[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-cuda-cores-memory-tensor-cores-win-zh":3,"article-related-cuda-cores-memory-tensor-cores-win-zh":33,"series-industry-15c682f7-7da1-4ecc-abb8-adec3192f9e4":87},{"id":4,"slug":5,"title":6,"content":7,"summary":8,"source":9,"source_url":10,"author":11,"image_url":12,"cover_image":12,"category":13,"language":14,"translated_content":11,"related_article_id":15,"keywords":16,"key_takeaways":25,"views":29,"created_at":30,"published_at":31,"topic_cluster_id":32},"15c682f7-7da1-4ecc-abb8-adec3192f9e4","cuda-cores-memory-tensor-cores-win-zh","CUDA 核心重要，但記憶體與 Tensor Core 才決定訓練速度","\u003Cp data-speakable=\"summary\">\u003Ca href=\"\u002Fnews\u002Fcuda-oxide-rust-ptx-kernels-zh\">CUDA\u003C\u002Fa> \u003Ca href=\"\u002Fnews\u002Fgpu-programming-core-software-skill-zh\">核心\u003C\u002Fa>能加速 AI 訓練，但記憶體、架構與 Tensor Core 往往更關鍵。\u003C\u002Fp>\u003Cp>如果你正在挑 \u003Ca href=\"\u002Ftag\u002Fgpu\">GPU\u003C\u002Fa> 來做 AI，讀完這 5 點，你就能分辨該看核心數、VRAM，還是 Tensor Core。先看一個直觀數字：RTX 4090 有 16,384 個 \u003Ca href=\"\u002Ftag\u002Fcuda\">CUDA\u003C\u002Fa> cores，FP32 峰值約 70 TFLOPS，但這不代表它一定比別張卡更適合訓練\u003Ca href=\"\u002Fnews\u002Fnvidia-nemotron-3-ultra-open-models-compete-zh\">模型\u003C\u002Fa>。\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>項目\u003C\u002Fth>\u003Cth>CUDA cores\u003C\u002Fth>\u003Cth>記憶體\u003C\u002Fth>\u003Cth>雲端價格\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">RTX A6000\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>10,752\u003C\u002Ftd>\u003Ctd>48 GB GDDR6\u003C\u002Ftd>\u003Ctd>$0.35\u002Fhr\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">A100 80GB\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>6,912\u003C\u002Ftd>\u003Ctd>80 GB HBM2e\u003C\u002Ftd>\u003Ctd>$0.78\u002Fhr\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">L40\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>n\u002Fa\u003C\u002Ftd>\u003Ctd>48 GB GDDR6\u003C\u002Ftd>\u003Ctd>$0.89\u002Fhr\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">L40S\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>n\u002Fa\u003C\u002Ftd>\u003Ctd>48 GB GDDR6\u003C\u002Ftd>\u003Ctd>$0.99\u002Fhr\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">H100 80GB\u003C\u002Fa>\u003C\u002Ftd>\u003Ctd>14,592\u003C\u002Ftd>\u003Ctd>80 GB HBM3\u003C\u002Ftd>\u003Ctd>$1.38\u002Fhr\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>1. CUDA cores 是 GPU 的通用工人\u003C\u002Fh2>\u003Cp>CUDA 是 \u003Ca href=\"\u002Ftag\u002Fnvidia\">NVIDIA\u003C\u002Fa> 的 \u003Ca href=\"https:\u002F\u002Fdeveloper.nvidia.com\u002Fcuda-zone\" target=\"_blank\" rel=\"noopener\">Compute Unified Device Architecture\u003C\u002Fa>，而 CUDA cores 就是 GPU 裡負責平行運算的實體單元。它們擅長加法、乘法、浮點數運算，也能把大量小任務同時推進。