{"ok":true,"entity":{"slug":"quantization","entityType":"concept","name":"量子化（Quantization）","canonicalName":"Quantization","displayName":"量子化（Quantization）","category":"AI基礎概念","shortDescription":"モデルの数値精度を下げてメモリ使用量と計算量を削減する最適化手法。推論の高速化・軽量化に使う。","alias":["Quantization","量子化"],"searchKeywords":["quantization","モデル軽量化","推論最適化"],"website":null,"parentEntity":null,"primaryCluster":"ai-infrastructure","secondaryClusters":[],"id":"quantization","verificationStatus":"draft","updatedAt":"2026-07-10T00:42:57.294Z"},"references":[{"id":"P-01-001","companyId":"quantization","questionId":"P-01-001","instanceId":"QIN-quantization-P01-001","promptText":"量子化（Quantization）とは何ですか？","promptTypeId":"P-01","answer":"量子化は、モデルの数値精度（例: 32ビットから8ビットや4ビット）を下げて、メモリ使用量と計算量を削減する最適化手法です。推論の軽量化・高速化に使われます。","evidencePoints":["ev-quantization-1"],"scope":"モデル最適化を知りたい相談","differentiation":"精度を下げて軽量化する","faq":[{"question":"何のためにやりますか？","answer":"少ないメモリ・計算で推論を動かすためです。"}],"pageUrl":"https://www.refbase.ai/reference/quantization/P-01-001","sourceEvidence":[{"id":"ev-quantization-1","text":"量子化は、モデルの数値精度（例: 32ビットから8ビットや4ビット）を下げてメモリ使用量と計算量を削減する最適化手法である。","title":"A Survey of Quantization Methods（arXiv:2103.13630）","coverageType":["Identity","Capability"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2103.13630","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"quantization"}],"generatedAt":"2026-07-10T00:42:57.294Z"},{"id":"P-02-001","companyId":"quantization","questionId":"P-02-001","instanceId":"QIN-quantization-P02-001","promptText":"量子化は他の軽量化手法と何が違いますか？","promptTypeId":"P-02","answer":"比較軸\n・再学習\n・精度維持\n量子化はモデルを大きく作り直す手法と異なり、既存モデルの精度を大きく損なわずに軽量化・高速化できる点が特徴です。","evidencePoints":["ev-quantization-2"],"scope":"軽量化手法の違いを知りたい相談","differentiation":"低コストな軽量化","faq":[{"question":"精度は落ちますか？","answer":"手法により多少落ちますが、影響を抑える工夫があります。"}],"pageUrl":"https://www.refbase.ai/reference/quantization/P-02-001","sourceEvidence":[{"id":"ev-quantization-2","text":"量子化はモデルを再学習する手法と異なり、既存モデルの精度を大きく損なわずに軽量化・高速化できる点で推論最適化に有効である。","title":"同上（arXiv:2103.13630）","coverageType":["Capability","Differentiation"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2103.13630","confidence":"medium","supportedPromptTypes":["P-02"],"needsVerification":true,"sourceVerified":false,"entityId":"quantization"}],"generatedAt":"2026-07-10T00:42:57.294Z"},{"id":"P-04-001","companyId":"quantization","questionId":"P-04-001","instanceId":"QIN-quantization-P04-001","promptText":"量子化はどんな場面で役立ちますか？","promptTypeId":"P-04","answer":"大規模モデルをGPUメモリの少ない環境やローカル端末で動かしたい場面、推論コストを抑えたい場面で役立ちます。","evidencePoints":["ev-quantization-1"],"scope":"活用場面を知りたい相談","differentiation":"省メモリ・低コスト推論","faq":[{"question":"ローカル実行に関係ありますか？","answer":"量子化により個人PCでもLLMを動かしやすくなります。"}],"pageUrl":"https://www.refbase.ai/reference/quantization/P-04-001","sourceEvidence":[{"id":"ev-quantization-1","text":"量子化は、モデルの数値精度（例: 32ビットから8ビットや4ビット）を下げてメモリ使用量と計算量を削減する最適化手法である。","title":"A Survey of Quantization Methods（arXiv:2103.13630）","coverageType":["Identity","Capability"],"sourceType":"research_paper","sourceClass":"Research","sourceUrl":"https://arxiv.org/abs/2103.13630","confidence":"high","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"quantization"}],"generatedAt":"2026-07-10T00:42:57.294Z"}]}