{"id":10540,"date":"2026-01-03T09:09:48","date_gmt":"2026-01-03T17:09:48","guid":{"rendered":"https:\/\/zxi.mytechroad.com\/blog\/?p=10540"},"modified":"2026-01-03T09:09:51","modified_gmt":"2026-01-03T17:09:51","slug":"%e5%b7%85%e5%b3%b0%e5%af%b9%e5%86%b3%ef%bc%9agoogle-tpu-v7-ironwood-vs-nvidia-blackwell-2026%e5%b9%b4ai%e7%ae%97%e5%8a%9b%e6%a0%bc%e5%b1%80%e6%b7%b1%e5%ba%a6%e8%a7%a3%e6%9e%90","status":"publish","type":"post","link":"https:\/\/zxi.mytechroad.com\/blog\/ai\/%e5%b7%85%e5%b3%b0%e5%af%b9%e5%86%b3%ef%bc%9agoogle-tpu-v7-ironwood-vs-nvidia-blackwell-2026%e5%b9%b4ai%e7%ae%97%e5%8a%9b%e6%a0%bc%e5%b1%80%e6%b7%b1%e5%ba%a6%e8%a7%a3%e6%9e%90\/","title":{"rendered":"\u5dc5\u5cf0\u5bf9\u51b3\uff1aGoogle TPU v7 (Ironwood) vs. NVIDIA Blackwell \u2014\u2014 2026\u5e74AI\u7b97\u529b\u683c\u5c40\u6df1\u5ea6\u89e3\u6790"},"content":{"rendered":"\n

\u53d1\u5e03\u65e5\u671f\uff1a 2026\u5e741\u67082\u65e5<\/p>\n\n\n\n

\u9605\u8bfb\u65f6\u95f4\uff1a \u7ea6 18 \u5206\u949f<\/p>\n\n\n\n

\u4f5c\u8005\uff1a Gemini (AI \u67b6\u6784\u5206\u6790\u5e08)<\/p>\n\n\n\n

\n\n\n\n

1. \u5f15\u8a00\uff1a\u4ece\u201c\u4e00\u5bb6\u72ec\u5927\u201d\u5230\u201c\u53cc\u96c4\u4e89\u9738\u201d<\/h1>\n\n\n\n

\u76f4\u52302024\u5e74\uff0cAI \u786c\u4ef6\u5e02\u573a\u7684\u4e3b\u65cb\u5f8b\u57fa\u672c\u4e0a\u662f\u201cNVIDIA \u53ca\u5176\u8ffd\u8d76\u8005\u201d\u3002\u7136\u800c\uff0c\u968f\u7740\u65f6\u95f4\u63a8\u8fdb\u52302026\u5e74\u521d\uff0c\u683c\u5c40\u53d1\u751f\u4e86\u5fae\u5999\u800c\u6df1\u523b\u7684\u53d8\u5316\u3002<\/p>\n\n\n\n

NVIDIA \u51ed\u501f Blackwell \u67b6\u6784\uff08B200\/GB200\uff09\u57282025\u5e74\u6a2a\u626b\u4e86\u6570\u636e\u4e2d\u5fc3\uff0c\u4f46 Google \u5e76\u6ca1\u6709\u5750\u4ee5\u5f85\u6bd9\u3002\u7ee7 Trillium (TPU v6) \u4e4b\u540e\uff0cGoogle \u57282025\u5e74\u5e95\u91cd\u78c5\u63a8\u51fa\u4e86\u7b2c\u4e03\u4ee3\u5f20\u91cf\u5904\u7406\u5355\u5143\u2014\u2014TPU v7 (\u4ee3\u53f7 Ironwood)<\/strong>\u3002<\/p>\n\n\n\n

\u8fd9\u4e00\u4ee3 TPU \u4e0d\u518d\u4ec5\u4ec5\u662f\u201cGoogle \u5185\u90e8\u7684\u73a9\u5177\u201d\uff0c\u5b83\u5728\u663e\u5b58\u5bb9\u91cf\u3001\u4e92\u8054\u5e26\u5bbd\u548c\u80fd\u6548\u6bd4\u4e0a\u5df2\u7ecf\u5b8c\u5168\u8ffd\u5e73\u751a\u81f3\u5728\u67d0\u4e9b\u7279\u5b9a\u573a\u666f\u8d85\u8d8a\u4e86 NVIDIA \u7684\u65d7\u8230\u4ea7\u54c1\u3002\u5bf9\u4e8e\u6b63\u5728\u89c4\u5212 2026-2027 \u5e74\u7b97\u529b\u96c6\u7fa4\u7684 CTO \u548c AI \u67b6\u6784\u5e08\u6765\u8bf4\uff0c\u9009\u62e9\u4e0d\u518d\u662f\u9ed8\u8ba4\u7684\u7eff\u8272\uff08NVIDIA\uff09\uff0c\u800c\u662f\u9700\u8981\u5728\u201c\u901a\u7528\u6027\u9738\u4e3b\u201d\u4e0e\u201c\u5782\u76f4\u6574\u5408\u602a\u517d\u201d\u4e4b\u95f4\u505a\u51fa\u8270\u96be\u6289\u62e9\u3002<\/p>\n\n\n\n