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781110977227-kfgw.png\" alt=\"CUDA 核心重要，但記憶體與 Tensor Core 才決定訓練速度\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>這也是 GPU 和 CPU 的分工差異：CPU 擅長少量複雜任務，GPU 則把可拆分的計算一次展開。只要工作負載夠平行，CUDA cores 越多，吞吐通常越高。\u003C\u002Fp>\u003Cul>\u003Cli>適合：浮點運算、整數運算、平行計算\u003C\u002Fli>\u003Cli>常見場景：圖形、科學運算、AI 前處理\u003C\u002Fli>\u003Cli>例子：RTX 4090 有 16,384 個 CUDA cores\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>2. Tensor Cores 才是深度學習的主力\u003C\u002Fh2>\u003Cp>CUDA cores 是通才，Tensor Cores 則是專門為矩陣運算設計的加速單元。從 Volta 世代開始，\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002Fen-us\u002Fdata-center\u002Ftensor-cores\u002F\" target=\"_blank\" rel=\"noopener\">Tensor Cores\u003C\u002Fa> 就用來加速訓練與推論中的矩陣乘法，尤其適合 FP16、BF16、INT8 與 TF32。\u003C\u002Fp>\u003Cp>在現代 AI 裡，真正拉開差距的常常是 Tensor Cores。因為神經網路的大量計算本質上是矩陣塊運算，它們能在單一時脈內完成大量乘加，速度遠高於只靠 CUDA cores。\u003C\u002Fp>\u003Ccode>CUDA cores：前處理、啟動函數、非矩陣運算\u003Cbr>Tensor Cores：attention、convolution 的矩陣乘法\u003C\u002Fcode>\u003Ch2>3. 核心數更多，不一定訓練更快\u003C\u002Fh2>\u003Cp>CUDA cores 數量可以參考，但不能直接當成效能排名。記憶體頻寬、快取設計、時脈、架構世代與 VRAM 容量，往往會在真實工作負載裡蓋過核心總數。\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781110974119-a9ky.png\" alt=\"CUDA 核心重要，但記憶體與 Tensor Core 才決定訓練速度\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>例如 RTX 4080 的 CUDA cores 比 RTX 3090 少，但在不少情境下反而更快，原因就是新架構和更好的記憶體系統。若是 AI 訓練，Tensor Core 數量與 VRAM 容量通常比核心數更值得先看。\u003C\u002Fp>\u003Cul>\u003Cli>先看記憶體頻寬，再比核心數\u003C\u002Fli>\u003Cli>模型或資料集大時，先看 VRAM\u003C\u002Fli>\u003Cli>別只比規格表，還要看架構代數\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>4. 資料搬運效率，常常比算力更重要\u003C\u002Fh2>\u003Cp>CUDA cores 位於 Streaming Multiprocessors，GPU 會用 warp 排程來推進執行。這套機制只有在資料能順利經過 registers、shared memory 與 global memory 時，才會真的跑滿。\u003C\u002Fp>\u003Cp>所以 GPU 在紙面上很強，實際上卻可能卡住。只要記憶體存取慢、資料排列不佳，核心就會閒著。對 AI 訓練來說，最好的卡通常是算力和記憶體最平衡的那張。\u003C\u002Fp>\u003Cul>\u003Cli>SM 會把 CUDA cores 組成執行區塊\u003C\u002Fli>\u003Cli>warp 讓多執行緒同步前進\u003C\u002Fli>\u003Cli>記憶體階層會直接影響實際吞吐\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>5. 選 GPU 時，CUDA 只是其中一個層級\u003C\u002Fh2>\u003Cp>CUDA 只跑在 NVIDIA GPU 上，所以你的選擇通常會落在消費級、工作站級或\u003Ca href=\"\u002Ftag\u002F資料中心\">資料中心\u003C\u002Fa>級。\u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">A100\u003C\u002Fa> 和 \u003Ca href=\"https:\u002F\u002Fwww.nvidia.com\u002F\" target=\"_blank\" rel=\"noopener\">H100\u003C\u002Fa> 適合大規模訓練，RTX 系列則常見於原型開發、微調與推論。\u003C\u002Fp>\u003Cp>如果你想先試不同配置，雲端 GPU 會更彈性。像 \u003Ca href=\"https:\u002F\u002Fwww.thundercompute.com\u002F\" target=\"_blank\" rel=\"noopener\">Thunder Compute\u003C\u002Fa> 這類服務，提供已預裝 CUDA 的實例，起價 $0.35\u002Fhr，A100 80GB 約 $0.78\u002Fhr，H100 約 $1.38\u002Fhr，適合先驗證模型再決定是否購機。\u003C\u002Fp>\u003Cul>\u003Cli>RTX A6000：適合原型與中型工作負載\u003C\u002Fli>\u003Cli>A100 80GB：適合大模型與記憶體吃緊的訓練\u003C\u002Fli>\u003Cli>H100 80GB：預算足夠時，優先考慮的高階訓練卡\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>怎麼挑\u003C\u002Fh2>\u003Cp>如果你做的是一般 CUDA 開發，先看核心數、VRAM 與記憶體頻寬是否平衡。