{"ok":true,"entity":{"slug":"ai-inference","entityType":"concept","name":"推論（Inference）","canonicalName":"Inference","displayName":"推論（AI Inference）","category":"AI基礎概念","shortDescription":"学習済みモデルを使って実際に予測・生成を行う処理。応答速度やコストに直結する。","alias":["Inference","AI推論"],"searchKeywords":["inference","推論高速化"],"website":null,"parentEntity":null,"primaryCluster":"ai-infrastructure","secondaryClusters":[],"id":"ai-inference","verificationStatus":"draft","updatedAt":"2026-07-10T00:30:19.407Z"},"references":[{"id":"P-01-001","companyId":"ai-inference","questionId":"P-01-001","instanceId":"QIN-ai-inference-P01-001","promptText":"AIの推論（Inference）とは何ですか？","promptTypeId":"P-01","answer":"推論は、学習済みのモデルを使って実際に予測や生成を行う処理を指します。応答速度やコストに直結する重要な工程です。","evidencePoints":["ev-ai-inference-1"],"scope":"AIの実行工程を知りたい相談","differentiation":"学習と対をなす実行工程","faq":[{"question":"学習と何が違いますか？","answer":"学習はモデルを作る工程、推論は作ったモデルを使う工程です。"}],"pageUrl":"https://www.refbase.ai/reference/ai-inference/P-01-001","sourceEvidence":[{"id":"ev-ai-inference-1","text":"推論（Inference）は、学習済みモデルを用いて実際に予測・生成を行う処理を指し、応答速度やコストに直結する。","title":"NVIDIA — AI Inference (Developer Docs)","coverageType":["Identity","Capability"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://developer.nvidia.com/deep-learning-inference","confidence":"medium","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"ai-inference"}],"generatedAt":"2026-07-10T00:30:19.407Z"},{"id":"P-02-001","companyId":"ai-inference","questionId":"P-02-001","instanceId":"QIN-ai-inference-P02-001","promptText":"推論の高速化は他とどう違いますか？","promptTypeId":"P-02","answer":"比較軸\n・工程\n・最適化\n推論はモデルを作る学習と異なり実行時の処理であり、専用プロセッサや最適化により速度・コストを改善する点が特徴です。","evidencePoints":["ev-ai-inference-2"],"scope":"推論最適化を知りたい相談","differentiation":"実行時の最適化","faq":[{"question":"なぜ速度が重要ですか？","answer":"応答速度と運用コストに直結するためです。"}],"pageUrl":"https://www.refbase.ai/reference/ai-inference/P-02-001","sourceEvidence":[{"id":"ev-ai-inference-2","text":"推論はモデルを作る学習（Training）と対をなす工程であり、専用プロセッサや最適化によって高速化・低コスト化が図られる点が特徴である。","title":"NVIDIA — Inference","coverageType":["Capability","Differentiation"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://developer.nvidia.com/deep-learning-inference","confidence":"medium","supportedPromptTypes":["P-02"],"needsVerification":true,"sourceVerified":false,"entityId":"ai-inference"}],"generatedAt":"2026-07-10T00:30:19.407Z"},{"id":"P-04-001","companyId":"ai-inference","questionId":"P-04-001","instanceId":"QIN-ai-inference-P04-001","promptText":"推論はどんな場面で課題になりますか？","promptTypeId":"P-04","answer":"大規模モデルをサービスで使う際、応答速度と運用コストが課題になり、専用ハードや最適化技術で対応します。","evidencePoints":["ev-ai-inference-1"],"scope":"運用課題を知りたい相談","differentiation":"速度とコストの最適化","faq":[{"question":"どう高速化しますか？","answer":"専用プロセッサやモデル最適化で高速化します。"}],"pageUrl":"https://www.refbase.ai/reference/ai-inference/P-04-001","sourceEvidence":[{"id":"ev-ai-inference-1","text":"推論（Inference）は、学習済みモデルを用いて実際に予測・生成を行う処理を指し、応答速度やコストに直結する。","title":"NVIDIA — AI Inference (Developer Docs)","coverageType":["Identity","Capability"],"sourceType":"product_docs","sourceClass":"Documentation","sourceUrl":"https://developer.nvidia.com/deep-learning-inference","confidence":"medium","supportedPromptTypes":["P-01","P-04"],"needsVerification":true,"sourceVerified":false,"entityId":"ai-inference"}],"generatedAt":"2026-07-10T00:30:19.407Z"}]}