\u672c\u6587\u5c06\u4ece\u67b6\u6784\u53c2\u6570\u3001\u4e92\u8054\u62d3\u6251\u3001\u8f6f\u4ef6\u751f\u6001\u3001\u4ee5\u53ca TCO\uff08\u603b\u62e5\u6709\u6210\u672c\uff09\u56db\u4e2a\u7ef4\u5ea6\uff0c\u5168\u65b9\u4f4d\u5bf9\u6bd4 TPU v7 \u4e0e NVIDIA Blackwell\u3002<\/p>\n\n\n\n

\n\n\n\n

2. \u6838\u5fc3\u89c4\u683c\u53c2\u6570\u5bf9\u6bd4\uff1a\u7eb8\u9762\u5b9e\u529b\u7684\u8d34\u8eab\u8089\u640f<\/h1>\n\n\n\n

\u5728 v5p \u548c v6 \u65f6\u4ee3\uff0cTPU \u5728\u5355\u82af\u7247\u7b97\u529b\u4e0a\u5f80\u5f80\u843d\u540e\u4e8e NVIDIA \u540c\u671f\u65d7\u8230\uff0c\u4e3b\u8981\u9760\u5927\u89c4\u6a21\u96c6\u7fa4\u53d6\u80dc\u3002\u4f46 TPU v7 “Ironwood” \u5f7b\u5e95\u6539\u53d8\u4e86\u8fd9\u4e00\u5c40\u9762\u3002Google \u91c7\u7528\u4e86\u7c7b\u4f3c Blackwell \u7684\u53cc\u82af\u7c92\uff08Dual-chiplet\uff09\u5c01\u88c5\u6280\u672f\uff0c\u4f7f\u5f97\u5355\u5361\u6027\u80fd\u66b4\u6da8\u3002<\/p>\n\n\n\n

\u4ee5\u4e0b\u662f TPU v7 (Ironwood)<\/strong> \u4e0e NVIDIA B200 Blackwell<\/strong> \u7684\u5173\u952e\u53c2\u6570\u5bf9\u6bd4\uff1a<\/p>\n\n\n\n

\u6838\u5fc3\u6307\u6807<\/strong><\/td>Google TPU v7 (Ironwood)<\/strong><\/td>NVIDIA B200 (Blackwell)<\/strong><\/td>\u80dc\u51fa\u8005<\/strong><\/td><\/tr><\/thead>
\u67b6\u6784\u4ee3\u53f7<\/strong><\/td>Ironwood (7th Gen)<\/td>Blackwell<\/td>\u5e73\u624b<\/td><\/tr>
\u5236\u9020\u5de5\u827a<\/strong><\/td>TSMC Custom Node (est. 3nm)<\/td>TSMC 4NP (Refined 5nm\/4nm)<\/td>TPU v7 (\u7a0d\u5360\u4f18)<\/td><\/tr>
HBM \u5bb9\u91cf<\/strong><\/td>192 GB<\/strong> (HBM3e)<\/td>192 GB<\/strong> (HBM3e)<\/td>\u5e73\u624b<\/td><\/tr>
\u5185\u5b58\u5e26\u5bbd<\/strong><\/td>7.38 TB\/s<\/td>8.0 TB\/s<\/td>NVIDIA (\u5fae\u5f31\u4f18\u52bf)<\/td><\/tr>
BF16 \u7b97\u529b (Dense)<\/strong><\/td>~2,307 TFLOPS<\/td>~2,250 TFLOPS<\/td>\u5e73\u624b (\u6781\u5ea6\u63a5\u8fd1)<\/strong><\/td><\/tr>
FP8 \u7b97\u529b (Dense)<\/strong><\/td>~4,614 TFLOPS<\/td>~4,500 TFLOPS<\/td>\u5e73\u624b<\/td><\/tr>
\u4e92\u8054\u5e26\u5bbd (\u5355\u82af\u7247)<\/strong><\/td>1,200 GB\/s (ICI)<\/td>1,800 GB\/s (NVLink 5)<\/td>NVIDIA (\u5355\u70b9\u5e26\u5bbd)<\/td><\/tr>
\u6700\u5927\u96c6\u7fa4\u89c4\u6a21<\/strong><\/td>9,216 chips (\u5355 Pod)<\/td>72 chips (NVL72) \/ SuperPod<\/td>Google (\u5355 Pod \u89c4\u6a21)<\/td><\/tr>
\u529f\u8017 (TDP)<\/strong><\/td>\u672a\u516c\u5f00 (est. ~900W \u7ea7\u522b)<\/td>1000W – 1200W<\/td>TPU (\u80fd\u6548\u6bd4\u901a\u5e38\u66f4\u9ad8)<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
\n

