637067","section_id":"sec_15c379c2-27b8-4e1e-9897-795e5aef8b2b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":166,"end":168,"exact":"推論","quote":"を想定した製品です。つまりMECは、巨大GPUを何百枚も並べる場というより、**比較的電力を抑えたGPUや専用推論器を、必要な場所に小さく分散配置する場** と考えると近いです。 ([NVIDIA](https://www.nvidia.com/en-us/data-center/l4/?utm_source=c","quote_start":111,"quote_end":268,"text_sha256":"d32fa325a862398b74713fd8912d18cf9c293b1ec63b17e2645fa3eb3adf0d0b","block_sha256":"d32fa325a862398b74713fd8912d18cf9c293b1ec63b17e2645fa3eb3adf0d0b","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_4a2e5964-206c-4862-b341-7f9a43637067"},{"id":"occ_11ec037f16b35d7e31a07873","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_4b351b94-cabb-45db-8cee-69cd2af69d50","section_id":"sec_6043ee41-c56d-481a-b47f-9ed798f6904d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":344,"end":346,"exact":"推論","quote":"えると、2026年のHBM売上ポケットを **$20B–$35B** と置くのが自然です。これは会社開示からの推論です。 ([Micron Technology](https://investors.micron.com/news-releases/news-release-details/micron-tech","quote_start":289,"quote_end":446,"text_sha256":"9cfe7726049ef7bcf18976f184cd3887a97b5d61933a5263151cf6f6806492ab","block_sha256":"9cfe7726049ef7bcf18976f184cd3887a97b5d61933a5263151cf6f6806492ab","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_4b351b94-cabb-45db-8cee-69cd2af69d50"},{"id":"occ_3063c9f635a91e780a046771","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_51e03c67-cc41-4258-8106-9e35de68b1a4","section_id":"sec_f908e7d3-5fb2-4a8b-932d-33f3b3584ecc","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":132,"end":134,"exact":"推論","quote":"るとは言えません。私の見立てでは、HBFは近い将来、HBMを食い尽くすというより、**“HBMだけでは足りない推論容量”を増やして総メモリ需要を広げる** 方向で効く可能性が高いです。これはSandisk/Kioxiaの公開情報に基づく推論です。 ([Sandisk](https://www.sandisk.co","quote_start":77,"quote_end":234,"text_sha256":"350bdd34697f253fd332dfa0756afa49524196ec8ce108ef1e3239ef7c7df531","block_sha256":"350bdd34697f253fd332dfa0756afa49524196ec8ce108ef1e3239ef7c7df531","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_51e03c67-cc41-4258-8106-9e35de68b1a4"},{"id":"occ_b587f9a111c6911e23ed798d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_56f4fef8-82f1-4318-b61d-6849033085d0","section_id":"sec_15c379c2-27b8-4e1e-9897-795e5aef8b2b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"っきり出しています。SoftBankは **基地局付近のAI-RAN/Regional Brain** で近い推論を回し、**Brain DataCenter** で大規模学習のような重すぎる処理を担う構想を示しています。さらに、従来クラウドがインターネット経由で数百ms往復していたのに対し、**基地局付近でAI","quote_start":27,"quote_end":184,"text_sha256":"16cf1784a5b6a763827d67ff14628ad609d2a6c03a1a1a9dc7e7b3c8860a8246","block_sha256":"16cf1784a5b6a763827d67ff14628ad609d2a6c03a1a1a9dc7e7b3c8860a8246","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_56f4fef8-82f1-4318-b61d-6849033085d0"},{"id":"occ_84f504e133b297c2701a89fe","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_5b4c8129-0456-45d8-b08d-80aed0c109ca","section_id":"sec_6f3a950d-ea6c-4c7f-b688-44b1e1b6b07e","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":78,"end":80,"exact":"推論","quote":" / Google Distributed Cloud Edge** 側で生まれる需要は、**MEC/オンプレ推論の低遅延メモリとローカルストレージ** です。ここではHBM級の高速メモリが一部必要で、同時にモデル・KV・ローカル知識の容量層も要るので、将来的にHBF的な階層がはまりやすいです。これは公開情報から","quote_start":23,"quote_end":180,"text_sha256":"056351708c8b29a560f35944d2e4a6241cf9bcb202f98c46aa23bc6a6713d484","block_sha256":"056351708c8b29a560f35944d2e4a6241cf9bcb202f98c46aa23bc6a6713d484","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_5b4c8129-0456-45d8-b08d-80aed0c109ca"},{"id":"occ_8656a889169fc6c9849b8371","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_5c1924a4-0594-448a-9b8e-f78bc993c326","section_id":"sec_754c558b-37f8-40ed-9bb9-5678c0ef922a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":4,"end":6,"exact":"推論","quote":"- **推論ASIC**：Groq、Broadcom custom、Marvell custom、AWS Inferentia/Trainium、Google TPU。\n- **SSD/HBF候補**：Kioxi","quote_start":0,"quote_end":106,"text_sha256":"3c82de4fcc009eae93750297299d95754d94f5414c9e2afaad41d32ad3f13b54","block_sha256":"3c82de4fcc009eae93750297299d95754d94f5414c9e2afaad41d32ad3f13b54","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_5c1924a4-0594-448a-9b8e-f78bc993c326"},{"id":"occ_3c5995c810d68bf4897fd655","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_5c514d14-7400-4f7a-bb9b-b4404f799bff","section_id":"sec_a3e620f2-6694-43f4-9be7-d3614f4901ca","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":233,"end":235,"exact":"推論","quote":"ラレータにもHBMが広がる** ため、HBM需要源はむしろ多極化していく可能性があります。これは公開情報からの推論です。 ([Amazon Web Services, Inc.](https://aws.amazon.com/ai/machine-learning/trainium/?utm_source=cha","quote_start":178,"quote_end":335,"text_sha256":"fedc87d3d17de6e896e1a91b592ccf3a704fa317dbcfa5a1c46a97b6f7814683","block_sha256":"fedc87d3d17de6e896e1a91b592ccf3a704fa317dbcfa5a1c46a97b6f7814683","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_5c514d14-7400-4f7a-bb9b-b4404f799bff"},{"id":"occ_3fe1adae1781965e964b6c43","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_5ce95b23-8ea7-45ef-9f39-e1f4ea6445d4","section_id":"sec_20652c04-e2cc-4425-97fa-dc1ad99e9ec7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":188,"end":190,"exact":"推論","quote":"ような遅延感度の高い用途を支えると整理されている。つまりフィジカルAIでは「モデルの賢さ」だけでなく、「どこで推論するか」が価値そのものになる。([ETSI](https://www.etsi.org/technical-groups/mec/))","quote_start":133,"quote_end":257,"text_sha256":"64ffb3d5ccb3aa3fb3bca5ddc241b8322f91064549261793bd736065429cfa17","block_sha256":"64ffb3d5ccb3aa3fb3bca5ddc241b8322f91064549261793bd736065429cfa17","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_5ce95b23-8ea7-45ef-9f39-e1f4ea6445d4"},{"id":"occ_5102085cc3220f4786952136","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_6483298e-02be-441d-ad72-c6570ebefabb","section_id":"sec_15c379c2-27b8-4e1e-9897-795e5aef8b2b","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":193,"end":195,"exact":"推論","quote":"070 FP4 TFLOPS** という、かなり強いエッジ向け計算モジュールです。つまりデバイス側でも相当重い推論はできますが、電力・熱・サイズはまだ厳しく制約されます。 ([NVIDIA](https://www.nvidia.com/en-us/autonomous-machines/embedded-sys","quote_start":138,"quote_end":295,"text_sha256":"d0c23e46d6ccbcdb403401120a48d55d6520353a1173d911463af1f067eff9ed","block_sha256":"d0c23e46d6ccbcdb403401120a48d55d6520353a1173d911463af1f067eff9ed","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_6483298e-02be-441d-ad72-c6570ebefabb"},{"id":"occ_7756853c5e4acb2ba51f610f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_68d138e0-b20a-4944-abd4-9cc009eed8af","section_id":"sec_aea549da-2262-4d36-b5c7-5d2114d394d9","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":197,"end":199,"exact":"推論","quote":"言で説明しづらい**。  \nだから、事業の強さの割に、株価材料としては“映えにくい”です。これは製品構造からの推論です。 ([Zscaler](https://www.zscaler.com/products-and-solutions/data-security))","quote_start":142,"quote_end":276,"text_sha256":"3432b0cf046d4db3ace82ef6a399978c6557758e452089bddd0a1353220e2241","block_sha256":"3432b0cf046d4db3ace82ef6a399978c6557758e452089bddd0a1353220e2241","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_68d138e0-b20a-4944-abd4-9cc009eed8af"},{"id":"occ_3e6e9a0f09da0dedc6da5526","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_691033e6-6ec2-47ad-a5e2-bfeaaa324e0d","section_id":"sec_1a939ca6-d324-48ca-ab56-4cbc4b72c019","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":71,"end":73,"exact":"推論","quote":"ント・工場OS・フィジカルAIの時代に、AI-RAN文脈で先に価値が乗りやすいのは「ロボット本体」よりも、分散推論を支える計算基盤・RANソフト・光/接続・工場の運用OS」側**です。AI-RANは、無線ネットワークをただの通信網ではなく、**RAN・エッジ・コアにAIを埋め込んだ分散AI基盤**へ変える発想で、","quote_start":16,"quote_end":173,"text_sha256":"6c20d20bf04b545d7b7d611e94fcd8bd13947056f9f112818114f03838189ee4","block_sha256":"6c20d20bf04b545d7b7d611e94fcd8bd13947056f9f112818114f03838189ee4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_691033e6-6ec2-47ad-a5e2-bfeaaa324e0d"},{"id":"occ_c2796e9038a6b72e382f9fa3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_7346318c-6bc2-46a9-a7fa-2d30b8cb2c2f","section_id":"sec_1a939ca6-d324-48ca-ab56-4cbc4b72c019","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":376,"end":378,"exact":"推論","quote":"*AIデータセンター需要**で利益見通しを引き上げています。AI-RANが本当に工場・物流・インフラ現場で分散推論の土台になるなら、最後に太く儲かるのは、通信会社だけではなく、**現場オペレーションを“AIで回すOS”を持つ会社**です。Siemensはその最有力です。 ([press.siemens.com](","quote_start":321,"quote_end":478,"text_sha256":"a9bae352e98d23ae08f762260c98b968134cef9bdc8eb6f41b7310ad52088506","block_sha256":"a9bae352e98d23ae08f762260c98b968134cef9bdc8eb6f41b7310ad52088506","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_7346318c-6bc2-46a9-a7fa-2d30b8cb2c2f"},{"id":"occ_0d5643a15f8ac8bcd2515a42","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_7526c283-5efa-4b62-bf7c-c9240c896590","section_id":"sec_07ccf819-f834-406b-b1df-9849605e45ab","layer":"body","character_id":null,"count":5,"matched_aliases":["inference","推論"],"evidence":{"text_basis":"markdown","start":92,"end":94,"exact":"推論","quote":"Q のような「特定用途向けにかなり作り込んだ専用チップ」の広い呼び方、**LPU** は Groq のような「推論、特に低遅延な生成AI推論に寄せた専用アーキテクチャ」、**NPU** は SoC内蔵や小型カードを含む「低電力なニューラル推論器」として使います。技術的には NPU も ASIC 的に実装されること","quote_start":37,"quote_end":194,"text_sha256":"ff8486e4199cfe4283157b4304f6b8469c0fbb6e3ed7d43061931e2f7ea17635","block_sha256":"ff8486e4199cfe4283157b4304f6b8469c0fbb6e3ed7d43061931e2f7ea17635","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_7526c283-5efa-4b62-bf7c-c9240c896590"},{"id":"occ_1783661a468050340bca241f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_792b5e20-b47d-4cd4-a613-3e5cfa7b1d0c","section_id":"sec_9a3701ea-1d25-4e2c-983b-247bee4a9b8a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":93,"end":95,"exact":"推論","quote":"lcomm / Tesla / Horizon / Hailo** は主に端末の脳。