若目標是 AI 訓練，優先順序應該是 Tensor Cores、VRAM，再來才是 CUDA cores。\u003C\u002Fp>\u003Cp>個人開發者和小團隊通常先從 RTX 或雲端入手最划算；只有當模型變大、batch size 變高，或訓練時間成為瓶頸時，再升級到 A100 或 H100 才更合理。\u003C\u002Fp>","5 個 CUDA 核心重點，說明 GPU 訓練速度不只看核心數，還要看 Tensor Cores、記憶體與架構。","www.thundercompute.com","https:\u002F\u002Fwww.thundercompute.com\u002Fblog\u002Fcuda-cores-explained-ai-training",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781110977227-kfgw.png","industry","zh","5417136f-52b3-4b04-9c9b-5cbb4df36584",[17,18,19,20,21,22,23,24],"CUDA cores","Tensor Cores","GPU training","NVIDIA","VRAM","memory bandwidth","A100","H100",[26,27,28],"CUDA cores 重要，但不等於 AI 訓練速度的全部。","Tensor Cores 和 VRAM 往往比核心數更能決定實際表現。","選 GPU 時要一起看架構、記憶體頻寬與使用情境。",0,"2026-06-10T17:02:24.803101+00:00","2026-06-10T17:02:24.796+00:00","fa1dc5e8-0eec-4179-8dc0-e35a3d82f701",{"tags":34,"relatedLang":46,"relatedPosts":50},[35,38,40,42,44],{"name":36,"slug":37},"Nvidia","nvidia",{"name":17,"slug":39},"cuda-cores",{"name":21,"slug":41},"vram",{"name":19,"slug":43},"gpu-training",{"name":18,"slug":45},"tensor-cores",{"id":15,"slug":47,"title":48,"language":49},"cuda-cores-memory-tensor-cores-win-en","CUDA cores matter, but memory and Tensor Cores win","en",[51,57,63,69,75,81],{"id":52,"slug":53,"title":54,"cover_image":55,"image_url":55,"created_at":56,"category":13},"b389653a-3d79-447c-92c3-dd3046ade459","manus-series-b-competitors-profile-zh","Manus 募資後正面對 Box、Airtable","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781113676523-ec9h.png","2026-06-10T17:47:29.385169+00:00",{"id":58,"slug":59,"title":60,"cover_image":61,"image_url":61,"created_at":62,"category":13},"c6712c6a-0d6a-43e0-a8d0-52a936f0c858","reid-hoffman-exit-microsoft-board-right-move-zh","Reid Hoffman 離開 Microsoft 董事會是對的","https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1781112768945-5q4d.png","2026-06-10T17:32:20.021427+00:00",{"id":64,"slug":65,"title":66,"cover_image":67,"image_url":67,"created_at":68,"category":13},"e6150343-1fa8-4cb1-aeda-ac3014747680","codex-0-139-0-web-search-tooling-zh","Codex 0.139.0 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3…","2026-03-26T07:30:12.825269+00:00",{"id":124,"slug":125,"title":126,"created_at":127},"e660d801-2421-4529-8fa9-86b82b066990","metas-llama-4-benchmark-scandal-gets-worse-zh","Meta Llama 4 分數風波又擴大","2026-03-26T07:34:21.156421+00:00",{"id":129,"slug":130,"title":131,"created_at":132},"183f9e7c-e143-40bb-a6d5-67ba84a3a8bc","accenture-mistral-ai-sovereign-enterprise-deal-zh","Accenture 攜手 Mistral AI 賣主權 AI","2026-03-26T07:38:14.818906+00:00",{"id":134,"slug":135,"title":136,"created_at":137},"191d9b1b-768a-478c-978c-dd7431a38149","mistral-ai-faces-its-hardest-year-yet-zh","Mistral AI 迎來最硬的一年","2026-03-26T07:40:23.716374+00:00"]