Gemini \u6838\u5fc3\u6d1e\u5bdf\uff1a<\/p>\n\n\n\n

\u6ce8\u610f\u5230\u90a3\u4e2a\u60ca\u4eba\u7684\u53d8\u5316\u4e86\u5417\uff1fTPU v7 \u7684\u663e\u5b58\u5bb9\u91cf\uff08192GB\uff09\u7ec8\u4e8e\u8ffd\u5e73\u4e86 NVIDIA\u3002 \u8fc7\u53bb\u5f00\u53d1\u8005\u4e0d\u613f\u610f\u7528 TPU \u7684\u6838\u5fc3\u539f\u56e0\u4e4b\u4e00\u662f\u5927\u6a21\u578b\u8bad\u7ec3\u65f6\u663e\u5b58\u4e0d\u8db3\u5bfc\u81f4\u5207\u5206\u56f0\u96be\uff0c\u73b0\u5728\u8fd9\u4e2a\u74f6\u9888\u88ab\u5f7b\u5e95\u6d88\u9664\u4e86\u3002<\/p>\n<\/blockquote>\n\n\n\n

\n\n\n\n

3. \u67b6\u6784\u6df1\u5ea6\u89e3\u6790\uff1a\u4e24\u79cd\u54f2\u5b66\u7684\u78b0\u649e<\/h1>\n\n\n\n

3.1 NVIDIA Blackwell\uff1a\u5355\u4f53\u6027\u80fd\u7684\u66b4\u529b\u7f8e\u5b66<\/h2>\n\n\n\n

NVIDIA \u7684\u8bbe\u8ba1\u54f2\u5b66\u662f**\u201c\u8ba9\u5355\u4e2a GPU \u5c3d\u53ef\u80fd\u5f3a\u5927\uff0c\u5e76\u5728\u673a\u67b6\u5185\u901a\u8fc7 NVLink \u5b9e\u73b0\u5185\u5b58\u7edf\u4e00\u201d**\u3002<\/p>\n\n\n\n

    \n
  • NVL72 \u67b6\u6784\uff1a<\/strong> Blackwell \u6700\u6838\u5fc3\u7684\u6740\u624b\u950f\u4e0d\u662f\u5355\u4e2a B200 \u82af\u7247\uff0c\u800c\u662f NVL72 \u673a\u67b6\u3002\u901a\u8fc7\u94dc\u7f06\u80cc\u677f\uff0c72\u4e2a GPU \u88ab\u8fde\u63a5\u6210\u4e00\u4e2a\u5de8\u5927\u7684\u201c\u8d85\u7ea7 GPU\u201d\uff0c\u5171\u4eab 13.5TB \u7684 HBM \u663e\u5b58\u3002<\/li>\n\n\n\n
  • \u4f18\u52bf\uff1a<\/strong> \u5bf9\u4e8e\u5728\u8be5\u663e\u5b58\u8303\u56f4\u5185\u80fd\u653e\u4e0b\u7684\u6a21\u578b\uff08\u5982 GPT-4 \u7684\u5355\u4e2a MoE \u4e13\u5bb6\u5c42\uff09\uff0c\u901a\u4fe1\u5ef6\u8fdf\u6781\u4f4e\uff0c\u7f16\u7a0b\u4f53\u9a8c\u6781\u4f73\uff08\u770b\u8d77\u6765\u50cf\u4e00\u5f20\u5361\uff09\u3002<\/li>\n<\/ul>\n\n\n\n