\n- **Groq** は推論サーバー脳。\n- **Amazon / Google** はAI工場と分散エッジ基盤。\n- **Broadcom / Marvell** はAI工場のネットワークとカスタム裏方。 ([NVIDIA](","quote_start":38,"quote_end":195,"text_sha256":"d2ebcb41377be4db33512f2656abcf0231fd3a30e9d3b065879e675f0ed8469c","block_sha256":"d2ebcb41377be4db33512f2656abcf0231fd3a30e9d3b065879e675f0ed8469c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_792b5e20-b47d-4cd4-a613-3e5cfa7b1d0c"},{"id":"occ_f279df67bacaeadb68a03a25","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_83cabdef-6303-4205-a6f7-3afd5d970ad6","section_id":"sec_a3e620f2-6694-43f4-9be7-d3614f4901ca","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":92,"end":94,"exact":"推論","quote":"* というより、まず **それらを学習・蒸留・世界モデル化する中央DC** と、次に **高級なエッジ/MEC推論サーバー** です。理由は、マルチカメラ、時系列、VLM/VLA、複数ロボット協調のような処理は、計算量より先に**メモリ帯域**が詰まりやすいからです。SandiskがHBFを売り込む文脈でも、AI","quote_start":37,"quote_end":194,"text_sha256":"d4cbf32a942e23f404fb60d16760aceabf9a4942b0fdcf364006d914459280a2","block_sha256":"d4cbf32a942e23f404fb60d16760aceabf9a4942b0fdcf364006d914459280a2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_83cabdef-6303-4205-a6f7-3afd5d970ad6"},{"id":"occ_f2e529e91efcff46ff8f4f3e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_96a484a2-4939-4ead-a3ed-0da3d7f14d80","section_id":"sec_179a7910-aa45-4257-a43d-2874e7278728","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":129,"end":131,"exact":"推論","quote":"まだ小さい。**  \nそして、**物理AIが本当に普及しても、最初に太るのは“端末そのもの”より“中央で学習・推論・同期を支える側”** です。NVIDIAのFY2026で Automotive が **$2.349B** に対して Data Center Compute が **$162.361B** という差","quote_start":74,"quote_end":231,"text_sha256":"e224ef18ede4b09223c81a5f338a869d65043374ff013e84c88003f8f10c6b8a","block_sha256":"e224ef18ede4b09223c81a5f338a869d65043374ff013e84c88003f8f10c6b8a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_96a484a2-4939-4ead-a3ed-0da3d7f14d80"},{"id":"occ_8099d9461e7af684f13b51f6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_980366bf-46d8-49de-861f-20cf037c659e","section_id":"sec_9a3701ea-1d25-4e2c-983b-247bee4a9b8a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":98,"end":100,"exact":"推論","quote":"で学習する側”と“その間をつなぐ側”が太りやすい** です。特にHBMは中央に、SSDは全層に、HBFは将来の推論容量層に効く、という見方がいちばんしっくりきます。 ([NVIDIA](https://www.nvidia.com/en-us/data-center/dgx-b200/?utm_source=ch","quote_start":43,"quote_end":200,"text_sha256":"8805748a8c851385f01d6f14546a6bb0fa8953bfb92819ec1ae8e29bb00dcfb1","block_sha256":"8805748a8c851385f01d6f14546a6bb0fa8953bfb92819ec1ae8e29bb00dcfb1","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_980366bf-46d8-49de-861f-20cf037c659e"},{"id":"occ_22ec1fe3148e479d7a8e0907","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_9ad79e64-e11f-4601-bde6-9d455b4119ff","section_id":"sec_9423de36-77aa-41b8-96d0-3aded4493994","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":47,"end":49,"exact":"推論","quote":"- 屋外ロボット\n- 自動運転支援\n- 都市のカメラ解析\n- 通信ネットワークと連携した低遅延推論\n- AI-RAN上のAI推論","quote_start":0,"quote_end":64,"text_sha256":"5dee8320a3e3613765d90cb7ee22f63d76332ea357c73eae87680c0b4e2c0658","block_sha256":"5dee8320a3e3613765d90cb7ee22f63d76332ea357c73eae87680c0b4e2c0658","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_9ad79e64-e11f-4601-bde6-9d455b4119ff"},{"id":"occ_6a5b2c8af3eab2793ac7f264","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_a5a3d371-1f40-4a17-bdb0-21fd0a58479f","section_id":"sec_83b2f7ee-35bf-4772-a402-b5c55311c87f","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":97,"end":99,"exact":"推論","quote":"→ マイコン、PLC、リアルタイム制御器\n- **本体内の上位計算機**  \n  カメラ認識、局所地図、簡単な推論、音声処理  \n  → 組み込みAIコンピュータ、産業用PC、Jetsonのようなモジュール\n- **本体外の近傍計算機**  \n  さらに重い推論や複数機協調  \n  → MECや工場内エッジサーバ","quote_start":42,"quote_end":199,"text_sha256":"008b24e389a2912345a65af6de5de9e78d43c0b26f426589a4c9ea1c2d36a0e7","block_sha256":"008b24e389a2912345a65af6de5de9e78d43c0b26f426589a4c9ea1c2d36a0e7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_a5a3d371-1f40-4a17-bdb0-21fd0a58479f"},{"id":"occ_2adfbaf63cecf9b5235fa44d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_a7cfcd5a-d5db-47d2-b30a-e270dd3af28f","section_id":"sec_e1d96629-dfc1-4d25-a2bb-4344ba666950","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":204,"end":206,"exact":"推論","quote":"ットワークが無くてもまず安全に走れる構成**が基本になります。これは上の公開情報から導ける、かなり強い実務的な推論です。 ([Waymo](https://waymo.com/waymo-driver/))","quote_start":149,"quote_end":252,"text_sha256":"40aa3a753192ac701e9fbea0a4a0a532dc636feed6e410046b1e7106344f0d17","block_sha256":"40aa3a753192ac701e9fbea0a4a0a532dc636feed6e410046b1e7106344f0d17","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_a7cfcd5a-d5db-47d2-b30a-e270dd3af28f"},{"id":"occ_7e2c8fbeaeadc980a33041e9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_ab40eb18-6600-4c8a-a044-d0afe58326f5","section_id":"sec_2d450fab-2fb2-4fd9-867e-65673b927463","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":173,"end":175,"exact":"推論","quote":"界、ゼロトラスト、修復自動化、AI自身の監査・制御、攻撃耐性を組み込んだ実行基盤** の側だと思います。