[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-推論":3},{"tag":4,"articles":10,"peer_article_count":11},{"id":5,"name":6,"slug":6,"article_count":7,"description_zh":8,"description_en":9},"65f138e4-6319-4593-a264-431ca37eb1bc","推論",3,"推論指的是模型在部署後進行即時或批次預測的階段，重點不只在 GPU 算力，也在軟體堆疊、記憶體效率與延遲控制。像 MLPerf 成績、TensorRT-LLM、Dynamo 與伺服器級推論架構，都是這個主題的核心。","Inference is the deployment phase where models generate predictions in real time or in batches. For AI systems, performance depends not only on GPU throughput but also on software stacks, memory efficiency, latency, and serving architecture, from MLPerf results to TensorRT-LLM and server-side optimization.",[],4]