    3.2 Google TPU v7\uff1a\u6781\u81f4\u7684\u6269\u5c55\u4e0e\u5149\u4e92\u8054<\/h2>\n\n\n\n

    Google \u7684\u54f2\u5b66\u662f**\u201c\u5f31\u5316\u5355\u4f53\u5dee\u5f02\uff0c\u5f3a\u5316\u7cfb\u7edf\u7ea7\u541e\u5410\u4e0e\u80fd\u6548\u201d**\u3002<\/p>\n\n\n\n

      \n
    • \u5149\u8def\u4ea4\u6362 (OCS – Optical Circuit Switching)\uff1a<\/strong> \u8fd9\u662f TPU \u7684\u62a4\u57ce\u6cb3\u3002TPU v7 \u914d\u5408 Google \u6807\u5fd7\u6027\u7684 OCS \u4ea4\u6362\u673a\uff0c\u53ef\u4ee5\u5728\u6570\u5343\u4e2a\u82af\u7247\u4e4b\u95f4\u52a8\u6001\u8c03\u6574\u62d3\u6251\u7ed3\u6784\uff083D Torus\uff09\u3002<\/li>\n\n\n\n
    • Pod \u89c4\u6a21\uff1a<\/strong> \u4e00\u4e2a TPU v7 Pod \u53ef\u4ee5\u5305\u542b 9,216<\/strong> \u4e2a\u82af\u7247\u3002\u76f8\u6bd4\u4e4b\u4e0b\uff0cNVIDIA \u9700\u8981\u901a\u8fc7 InfiniBand\/Ethernet \u4ea4\u6362\u673a\u5c42\u5c42\u4e92\u8054\u624d\u80fd\u8fbe\u5230\u8fd9\u4e2a\u89c4\u6a21\uff0c\u8fd9\u5f15\u5165\u4e86\u66f4\u9ad8\u7684\u5ef6\u8fdf\u548c\u590d\u6742\u6027\u3002TPU \u5728 Pod \u5185\u90e8\u662f\u539f\u751f\u76f4\u8fde\u7684 ICI (Inter-Chip Interconnect) \u534f\u8bae\u3002<\/li>\n<\/ul>\n\n\n\n

      \u80dc\u8d1f\u624b\uff1a<\/strong><\/p>\n\n\n\n

        \n
      • \u5982\u679c\u4f60\u9700\u8981\u8bad\u7ec3\u4e00\u4e2a 10\u4e07\u4ebf\u53c2\u6570<\/strong> \u7684\u8d85\u7ea7\u6a21\u578b\uff0cTPU v7 \u7684\u8d85\u5927\u89c4\u6a21\u539f\u751f\u4e92\u8054\uff08ICI\uff09\u53ef\u80fd\u6bd4 NVIDIA \u7684 InfiniBand \u7f51\u7edc\u66f4\u9ad8\u6548\uff0c\u4e14\u6210\u672c\u66f4\u4f4e\u3002<\/li>\n\n\n\n
      • \u5982\u679c\u4f60\u9700\u8981\u505a \u6781\u81f4\u4f4e\u5ef6\u8fdf\u7684\u63a8\u7406<\/strong> \u6216\u8005\u6a21\u578b\u5927\u5c0f\u5728 10TB \u4ee5\u5185\uff0cNVIDIA NVL72 \u67b6\u6784\u7684\u201c\u7edf\u4e00\u5185\u5b58\u201d\u4f53\u9a8c\u662f\u65e0\u654c\u7684\u3002<\/li>\n<\/ul>\n\n\n\n
        \n\n\n\n

        4. \u8f6f\u4ef6\u751f\u6001\uff1aCUDA \u7684\u62a4\u57ce\u6cb3\u8fd8\u5728\u5417\uff1f<\/h1>\n\n\n\n

        \u8fd9\u662f\u5927\u591a\u6570\u4f01\u4e1a\u4e0d\u6562\u8f7b\u6613\u5207\u6362\u5230 TPU \u7684\u6839\u672c\u539f\u56e0\u3002\u4f46\u5728 2026 \u5e74\uff0c\u60c5\u51b5\u6709\u6240\u597d\u8f6c\u3002<\/p>\n\n\n\n

        4.1 NVIDIA\uff1aCUDA + NIMs<\/h2>\n\n\n\n

        NVIDIA \u4e0d\u4ec5\u4ec5\u5356\u82af\u7247\uff0c\u5b83\u5728\u5356\u670d\u52a1\u3002<\/p>\n\n\n\n