これは推論ですが、Reuters の一連の報道が示している「規制当局・銀行・大手テックが Mythos をサイバー防衛の再設計問題として見ている」流れと整合的です。 ([Reuters](https://www","quote_start":118,"quote_end":275,"text_sha256":"7b994f843d70ebc504ab05034931fa82801381dbacd4ac89b785566c50a6937d","block_sha256":"7b994f843d70ebc504ab05034931fa82801381dbacd4ac89b785566c50a6937d","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_ab40eb18-6600-4c8a-a044-d0afe58326f5"},{"id":"occ_6c6e6dbcb2b93fa96b201ca8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_ab6e02ae-b994-4a7a-b192-33befe8f8cbd","section_id":"sec_f373b163-ed9c-4ce3-9793-6f866e1f25c7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":88,"end":90,"exact":"推論","quote":"assive MIMOを最適化する」は AI-for-RAN です。  \n「GPUサーバーの上でvRANとAI推論を両方走らせる」は AI-and-RAN です。  \n「AIグラス、ロボット、車両、ドローン向けに、エッジでAIを回しつつ uplink と低遅延を保証する」は AI-on-RAN です。AI-RAN","quote_start":33,"quote_end":190,"text_sha256":"c3be37c5cd3ea67cf41438a8ec640e6eeaf85e48ce125d76b7134c9fda84a47c","block_sha256":"c3be37c5cd3ea67cf41438a8ec640e6eeaf85e48ce125d76b7134c9fda84a47c","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_ab6e02ae-b994-4a7a-b192-33befe8f8cbd"},{"id":"occ_6393b35898326093ce268e64","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_ab91265c-8ff0-42a5-822e-4a02c6924b1a","section_id":"sec_83b2f7ee-35bf-4772-a402-b5c55311c87f","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":105,"end":107,"exact":"推論","quote":"ン、全体最適、長期記憶、モデル更新** に向いています。NVIDIAもロボティクスを、学習・シミュレーション・推論の三つの計算資源で語っており、物理AIはクラウドとエッジをまたぐ構成を前提にしています。 ([NVIDIA](https://www.nvidia.com/en-us/industries/robot","quote_start":50,"quote_end":207,"text_sha256":"dfc7009ba7fecee01528166709d9fe7128381ce2c70c5a27713936d9733a8e04","block_sha256":"dfc7009ba7fecee01528166709d9fe7128381ce2c70c5a27713936d9733a8e04","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_ab91265c-8ff0-42a5-822e-4a02c6924b1a"},{"id":"occ_3e011828363120b1262184d0","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_abf5e247-868d-45a0-9bf5-6ca39bb6e332","section_id":"sec_5a96d32e-ac76-47bc-8d53-93c707709b6c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":173,"end":175,"exact":"推論","quote":"owdStrike** の組み合わせになります。単独で全部を持つ会社はまだありません。これは各社の製品面からの推論です。 ([crowdstrike.com](https://www.crowdstrike.com/en-us/platform/charlotte-ai/?utm_source=chatgpt.c","quote_start":118,"quote_end":275,"text_sha256":"5d6cf25bfd349f5368116292a3b8a6eb2e9647baef75ec5570e86d90d0ffb6f2","block_sha256":"5d6cf25bfd349f5368116292a3b8a6eb2e9647baef75ec5570e86d90d0ffb6f2","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_abf5e247-868d-45a0-9bf5-6ca39bb6e332"},{"id":"occ_5d950b6b43c538034bf06991","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_b0ab6c83-c106-4900-b89f-cf663064b253","section_id":"sec_1a939ca6-d324-48ca-ab56-4cbc4b72c019","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":45,"end":47,"exact":"推論","quote":"強気シナリオでは、AIエージェントが現場へ降りてきて、ロボットや設備が常時ネットワーク側の推論資源を借りるようになります。その場合、**NVIDIA 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Siemens**の順に大きく恩恵を受けます。NVIDIAは計算基盤、Nokia/Eri","quote_start":0,"quote_end":147,"text_sha256":"854e4bf74bfb3b6f982d1934e48b9777961d568c017acc0af74fad3f6cac058a","block_sha256":"854e4bf74bfb3b6f982d1934e48b9777961d568c017acc0af74fad3f6cac058a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_b0ab6c83-c106-4900-b89f-cf663064b253"},{"id":"occ_43277c8e4d4cf744b0eea2af","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_b92f8f02-0ec3-497e-9ffa-755403ad57c2","section_id":"sec_d804928a-d2fc-4c8a-9708-9a5250cd7da2","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":254,"end":256,"exact":"推論","quote":"一つの場所で扱えます。さらに 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\nたとえば、複数カメラの認識、VLM/VLA系の推論、周辺状況の理解、経路計画、複数ロボットの協調、ローカル地図の更新などです。SoftBankとEricssonは、ロボットと通信ネットワークと外部計算資源を統合制御し、必要に応じてAI処理を近傍MEC","quote_start":31,"quote_end":188,"text_sha256":"0eda8d27d5cb20c899afc2765b049cc351e6a2a0a92009d4f38b0552dc61bde5","block_sha256":"0eda8d27d5cb20c899afc2765b049cc351e6a2a0a92009d4f38b0552dc61bde5","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_bc26b0ce-178a-4598-b149-95a9d3f25275"},{"id":"occ_97453b107fde491c1360e512","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_bcb70b73-f287-4db0-b2a8-fb3c3111d740","section_id":"sec_83789c2e-c44b-48f4-91d6-753c24e14228","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":81,"end":83,"exact":"推論","quote":"層、Mobileye/Qualcomm/Tesla/Horizon/Hailoは主に“端末の脳”、Groqは“推論サーバー脳”、Amazon/Googleは“AI工場とエッジ基盤”、Broadcom/Marvellは“その全部をつなぐ血管と裏方シリコン”** を取りに行っています。