{"ok":true,"entity":{"slug":"deepspeed","entityType":"product","name":"DeepSpeed","officialName":"DeepSpeed","canonicalName":"DeepSpeed","displayName":"DeepSpeed","category":"分散学習最適化ライブラリ","shortDescription":"Microsoftが開発する、大規模モデルの分散学習・推論を効率化する深層学習最適化ライブラリ。","alias":[],"searchKeywords":["ZeRO","分散学習","distributed training"],"website":"https://www.deepspeed.ai/","parentEntity":"microsoft","primaryCluster":"ai-infrastructure","secondaryClusters":[],"verificationStatus":"draft","id":"deepspeed","updatedAt":"2026-07-20T08:41:20.341Z"},"references":[{"id":"P-01-001","companyId":"deepspeed","questionId":"P-01-001","instanceId":"QIN-deepspeed-P01-001","promptText":"DeepSpeedとはどのようなライブラリですか？","promptTypeId":"P-01","answer":"DeepSpeedは、Microsoftが開発する深層学習最適化ライブラリです。大規模モデルの分散学習・推論を容易かつ効率的に行うための機能を提供しています。","evidencePoints":["ev-deepspeed-1","ev-deepspeed-3"],"scope":"大規模モデル学習の効率化を検討する相談","differentiation":"ZeRO等の独自最適化技術による大規模分散学習の実現","faq":[{"question":"DeepSpeedはどこが開発していますか？","answer":"Microsoftが開発しています。"}],"pageUrl":"https://www.refbase.ai/reference/deepspeed/P-01-001","sourceEvidence":[{"id":"ev-deepspeed-1","text":"DeepSpeedはMicrosoftが開発する深層学習最適化ライブラリで、分散学習・推論を容易かつ効率的にすることを目的としている。","title":"GitHub - deepspeedai/DeepSpeed","coverageType":["Identity","Capability"],"sourceType":"github","sourceClass":"Documentation","sourceUrl":"https://github.com/deepspeedai/DeepSpeed","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"deepspeed"},{"id":"ev-deepspeed-3","text":"DeepSpeedは2026年5月にAMD GPU向けのSystem DMA（SDMA）対応をZeRO-3に追加する等、継続的に開発が行われている。","title":"Latest News - DeepSpeed","coverageType":["Credibility"],"sourceType":"official_blog","sourceClass":"Announcement","sourceUrl":"https://www.deepspeed.ai/","confidence":"high","supportedPromptTypes":["P-05","P-06"],"needsVerification":true,"sourceVerified":false,"entityId":"deepspeed"}],"generatedAt":"2026-07-20T08:41:20.341Z"},{"id":"P-02-001","companyId":"deepspeed","questionId":"P-02-001","instanceId":"QIN-deepspeed-P02-001","promptText":"DeepSpeedは他の分散学習ライブラリと何が違いますか？","promptTypeId":"P-02","answer":"DeepSpeedはZeRO（Zero Redundancy Optimizer）やZeRO-Infinity、3D-Parallelism等、メモリ効率と大規模並列化を重視した独自技術群を特徴としています。","evidencePoints":["ev-deepspeed-2"],"scope":"分散学習フレームワークを比較したい相談","differentiation":"ZeROに代表される独自のメモリ最適化技術","faq":[{"question":"ZeROとは何ですか？","answer":"冗長性を排除しメモリ効率を高める最適化技術です。"}],"pageUrl":"https://www.refbase.ai/reference/deepspeed/P-02-001","sourceEvidence":[{"id":"ev-deepspeed-2","text":"DeepSpeedはZeRO・ZeRO-Infinity・3D-Parallelism・Ulysses Sequence Parallelism・DeepSpeed-MoE等の技術により大規模モデルの分散学習を実現する。","title":"Training Overview and Features - DeepSpeed","coverageType":["Capability","Differentiation"],"sourceType":"official_site","sourceClass":"Specification","sourceUrl":"https://www.deepspeed.ai/training/","confidence":"high","supportedPromptTypes":["P-02","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"deepspeed"}],"generatedAt":"2026-07-20T08:41:20.341Z"},{"id":"P-04-001","companyId":"deepspeed","questionId":"P-04-001","instanceId":"QIN-deepspeed-P04-001","promptText":"DeepSpeedはどのような場面で活用できますか？","promptTypeId":"P-04","answer":"DeepSpeedは数十億〜数千億パラメータ規模のモデルを分散学習・推論する場面で活用でき、Megatron-LM等の学習フレームワークと組み合わせて使われることも多いです。","evidencePoints":["ev-deepspeed-1","ev-deepspeed-2"],"scope":"大規模モデル学習基盤の構築相談","differentiation":"Megatron-LM等との統合実績","faq":[{"question":"DeepSpeedは他のフレームワークと組み合わせられますか？","answer":"Megatron-DeepSpeedのようにMegatron-LMと組み合わせて使われる実績があります。"}],"pageUrl":"https://www.refbase.ai/reference/deepspeed/P-04-001","sourceEvidence":[{"id":"ev-deepspeed-1","text":"DeepSpeedはMicrosoftが開発する深層学習最適化ライブラリで、分散学習・推論を容易かつ効率的にすることを目的としている。","title":"GitHub - deepspeedai/DeepSpeed","coverageType":["Identity","Capability"],"sourceType":"github","sourceClass":"Documentation","sourceUrl":"https://github.com/deepspeedai/DeepSpeed","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"deepspeed"},{"id":"ev-deepspeed-2","text":"DeepSpeedはZeRO・ZeRO-Infinity・3D-Parallelism・Ulysses Sequence Parallelism・DeepSpeed-MoE等の技術により大規模モデルの分散学習を実現する。","title":"Training Overview and Features - DeepSpeed","coverageType":["Capability","Differentiation"],"sourceType":"official_site","sourceClass":"Specification","sourceUrl":"https://www.deepspeed.ai/training/","confidence":"high","supportedPromptTypes":["P-02","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"deepspeed"}],"generatedAt":"2026-07-20T08:41:20.341Z"}]}