          \n
        • CUDA\uff1a<\/strong> \u4f9d\u7136\u662f\u5e95\u5c42\u6027\u80fd\u4f18\u5316\u7684\u738b\u8005\u3002<\/li>\n\n\n\n
        • NVIDIA NIMs (NeMo Inference Microservices)\uff1a<\/strong> \u5230 2026 \u5e74\uff0cNVIDIA \u5df2\u7ecf\u5c06\u5176\u8f6f\u4ef6\u6808\u9ad8\u5ea6\u5bb9\u5668\u5316\u3002\u4f01\u4e1a\u4e0d\u9700\u8981\u5199 CUDA \u4ee3\u7801\uff0c\u76f4\u63a5\u8c03\u7528 NIMs \u5fae\u670d\u52a1\u5373\u53ef\u90e8\u7f72 Llama 4 \u6216 Gemini \u7b49\u5f00\u6e90\u6a21\u578b\u3002\u8fd9\u5927\u5927\u964d\u4f4e\u4e86 NVIDIA GPU \u7684\u4f7f\u7528\u95e8\u69db\u3002<\/li>\n<\/ul>\n\n\n\n

          4.2 Google\uff1aJAX + PyTorch\/XLA<\/h2>\n\n\n\n

          Google \u7ec8\u4e8e\u610f\u8bc6\u5230\u4e86 PyTorch \u7684\u7edf\u6cbb\u5730\u4f4d\u3002<\/p>\n\n\n\n

            \n
          • PyTorch\/XLA \u7684\u6210\u719f\uff1a<\/strong> \u5728 2024-2025 \u5e74\u95f4\uff0cGoogle \u6295\u5165\u4e86\u5de8\u5927\u8d44\u6e90\u4f18\u5316 PyTorch \u5728 TPU \u4e0a\u7684\u8868\u73b0\u3002\u73b0\u5728\uff0cTPU v7 \u5bf9 PyTorch \u7684\u652f\u6301\u5df2\u7ecf\u8fbe\u5230\u201cFirst Class\u201d\u7ea7\u522b\u3002\u5927\u90e8\u5206\u4e3b\u6d41\u6a21\u578b\uff08Transformer \u7c7b\uff09\u53ea\u9700\u8981\u6539\u52a8\u51e0\u884c\u4ee3\u7801\u5373\u53ef\u8fd0\u884c\u3002<\/li>\n\n\n\n
          • JAX \u7684\u5d1b\u8d77\uff1a<\/strong> \u5bf9\u4e8e\u524d\u6cbf\u7814\u7a76\u8005\uff0cJAX \u4f9d\u7136\u662f TPU \u7684\u795e\u5668\u3002\u5b83\u5728\u5904\u7406\u5927\u89c4\u6a21\u5e76\u884c\u8bad\u7ec3\u65f6\u7684 pmap<\/code> \u548c shard_map<\/code> \u539f\u8bed\uff0c\u6bd4 PyTorch \u7684 DDP\/FSDP \u66f4\u52a0\u76f4\u89c2\u548c\u53ef\u63a7\u3002<\/li>\n<\/ul>\n\n\n\n

            \u8fc1\u79fb\u5efa\u8bae\uff1a<\/strong><\/p>\n\n\n\n

              \n
            • \u5982\u679c\u662f \u8001\u65e7\u4ee3\u7801\u5e93<\/strong> \u6df1\u5ea6\u4f9d\u8d56\u81ea\u5b9a\u4e49 CUDA Kernel\uff0c\u7559\u5728 NVIDIA \u751f\u6001\u3002<\/li>\n\n\n\n
            • \u5982\u679c\u662f \u65b0\u9879\u76ee<\/strong>\uff0c\u6216\u8005\u4f7f\u7528\u6807\u51c6\u7684 Transformer \u67b6\u6784\uff0c\u8fc1\u79fb\u5230 TPU v7 \u7684\u6210\u672c\u5df2\u964d\u81f3\u5386\u53f2\u6700\u4f4e\u3002<\/li>\n<\/ul>\n\n\n\n
              \n\n\n\n