なお、あなたの「Marvel」は","quote_start":26,"quote_end":183,"text_sha256":"4a0143f7319cb404e704e1f7a595f2f0934d14bb803f0a4d4cf2998603522a78","block_sha256":"4a0143f7319cb404e704e1f7a595f2f0934d14bb803f0a4d4cf2998603522a78","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_bcb70b73-f287-4db0-b2a8-fb3c3111d740"},{"id":"occ_ccda134c9a4afccb0cb924de","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_c6ac6716-6a8c-4dcc-9aff-9b17cc6d89c9","section_id":"sec_3b265602-31bc-4756-a29f-21ab4a228fa4","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":7,"end":9,"exact":"推論","quote":"### 7) 推論ASIC / カスタムXPU","quote_start":0,"quote_end":23,"text_sha256":"3dfea57d0c612816f0c9cc653e09c91f1ea3f7309ef95c1a671867d48e3d6d72","block_sha256":"3dfea57d0c612816f0c9cc653e09c91f1ea3f7309ef95c1a671867d48e3d6d72","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_c6ac6716-6a8c-4dcc-9aff-9b17cc6d89c9"},{"id":"occ_10a2d070fc29f8b395f2257b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_d00f736f-1b1c-43f8-8b25-f04772a1a0ca","section_id":"sec_f908e7d3-5fb2-4a8b-932d-33f3b3584ecc","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":174,"end":176,"exact":"推論","quote":"けに**persistence and thermal stability** を強調しています。つまり、巨大推論で「HBMだけでは高すぎる・少なすぎる、SSDだけでは遅すぎる」という場所に入ろうとしているわけです。 ([Sandisk](https://www.sandisk.com/company/newsr","quote_start":119,"quote_end":276,"text_sha256":"ee2e398634d820c358c721648d32429822ce1b491b38e2b84e941f408fd5550f","block_sha256":"ee2e398634d820c358c721648d32429822ce1b491b38e2b84e941f408fd5550f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_d00f736f-1b1c-43f8-8b25-f04772a1a0ca"},{"id":"occ_e78a4137087e465237b168a8","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_d0dd5e84-3d9f-46a6-a3f9-c66674d9027d","section_id":"sec_c728c1c7-ecaf-4f87-a293-ae46e371290a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":138,"end":140,"exact":"推論","quote":"録** になったと述べています。つまり、AIインフラではHBMだけでなく、**大容量データ、チェックポイント、推論キャッシュ、ベクトルDB、ログ** を支えるSSD/NANDも着実に太っています。ここにSamsung、SK hynix/Solidigm、Kioxia、SanDiskが乗るので、AI由来のSSD/フ","quote_start":83,"quote_end":240,"text_sha256":"60501b7131c5ceb7afa3fed4917c1afd35e64a68270ddc30161c4727061e16be","block_sha256":"60501b7131c5ceb7afa3fed4917c1afd35e64a68270ddc30161c4727061e16be","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_d0dd5e84-3d9f-46a6-a3f9-c66674d9027d"},{"id":"occ_20f65ce39dcf84267385ab5a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_d0dd9acf-7556-40c8-a484-84c75c485bb1","section_id":"sec_c3836a09-c36a-401e-b0d8-62ef2fb692c7","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":93,"end":95,"exact":"推論","quote":"U/MCU/PLC/安全制御器が中心軽量・常時AINPUNPU重い知覚・世界理解GPUGPU固定化しやすい量産推論ASICNPU/ASICMEC上の生成AILPULPU学習・シミュレーションGPUGPU","quote_start":38,"quote_end":139,"text_sha256":"d7f6d6d94ea4c67249eb3b27579082b94a51203d84e14330bc37361e57813c6f","block_sha256":"d7f6d6d94ea4c67249eb3b27579082b94a51203d84e14330bc37361e57813c6f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_d0dd9acf-7556-40c8-a484-84c75c485bb1"},{"id":"occ_311e08bffc3c82ec1791984f","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_d58acf2f-200a-4983-ae67-d82415087963","section_id":"sec_f908e7d3-5fb2-4a8b-932d-33f3b3584ecc","layer":"body","character_id":null,"count":2,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":26,"end":28,"exact":"推論","quote":"物理AIの文脈では、HBFは特に **MEC/エッジ推論** や **大規模推論サーバー** と相性が良い可能性があります。なぜなら、ロボットや車の近くで重いモデルを動かすときは、中央DCほど潤沢な電力や冷却がなく、しかしSSDだけでは足りないからです。K","quote_start":0,"quote_end":128,"text_sha256":"61f0c3d6edcff5c34cae04b65b4c2820a38383aead0fcfdfa29c81e5ec966c0e","block_sha256":"61f0c3d6edcff5c34cae04b65b4c2820a38383aead0fcfdfa29c81e5ec966c0e","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_d58acf2f-200a-4983-ae67-d82415087963"},{"id":"occ_f7f2abb0c29b7f611d64c875","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_d975a086-a654-41a0-9213-0b892b259f1a","section_id":"sec_59c3afc2-b470-4440-9dea-183c913e44c5","layer":"body","character_id":null,"count":1,"matched_aliases":["inference"],"evidence":{"text_basis":"markdown","start":139,"end":148,"exact":"inference","quote":"-to-model traffic です。Cloudflare は AI Gateway を unified inference layer に拡張し、Workers AI でモデル実行を持ち、Access で AI agents 向け Managed OAuth を出し、MCP governance まで用意しています。つま","quote_start":84,"quote_end":248,"text_sha256":"90b59a4736eb76ecf267af699daf0d1e30cc93b8e513d7bb89cce54ea6626eb4","block_sha256":"90b59a4736eb76ecf267af699daf0d1e30cc93b8e513d7bb89cce54ea6626eb4","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f487aa08-2568-42ad-85cb-11c258b5b71d/#blk_d975a086-a654-41a0-9213-0b892b259f1a"},{"id":"occ_2178ca83578ae470461f3304","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f487aa08-2568-42ad-85cb-11c258b5b71d","work_id":"wrk_a5567405-5ef5-4c25-abcd-63351a43cdfd","block_id":"blk_dc23d068-dab0-4340-8670-a665b33003d3","section_id":"sec_1a939ca6-d324-48ca-ab56-4cbc4b72c019","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":124,"end":126,"exact":"推論","quote":"S」\\*\\*です。  \nこのテーマでいちばん危ないのは、**physical AI本体だけを見て、その下の分散推論・ネットワーク・運用OSを見落とすこと**です。価値はまず下の基盤に落ち、あとから上のアプリに伝わります。 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inference technology licenseを締結し、2026年8月にはNVIDIA Cloud Partnerにもなった。([Groq](https://groq.com/newsroom/groq-be","quote_start":0,"quote_end":138,"text_sha256":"9e12a257a6c32dd4a43a8cb1bebdbee68020ef3ba2c7fb2411ecb7f28ad18a74","block_sha256":"9e12a257a6c32dd4a43a8cb1bebdbee68020ef3ba2c7fb2411ecb7f28ad18a74","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a/#blk_a9d7fd84-a55b-41aa-8cad-b9dd0716fb36"},{"id":"occ_af82ddae74854362d9c7ad2e","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a","work_id":"wrk_72f5f141-da8e-4f5b-a337-f46180c37f2e","block_id":"blk_c01768dc-dd6d-4f2d-a126-68cf16e18572","section_id":"sec_b54c4d58-fa85-45fd-bf01-fab2f089b664","layer":"body","character_id":null,"count":1,"matched_aliases":["Prefill"],"evidence":{"text_basis":"markdown","start":10,"end":17,"exact":"Prefill","quote":"どのComputeがPrefillをするか。","quote_start":0,"quote_end":22,"text_sha256":"144d1174c30a5d7ef256c279e2ad8764bf26b5e199d2cc8aa8ba1d724e82c2ac","block_sha256":"144d1174c30a5d7ef256c279e2ad8764bf26b5e199d2cc8aa8ba1d724e82c2ac","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a/#blk_c01768dc-dd6d-4f2d-a126-68cf16e18572"},{"id":"occ_8682e2c1ab2c0ecadb42ccae","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a","work_id":"wrk_72f5f141-da8e-4f5b-a337-f46180c37f2e","block_id":"blk_c133d351-0a8b-472e-8780-861a4e6debc1","section_id":"sec_2de441c4-4d36-4cef-8cba-53c9a390dec6","layer":"code","character_id":null,"count":3,"matched_aliases":["Decode","Inference","Prefill"],"evidence":{"text_basis":"markdown","start":15,"end":22,"exact":"Prefill","quote":"```\nTraining 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3D","quote_start":0,"quote_end":121,"text_sha256":"d0317e48e4b1d07ef5f65318f00612f0383f8a1fc5a3cb0eff562f64288a732a","block_sha256":"d0317e48e4b1d07ef5f65318f00612f0383f8a1fc5a3cb0eff562f64288a732a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a/#blk_e10c3be9-d7e3-4588-ba34-eedf5f9238f7"},{"id":"occ_449cda9af3bfd8e52cf58617","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a","work_id":"wrk_72f5f141-da8e-4f5b-a337-f46180c37f2e","block_id":"blk_e2139616-7276-4a54-9ee4-be6a5e151682","section_id":"sec_b1a19885-b2ae-4a09-b4df-6ddafb19473f","layer":"body","character_id":null,"count":1,"matched_aliases":["Decode"],"evidence":{"text_basis":"markdown","start":0,"end":6,"exact":"Decode","quote":"Decodeでは1token生成するたびWeightやKVを読み、Memory trafficが発生する。","quote_start":0,"quote_end":53,"text_sha256":"6e71e682712128fd068197dcf37b174a0590d5434a501b9c492c9773c2a88354","block_sha256":"6e71e682712128fd068197dcf37b174a0590d5434a501b9c492c9773c2a88354","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a/#blk_e2139616-7276-4a54-9ee4-be6a5e151682"},{"id":"occ_7559bdf7adefa05878689494","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a","work_id":"wrk_72f5f141-da8e-4f5b-a337-f46180c37f2e","block_id":"blk_e2b4f1b7-fab8-44e6-bd66-8a6121a4310d","section_id":"sec_52abc8ff-396c-4413-a079-2037df5d8a43","layer":"body","character_id":null,"count":1,"matched_aliases":["Inference"],"evidence":{"text_basis":"markdown","start":22,"end":31,"exact":"Inference","quote":"## 16．GroqはChip企業というよりInference Cloud企業になりつつある","quote_start":0,"quote_end":46,"text_sha256":"37eb67874434095a14a4f1f10a16862a9f413885ef6a5fb8608c457f91df7425","block_sha256":"37eb67874434095a14a4f1f10a16862a9f413885ef6a5fb8608c457f91df7425","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a/#blk_e2b4f1b7-fab8-44e6-bd66-8a6121a4310d"},{"id":"occ_2c3a2b68e63b61e4f2d52944","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_f76185ef-dd8b-44e4-a82a-7b8cc8225b9a","work_id":"wrk_72f5f141-da8e-4f5b-a337-f46180c37f2e","block_id":"blk_efd85e1f-9876-490d-bacd-8e86626d9215","section_id":"sec_fd8570f4-91e2-4e92-9e2d-085db0232f52","layer":"body","character_id":null,"count":1,"matched_aliases":["decode"],"evidence":{"text_basis":"markdown","start":37,"end":43,"exact":"decode","quote":"さらにCustom HBMのlogic base dieへ高速token 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HBM3E）は、長いシーケンス長のKVキャッシュを扱うOpenAI O3スタイルのLLM推論トレーニングに特に重要です。","quote_start":82,"quote_end":180,"text_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","block_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_4c1d3126-7e27-4347-9cf9-a16271325569"},{"id":"occ_4348f55c66eafa64318e58d6","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64","work_id":"wrk_e8cd9e41-ade7-4f50-b16e-dc0e90202415","block_id":"blk_728d7236-335f-4f79-b518-00b0a4acf63a","section_id":"sec_af8f606c-1149-4602-ba9d-7b1c504e61dc","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":137,"end":144,"exact":"KVキャッシュ","quote":"Lによる高密度パッケージングの結果です。