[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"tag-mlperf":3},{"tag":4,"articles":11},{"id":5,"name":6,"slug":7,"article_count":8,"description_zh":9,"description_en":10},"04e80913-2de0-4f2d-a44f-44feede215a5","MLPerf","mlperf",3,"MLPerf 是用來衡量機器學習訓練與推論效能的公開基準，常被拿來比較 GPU、伺服器與軟體堆疊的實際差異。這個標籤聚焦最新成績、模型類型與最佳化手法，尤其是推論延遲、吞吐量與系統調校。","MLPerf is the public benchmark used to compare machine learning training and inference across GPUs, servers, and software stacks. This tag tracks new results, model classes, and optimization methods, especially latency, throughput, and system tuning.",[12,21],{"id":13,"slug":14,"title":15,"summary":16,"category":17,"image_url":18,"cover_image":18,"language":19,"created_at":20},"0b5979a7-dbb3-438f-b8a1-68de0f838df0","nvidia-mlperf-software-inference-benchmarks-zh","Nvidia MLPerf 成績證明軟體還很重要","Nvidia 在 MLPerf v6.0 交出最高 2.77x 推論提升。GB300 NVL72 的成績顯示，Dynamo、TensorRT-LLM 這類軟體優化，已經和 GPU 硬體同樣重要。","research","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775185790112-2r4u.png","zh","2026-04-03T03:09:34.300263+00:00",{"id":22,"slug":23,"title":24,"summary":25,"category":26,"image_url":27,"cover_image":27,"language":19,"created_at":28},"d9fda242-d695-4ea4-a0e0-c6c64ad72965","nvidia-sets-new-mlperf-inference-records-zh","NVIDIA 再刷 MLPerf 推論紀錄","NVIDIA 在 MLPerf Inference v6.0 再交出新成績，GB300 NVL72 對 DeepSeek-R1 伺服器推論提升 2.7x，Llama 3.1 405B 也提升 1.5x。","industry","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122496881-vxz0.png","2026-04-02T08:48:38.43437+00:00"]