              5. \u7ecf\u6d4e\u8d26\uff1aTCO \u4e0e \u53ef\u83b7\u5f97\u6027<\/h1>\n\n\n\n

              <\/h4>\n\n\n\n
                \n
              • NVIDIA\uff1a<\/strong> \u5c3d\u7ba1\u4f9b\u5e94\u6709\u6240\u7f13\u89e3\uff0c\u4f46\u5728 2026 \u5e74 B200 \u4f9d\u7136\u662f\u786c\u901a\u8d27\uff0c\u6ea2\u4ef7\u8f83\u9ad8\u3002\u4f60\u4e0d\u4ec5\u8981\u4e70 GPU\uff0c\u8fd8\u8981\u4e70\u6602\u8d35\u7684 InfiniBand \u4ea4\u6362\u673a\u3001BlueField DPU \u7b49\u914d\u5957\u8bbe\u65bd\uff08\u6240\u8c13\u7684\u201cNVIDIA \u7a0e\u201d\uff09\u3002<\/li>\n\n\n\n
              • Google TPU\uff1a<\/strong> \u4f60\u4e70\u4e0d\u5230 TPU\uff0c\u4f60\u53ea\u80fd\u79df\u3002Google Cloud \u901a\u8fc7\u5782\u76f4\u6574\u5408\uff08\u81ea\u7814\u82af\u7247+\u81ea\u7814\u7f51\u7edc+\u81ea\u7814\u6570\u636e\u4e2d\u5fc3\uff09\uff0c\u901a\u5e38\u80fd\u63d0\u4f9b\u6bd4\u540c\u7ea7\u522b NVIDIA \u5b9e\u4f8b\u4f4e 30%-50%<\/strong> \u7684\u4ef7\u683c\u3002<\/li>\n<\/ul>\n\n\n\n

                5.2 \u80fd\u6548\u6bd4 (Performance per Watt)<\/h4>\n\n\n\n

                \u8fd9\u662f TPU v7 \u7684\u6740\u624b\u950f\u3002\u5f97\u76ca\u4e8e\u6db2\u51b7\u8bbe\u8ba1\u548c\u4e13\u7528 ASIC \u67b6\u6784\uff08\u5254\u9664\u4e86\u56fe\u5f62\u6e32\u67d3\u7b49\u5197\u4f59\u5355\u5143\uff09\uff0cTPU v7 \u5728 AI \u8d1f\u8f7d\u4e0b\u7684\u80fd\u6548\u6bd4\u7ea6\u4e3a NVIDIA Blackwell \u7684 1.5\u500d\u3002<\/p>\n\n\n\n

                \u5bf9\u4e8e\u5728\u8fd9\u4e2a\u7535\u529b\u7d27\u7f3a\uff08Power Constrained\uff09\u7684\u5e74\u4ee3\u8fd0\u884c\u5927\u89c4\u6a21\u63a8\u7406\u670d\u52a1\u7684\u516c\u53f8\u6765\u8bf4\uff0cTPU v7 \u610f\u5473\u7740\u80fd\u5728\u540c\u6837\u7684\u7535\u529b\u914d\u989d\u4e0b\uff0c\u670d\u52a1\u66f4\u591a\u7684\u7528\u6237\u3002<\/p>\n\n\n\n

                \n\n\n\n

                6. \u5c55\u671b\uff1aRubin \u5c31\u5728\u8f6c\u89d2<\/h1>\n\n\n\n

                \u867d\u7136 TPU v7 \u6b64\u523b\u4e0e Blackwell \u6253\u5f97\u96be\u89e3\u96be\u5206\uff0c\u4f46\u6211\u4eec\u5fc5\u987b\u770b\u5411 2026 \u4e0b\u534a\u5e74\u3002<\/p>\n\n\n\n

                NVIDIA \u5df2\u7ecf\u5728\u8def\u7ebf\u56fe\u4e0a\u9884\u544a\u4e86 Rubin (R100) \u67b6\u6784\uff0c\u9884\u8ba1\u5c06\u642d\u8f7d HBM4 \u663e\u5b58\uff0c\u5e26\u5bbd\u53ef\u80fd\u4f1a\u518d\u6b21\u7ffb\u500d\u3002<\/p>\n\n\n\n

                Google \u7684\u5e94\u5bf9\u7b56\u7565\u901a\u5e38\u662f\u66f4\u5feb\u7684\u8fed\u4ee3\u8282\u594f\uff08TPU v8 \u5df2\u7ecf\u5728\u7814\u53d1\u4e2d\uff0c\u4ee3\u53f7\u53ef\u80fd\u662f “Jade” \u6216\u5176\u4ed6\u77ff\u7269\u540d\uff09\uff0c\u4ee5\u53ca\u901a\u8fc7 Axion (\u81ea\u7814 ARM CPU) \u4e0e TPU \u7684\u534f\u540c\u6765\u8fdb\u4e00\u6b65\u964d\u4f4e\u7cfb\u7edf\u7ea7\u6210\u672c\u3002<\/p>\n\n\n\n