また、メモリ容量の増加（288GB HBM3E）は、長いシーケンス長のKVキャッシュを扱うOpenAI O3スタイルのLLM推論トレーニングに特に重要です。","quote_start":82,"quote_end":180,"text_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","block_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_728d7236-335f-4f79-b518-00b0a4acf63a"},{"id":"occ_392249d43b546dd3119d3afb","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64","work_id":"wrk_e8cd9e41-ade7-4f50-b16e-dc0e90202415","block_id":"blk_7cc1595c-4aef-41c5-93c3-bae7fd6cfb09","section_id":"sec_87da8dee-73da-4f14-ad2c-259a8b7a8f1d","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":215,"end":217,"exact":"推論","quote":"10 TB/sのチップ間リンクと288GBのHBM3Eメモリの高帯域幅接続が可能になり、AIワークロード、特に推論モデルとトレーニングモデルの性能が大幅に向上します。","quote_start":160,"quote_end":243,"text_sha256":"a8006164c6309f691e38d84a8e57bcb12139d17fdef759139e7209c979515f81","block_sha256":"a8006164c6309f691e38d84a8e57bcb12139d17fdef759139e7209c979515f81","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_7cc1595c-4aef-41c5-93c3-bae7fd6cfb09"},{"id":"occ_e2ee7759d6a3cb5a9858bcc3","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64","work_id":"wrk_e8cd9e41-ade7-4f50-b16e-dc0e90202415","block_id":"blk_9417abfb-0e81-46d3-8d3d-1d96c186371c","section_id":"sec_2d5e4366-a96e-4e80-9fb4-9ad5ea2ad952","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":130,"end":132,"exact":"推論","quote":"の高帯域幅接続が可能です。CoWoS-Lの使用は、GPUのスケーラビリティと性能を高め、AIワークロード、特に推論モデルとトレーニングモデルの性能を大幅に向上させます。","quote_start":75,"quote_end":159,"text_sha256":"1173ce0caf30731778ef5a208cd8d38953cc13bf683dab9c81e40990bf244aa7","block_sha256":"1173ce0caf30731778ef5a208cd8d38953cc13bf683dab9c81e40990bf244aa7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_9417abfb-0e81-46d3-8d3d-1d96c186371c"},{"id":"occ_b7c482975c8211a76ad8cd9b","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64","work_id":"wrk_e8cd9e41-ade7-4f50-b16e-dc0e90202415","block_id":"blk_ba119c61-88a9-4e01-aedc-79a98129452b","section_id":"sec_0d357819-4924-4eaa-a396-0427ffcf160c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":130,"end":132,"exact":"推論","quote":"の高帯域幅接続が可能です。CoWoS-Lの使用は、GPUのスケーラビリティと性能を高め、AIワークロード、特に推論モデルとトレーニングモデルの性能を大幅に向上させます。","quote_start":75,"quote_end":159,"text_sha256":"1173ce0caf30731778ef5a208cd8d38953cc13bf683dab9c81e40990bf244aa7","block_sha256":"1173ce0caf30731778ef5a208cd8d38953cc13bf683dab9c81e40990bf244aa7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_ba119c61-88a9-4e01-aedc-79a98129452b"},{"id":"occ_804bc75af8fa19a59a1d7fbc","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64","work_id":"wrk_e8cd9e41-ade7-4f50-b16e-dc0e90202415","block_id":"blk_dea7b7fc-9a5e-4737-8c43-98fe33785b84","section_id":"sec_73389177-023d-445a-8bcd-fa428a9c155c","layer":"body","character_id":null,"count":2,"matched_aliases":["KVキャッシュ","推論"],"evidence":{"text_basis":"markdown","start":137,"end":144,"exact":"KVキャッシュ","quote":"Lによる高密度パッケージングの結果です。また、メモリ容量の増加（288GB HBM3E）は、長いシーケンス長のKVキャッシュを扱うOpenAI O3スタイルのLLM推論トレーニングに特に重要です。","quote_start":82,"quote_end":180,"text_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","block_sha256":"0806ac64f66cf0527dab4f0106a9361ff1e9ca9628298df22eb8302082a34150","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ff004386-e6c0-470b-b19a-7eaa2c2aba64/#blk_dea7b7fc-9a5e-4737-8c43-98fe33785b84"},{"id":"occ_b7478b12109cc24028dfa774","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_153956e9-8aa8-4dc3-92a4-a2d23a492683","section_id":"sec_cbe2c495-07df-49fa-8f3b-3279351d5757","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":46,"end":48,"exact":"推論","quote":"さらに2026年に発表されたCosmos 3は、言語、画像、動画、音声、行動を扱い、**物理推論・世界生成・行動生成を一つのモデル系列で統合する方向**へ進んでいます。NVIDIAはCosmos 3をWAMのバックボーンとして利用できるものと位置付けています。