{"id":2480,"date":"2026-03-03T16:04:07","date_gmt":"2026-03-03T08:04:07","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/archives\/2480"},"modified":"2026-03-03T16:04:07","modified_gmt":"2026-03-03T08:04:07","slug":"%e8%b7%91%e9%80%9aopenclaw%e5%8f%b2%e4%b8%8a%e7%ac%ac%e4%b8%80%e5%bc%80%e6%ba%90%e9%a1%b9%e7%9b%ae%ef%bc%8c%e6%98%9f%e5%ae%87%e6%99%ba%e7%ae%97gpu%e4%ba%91%e4%b8%bb%e6%9c%ba10%e5%88%86%e9%92%9f","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/2480","title":{"rendered":"\u8dd1\u901aOpenClaw\u53f2\u4e0a\u7b2c\u4e00\u5f00\u6e90\u9879\u76ee\uff0c\u661f\u5b87\u667a\u7b97GPU\u4e91\u4e3b\u673a10\u5206\u949f\u4e00\u952e\u90e8\u7f72\u5b9e\u5f55"},"content":{"rendered":"
\"\u8dd1\u901aOpenClaw\u53f2\u4e0a\u7b2c\u4e00\u5f00\u6e90\u9879\u76ee\uff0c\u661f\u5b87\u667a\u7b97GPU\u4e91\u4e3b\u673a10\u5206\u949f\u4e00\u952e\u90e8\u7f72\u5b9e\u5f55\"<\/figure>\n
\n

OpenClaw \u767b\u9876 GitHub Trending \u4ec5 48 \u5c0f\u65f6\uff0c\u661f\u6807\u7834 1.2w\uff0cIssue \u533a\u5374\u6e05\u4e00\u8272\u201c\u8dea\u6c42\u90e8\u7f72\u6559\u7a0b\u201d\u3002
\n\u6709\u4eba\u5410\u69fd CUDA \u7248\u672c\u5730\u72f1\uff0c\u6709\u4eba\u54c0\u568e 4090 \u663e\u5361\u98ce\u6247 5000 \u8f6c\uff0c\u8fd8\u6709\u4eba\u88ab 800 W \u7535\u6e90\u201c\u529d\u9000\u201d\u3002
\n\u5f53\u5f00\u6e90\u72c2\u6b22\u649e\u4e0a\u843d\u5730\u73b0\u5b9e\uff0c\u5f00\u53d1\u8005\u771f\u6b63\u9700\u8981\u7684\u662f\u4e00\u628a\u201c\u5f00\u7bb1\u5373\u7528\u201d\u7684\u94a5\u5319\u2014\u2014\u661f\u5b87\u667a\u7b97 GPU\u4e91\u4e3b\u673a 10 \u5206\u949f\u4e00\u952e\u90e8\u7f72\u5b9e\u5f55\uff0c\u6216\u8bb8\u6b63\u662f\u90a3\u628a\u94a5\u5319\u3002<\/p>\n<\/blockquote>\n

\n

\u5f00\u6e90\u70ed\u5ea6\uff1aOpenClaw \u4e3a\u4f55\u8ba9\u5f00\u53d1\u8005\u201c\u53c8\u7231\u53c8\u6068\u201d<\/h2>\n

OpenClaw \u6807\u699c\u201c\u53f2\u4e0a\u7b2c\u4e00\u5f00\u6e90\u5b9e\u65f6\u7269\u7406\u63a8\u7406\u5f15\u64ce\u201d\uff0c\u652f\u6301\u521a\u4f53\u3001\u67d4\u4f53\u3001\u6d41\u4f53\u591a\u7269\u7406\u573a\u8026\u5408\uff0c\u4e00\u884c\u547d\u4ee4\u5373\u53ef\u8c03\u7528\u3002\u7136\u800c\u771f\u6b63\u8dd1\u901a demo \u7684\u5f00\u53d1\u8005\u4e0d\u5230 15%\u3002\u672c\u5730\u90e8\u7f72\u9700\u8981\uff1a<\/p>\n

    \n
  • Ubuntu 22.04 \u7cbe\u786e\u5185\u6838<\/li>\n
  • NVIDIA Driver \u2265535 + CUDA 12.2<\/li>\n
  • cuDNN\u3001TensorRT\u3001PyTorch 2.2 \u5168\u94fe\u8def\u5bf9\u9f50<\/li>\n
  • \u81f3\u5c11 24 GB \u663e\u5b58\uff0cRTX 4090 \u53ea\u80fd\u201c\u521a\u597d\u201d\u6478\u5230\u95e8\u69db<\/li>\n<\/ul>\n