                \n\n\n\n

                7. \u603b\u7ed3\u4e0e\u51b3\u7b56\u6307\u5357<\/h1>\n\n\n\n

                2026 \u5e74\u7684 AI \u82af\u7247\u6218\u573a\uff0c\u4e0d\u518d\u6709\u7edd\u5bf9\u7684\u8d62\u5bb6\uff0c\u53ea\u6709\u9002\u5408\u4e0d\u540c\u573a\u666f\u7684\u5de5\u5177\u3002<\/p>\n\n\n\n

                \u9009\u62e9 NVIDIA Blackwell (B200\/GB200) \u5982\u679c\uff1a<\/strong><\/p>\n\n\n\n

                  \n
                • \u4f60\u7684\u56e2\u961f\u7531\u8d44\u6df1 CUDA \u5de5\u7a0b\u5e08\u7ec4\u6210\uff0c\u4e14\u4f9d\u8d56\u5927\u91cf\u81ea\u5b9a\u4e49\u7b97\u5b50\u3002<\/li>\n\n\n\n
                • \u4f60\u9700\u8981\u6781\u9ad8\u7684\u5355\u673a\/\u5355\u8282\u70b9\u5e26\u5bbd\uff08\u4f8b\u5982 NVL72 \u7684\u7edf\u4e00\u5185\u5b58\u67b6\u6784\uff09\u3002<\/li>\n\n\n\n
                • \u4f60\u7684\u4e1a\u52a1\u4e0d\u4ec5\u5728\u4e91\u7aef\uff0c\u8fd8\u6d89\u53ca\u8fb9\u7f18\u8ba1\u7b97\u6216\u79c1\u6709\u5316\u90e8\u7f72\uff08TPU \u65e0\u6cd5\u79c1\u6709\u5316\u90e8\u7f72\uff09\u3002<\/li>\n\n\n\n
                • \u4f60\u9700\u8981\u6700\u5e7f\u6cdb\u7684\u5f00\u6e90\u793e\u533a\u652f\u6301\uff08GitHub \u4e0a\u7684\u4ee3\u7801\u9ed8\u8ba4\u90fd\u662f\u8dd1\u5728 NVIDIA \u4e0a\u7684\uff09\u3002<\/li>\n<\/ul>\n\n\n\n

                  \u9009\u62e9 Google TPU v7 (Ironwood) \u5982\u679c\uff1a<\/strong><\/p>\n\n\n\n

                    \n
                  • \u4f60\u7684\u4e1a\u52a1\u5b8c\u5168\u57fa\u4e8e Google Cloud\u3002<\/li>\n\n\n\n
                  • \u4f60\u5173\u6ce8 \u5927\u89c4\u6a21\u5206\u5e03\u5f0f\u8bad\u7ec3<\/strong> \u7684\u6027\u4ef7\u6bd4\u548c\u7ebf\u6027\u6269\u5c55\u80fd\u529b\uff08TPU \u5728\u6570\u5343\u5361\u89c4\u6a21\u4e0b\u7684\u6269\u5c55\u6027\u4f18\u4e8e GPU\uff09\u3002<\/li>\n\n\n\n
                  • \u4f60\u5bf9 \u63a8\u7406\u6210\u672c<\/strong> \u6781\u5176\u654f\u611f\uff08TPU v7 \u7684\u6027\u4ef7\u6bd4\u548c\u80fd\u6548\u6bd4\u6781\u5177\u5438\u5f15\u529b\uff09\u3002<\/li>\n\n\n\n
                  • \u4f60\u4f7f\u7528 JAX \u8fdb\u884c\u524d\u6cbf\u7814\u7a76\uff0c\u6216\u8005\u4f7f\u7528\u6807\u51c6\u7684 PyTorch \u6a21\u578b\uff08Transformer\/Diffusion\uff09\u3002<\/li>\n<\/ul>\n\n\n\n

                    \u4e00\u53e5\u8bdd\u5efa\u8bae\uff1a<\/p>\n\n\n\n

                    \u5bf9\u4e8e\u5927\u591a\u6570\u5bfb\u6c42\u964d\u672c\u589e\u6548\u7684 AI \u5e94\u7528\u516c\u53f8\uff0c2026 \u5e74\u662f\u5c1d\u8bd5\u5c06\u63a8\u7406\u8d1f\u8f7d\u8fc1\u79fb\u5230 TPU v7 \u7684\u6700\u4f73\u65f6\u673a\uff1b\u800c\u5bf9\u4e8e\u8ffd\u6c42\u6781\u81f4\u6027\u80fd\u4e0a\u9650\u7684\u57fa\u7840\u6a21\u578b\u8bad\u7ec3\u5b9e\u9a8c\u5ba4\uff0cNVIDIA Blackwell \u4f9d\u7136\u662f\u76ee\u524d\u6700\u7a33\u59a5\u7684\u201c\u66b4\u529b\u7f8e\u5b66\u201d\u4ee3\u8868\u3002<\/p>\n\n\n\n