([NVIDIA Developer","quote_start":0,"quote_end":148,"text_sha256":"196c5b949e776d678e9a60d5181ed7703c5ff2fc1e182ba7bcc71085a1de344f","block_sha256":"196c5b949e776d678e9a60d5181ed7703c5ff2fc1e182ba7bcc71085a1de344f","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_153956e9-8aa8-4dc3-92a4-a2d23a492683"},{"id":"occ_5a411826a8bd44714a4f152a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_58760f96-c03c-446f-abed-678b63642281","section_id":"sec_80ea3fd9-54df-4928-af22-5adb7a8b3d7c","layer":"code","character_id":null,"count":3,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":6,"end":8,"exact":"推論","quote":"```\n長期推論・タスク理解\n→ GR00Tの推論機能、言語モデル\n\n世界理解・未来生成\n→ Cosmos\n\n行動生成\n→ GR00T、Cosmosを基盤としたWAM\n\n仮想世界・データ生成\n→ Omniverse、","quote_start":0,"quote_end":108,"text_sha256":"d3e2fac903ca507fba04f283ea9d564279d38140884d3743fef1deb4a5f69d87","block_sha256":"d3e2fac903ca507fba04f283ea9d564279d38140884d3743fef1deb4a5f69d87","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_58760f96-c03c-446f-abed-678b63642281"},{"id":"occ_1cb735751c5d782f99321a54","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_9fd9934d-9a73-46b6-82cc-c53eef5d9040","section_id":"sec_80ea3fd9-54df-4928-af22-5adb7a8b3d7c","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":18,"end":20,"exact":"推論","quote":"特にCosmos 3は世界生成、物理推論、行動生成を統合する方向へ進み、GR00T N1.7は言語・画像から具体的なロボット行動を生成します。([NVIDIA Developer](https://developer.nvidia.com/","quote_start":0,"quote_end":120,"text_sha256":"9a81d17768aaa613801115bbe7a88d37f919f15bc676d1d75aff9e33c9601341","block_sha256":"9a81d17768aaa613801115bbe7a88d37f919f15bc676d1d75aff9e33c9601341","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_9fd9934d-9a73-46b6-82cc-c53eef5d9040"},{"id":"occ_bd0cc1c8d6a8aa76f7b52ad9","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_c9742176-f1b4-428c-9609-1656ce21bd34","section_id":"sec_d941910a-df19-4c5d-b04e-7c5930e23015","layer":"code","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":136,"end":138,"exact":"推論","quote":"る\n        ↓\nGR00Tが具体的なロボット行動を生成する\n        ↓\nJetsonが実機上で推論を実行する\n```","quote_start":81,"quote_end":147,"text_sha256":"d1f17f7ad40dbfe0787a02bc6cc5d221e50bdbf42ddf5c08a29a69b6dc94610a","block_sha256":"d1f17f7ad40dbfe0787a02bc6cc5d221e50bdbf42ddf5c08a29a69b6dc94610a","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_c9742176-f1b4-428c-9609-1656ce21bd34"},{"id":"occ_c1c0b0cb8f4bfdfa6e50618d","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_daf8d1f9-e0f4-47d0-8a0c-d18b0b047eff","section_id":"sec_b017e86a-ce31-4ab9-895f-d5d2ea32bf58","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":66,"end":68,"exact":"推論","quote":"来を予測することもありますし、WAMが言語指示を受け取ることもあります。GR00Tのように、VLAでありながら推論や複数ステップの処理を行うモデルもあります。","quote_start":11,"quote_end":90,"text_sha256":"a21e9155b9b8c7623b85e7577ef535e0647266dbb0d867490cef2688ceff21a0","block_sha256":"a21e9155b9b8c7623b85e7577ef535e0647266dbb0d867490cef2688ceff21a0","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_daf8d1f9-e0f4-47d0-8a0c-d18b0b047eff"},{"id":"occ_f91740f4bc25d4d81b129b17","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_e58e4821-2ea4-40d3-a6ad-6cd219b840de","section_id":"sec_e2f8e560-ad7e-45b3-959d-f8cc7e1a338a","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":82,"end":84,"exact":"推論","quote":"」と定義するのは適切ではありません。現在のVLAには、複数行動をまとめたアクションチャンク、高レベル計画、言語推論、暗黙的な世界モデルを持つものも存在します。VLA研究でも、計画と実行を分離した階層型や、世界モデルを組み込んだ構成が整理されています。([arXiv](https://arxiv.org/abs/2","quote_start":27,"quote_end":184,"text_sha256":"3002b271407710b004e6fdc3766071588922b1f2b8ab5a64eff68217c75641a7","block_sha256":"3002b271407710b004e6fdc3766071588922b1f2b8ab5a64eff68217c75641a7","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_e58e4821-2ea4-40d3-a6ad-6cd219b840de"},{"id":"occ_b9052fcc80bc3da5c115af4a","topic_id":"top_1b107527-55b8-4791-a6b1-3e69df457633","revision_id":"rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47","work_id":"wrk_75c66a96-f27d-4786-b253-3d6d4f826277","block_id":"blk_f2aa5856-e811-427b-976a-90b942c81cfb","section_id":"sec_d15acc75-99cd-4a55-bfcc-a4080ac13db3","layer":"body","character_id":null,"count":1,"matched_aliases":["推論"],"evidence":{"text_basis":"markdown","start":28,"end":30,"exact":"推論","quote":"ただし、近年のGR00Tは単純な反射的行動だけではなく、推論、指示追従、複数段階の作業への対応も強化されています。したがって、「GR00Tは短期動作しかできない」と考えるのは正確ではありません。([NVIDIA Newsroom](https://nvidia","quote_start":0,"quote_end":130,"text_sha256":"38f65fc2c18b3fe46ee8b7407a6f8f4641d8d382007a02046543e2732d102f89","block_sha256":"38f65fc2c18b3fe46ee8b7407a6f8f4641d8d382007a02046543e2732d102f89","offset_unit":"unicode_code_point"},"detection_method":"deterministic_literal","dictionary_revision":"topics-20260909-v1","review_status":"automatic_match","role":"unassessed","href":"articles/rev_ffbdfa80-0e8b-40b5-8765-ba1e5b977d47/#blk_f2aa5856-e811-427b-976a-90b942c81cfb"}],"timeline":{"dated":[{"date":{"precision":"instant","search_end":"2024-10-10","search_start":"2024-10-09","timezone":null,"value":"2024-10-09 21:28:08"},"date_kind":"source_date","is_distribution_representative":true,"occurrence_ids":["occ_e8dc6c49454718f2c6f1acc2"],"revision_id":"rev_8b1f5dca-c040-4a1f-8ef8-530daabc764c","work_id":"wrk_64f4eda6-371c-4a72-8362-8462b01a5501"},{"date":{"precision":"instant","search_end":"2025-01-29","search_start":"2025-01-28","timezone":null,"value":"2025-01-28 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