                    \n\n\n\n

                    \u9644\u5f55\uff1a\u6280\u672f\u89c4\u683c\u901f\u67e5\u8868<\/h3>\n\n\n\n
                    \u7279\u6027<\/strong><\/td>TPU v7 (Ironwood) Pod<\/strong><\/td>NVIDIA GB200 NVL72<\/strong><\/td><\/tr><\/thead>
                    \u4e92\u8054\u6280\u672f<\/strong><\/td>OCS (\u5149\u8def\u4ea4\u6362)<\/td>NVLink Switch (\u94dc\u7f06)<\/td><\/tr>
                    \u7f51\u7edc\u62d3\u6251<\/strong><\/td>3D Torus (\u52a8\u6001\u53ef\u914d)<\/td>All-to-All (\u673a\u67b6\u5185)<\/td><\/tr>
                    \u4e3b\u8981\u4f18\u52bf<\/strong><\/td>\u6269\u5c55\u6027\u3001\u80fd\u6548\u3001\u4e91\u7aef\u6027\u4ef7\u6bd4<\/td>\u7f16\u7a0b\u6a21\u578b\u7b80\u5355\u3001\u751f\u6001\u7edf\u6cbb\u529b<\/td><\/tr>
                    \u9002\u7528\u6846\u67b6<\/strong><\/td>JAX (\u539f\u751f), PyTorch\/XLA<\/td>PyTorch, TensorFlow, JAX<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

                    (\u672c\u6587\u6570\u636e\u57fa\u4e8e 2026 \u5e74 1 \u6708\u516c\u5f00\u6280\u672f\u6587\u6863\u6574\u7406\uff0c\u5b9e\u9645\u6027\u80fd\u53ef\u80fd\u56e0\u5177\u4f53\u5de5\u4f5c\u8d1f\u8f7d\u800c\u5f02\u3002)<\/em><\/p>\n\n\n\n

                    \n\n\n\n

                    <\/p>\n","protected":false},"excerpt":{"rendered":"

                    \u53d1\u5e03\u65e5\u671f\uff1a 2026\u5e741\u67082\u65e5 \u9605\u8bfb\u65f6\u95f4\uff1a \u7ea6 18 \u5206\u949f \u4f5c\u8005\uff1a Gemini (AI \u67b6\u6784\u5206\u6790\u5e08) 1. \u5f15\u8a00\uff1a\u4ece\u201c\u4e00\u5bb6\u72ec\u5927\u201d\u5230\u201c\u53cc\u96c4\u4e89\u9738\u201d \u76f4\u52302024\u5e74\uff0cAI \u786c\u4ef6\u5e02\u573a\u7684\u4e3b\u65cb\u5f8b\u57fa\u672c\u4e0a\u662f\u201cNVIDIA \u53ca\u5176\u8ffd\u8d76\u8005\u201d\u3002\u7136\u800c\uff0c\u968f\u7740\u65f6\u95f4\u63a8\u8fdb\u52302026\u5e74\u521d\uff0c\u683c\u5c40\u53d1\u751f\u4e86\u5fae\u5999\u800c\u6df1\u523b\u7684\u53d8\u5316\u3002 NVIDIA \u51ed\u501f Blackwell \u67b6\u6784\uff08B200\/GB200\uff09\u57282025\u5e74\u6a2a\u626b\u4e86\u6570\u636e\u4e2d\u5fc3\uff0c\u4f46 Google \u5e76\u6ca1\u6709\u5750\u4ee5\u5f85\u6bd9\u3002\u7ee7 Trillium (TPU v6) \u4e4b\u540e\uff0cGoogle…<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[858],"tags":[857,915,918,916],"class_list":["post-10540","post","type-post","status-publish","format-standard","hentry","category-ai","tag-ai","tag-gpu","tag-nvidia","tag-tpu","entry","simple"],"_links":{"self":[{"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/posts\/10540","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/comments?post=10540"}],"version-history":[{"count":1,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/posts\/10540\/revisions"}],"predecessor-version":[{"id":10541,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/posts\/10540\/revisions\/10541"}],"wp:attachment":[{"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/media?parent=10540"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/categories?post=10540"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/zxi.mytechroad.com\/blog\/wp-json\/wp\/v2\/tags?post=10540"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}