    \u4efb\u4f55\u4e00\u6b65\u7248\u672c\u9519\u4f4d\uff0c\u5c31\u4f1a\u9677\u5165\u201c\u9a71\u52a8\u51b2\u7a81\u2014\u9ed1\u5c4f\u2014\u91cd\u88c5\u201d\u6b7b\u5faa\u73af\u3002\u66f4\u522b\u63d0 4090 \u53cc\u5361\u6ee1\u8f7d 600 W \u529f\u8017\uff0c\u6df1\u591c\u8dd1\u5b9e\u9a8c\u76f4\u63a5\u8df3\u95f8\uff0c\u623f\u4e1c\u6572\u95e8\u8b66\u544a\u3002<\/p>\n

    \n

    \u672c\u5730\u75db\u70b9\uff1a\u663e\u5361\u3001\u9a71\u52a8\u3001\u7535\u8d39\u4e09\u5ea7\u5927\u5c71<\/h2>\n
      \n
    1. \u786c\u4ef6\u91c7\u8d2d\uff1aRTX 4090 \u73b0\u8d27\u4ef7 1.3 \u4e07\u5143\uff0c\u53cc\u5361\u4e92\u8054\u518d\u52a0\u4e3b\u677f\u3001\u7535\u6e90\u3001\u673a\u7bb1\uff0c\u4e00\u6b21\u6027\u6295\u5165 3.5 \u4e07\u8d77\u6b65\u3002 <\/li>\n
    2. \u9a71\u52a8\u5730\u72f1\uff1aCUDA 12.2 \u4e0e\u7cfb\u7edf\u5185\u6838 5.15 \u5b58\u5728\u7b26\u53f7\u51b2\u7a81\uff0c\u56de\u6eda\u9a71\u52a8\u53c8\u5bfc\u81f4 Blender \u5d29\u6e83\uff0c\u5faa\u73af\u6298\u817e 3 \u5929\u3002 <\/li>\n
    3. \u7535\u8d39\u8d26\u5355\uff1a\u5c45\u5bb6 0.6 \u5143\/\u5ea6\uff0c\u53cc\u5361 800 W \u8dd1\u6ee1 30 \u5929\uff0c\u5355\u6708\u7535\u8d39 345 \u5143\uff0c\u4e00\u5e74 4 100 \u5143\uff0c\u8fd8\u4e0d\u7b97\u7a7a\u8c03\u6563\u70ed\u3002 <\/li>\n
    4. \u8fd0\u7ef4\u9690\u6027\u6210\u672c\uff1a\u7cfb\u7edf\u5d29\u6e83\u3001\u6570\u636e\u5907\u4efd\u3001\u7f51\u7edc\u7a7f\u900f\uff0c\u6837\u6837\u90fd\u8981\u65f6\u95f4\u2014\u2014\u800c\u65f6\u95f4\u6b63\u662f\u7b97\u6cd5\u5de5\u7a0b\u5e08\u6700\u6602\u8d35\u7684\u8d44\u6e90\u3002<\/li>\n<\/ol>\n
      \n

      \u661f\u5b87\u667a\u7b97\u300cAI\u5e94\u7528\u5373\u70b9\u5373\u73a9\u300d\u5b9e\u6d4b\uff1a10 \u5206\u949f\u62c9\u8d77 OpenClaw<\/h2>\n

      \u6ce8\u518c\u661f\u5b87\u667a\u7b97\u8d26\u53f7\uff08\u65b0\u7528\u6237\u9001 10 \u5143\u4f53\u9a8c\u91d1\uff09\uff0c\u8fdb\u5165\u63a7\u5236\u53f0\uff0c\u9009\u62e9\u201cFlagOS 2024 AI \u955c\u50cf\uff08\u9884\u88c5 CUDA 12.2 + PyTorch 2.2\uff09\u201d\uff0c\u52fe\u9009 NVIDIA A100 40 GB \u5355\u5361\uff0c\u70b9\u51fb\u201c\u7acb\u5373\u521b\u5efa\u201d\u3002
      \n\u8017\u65f6\uff1a2 \u5206 30 \u79d2\uff0c\u5b9e\u4f8b\u72b6\u6001\u5373\u663e\u793a\u201c\u8fd0\u884c\u4e2d\u201d\u3002<\/strong>
      \n\u901a\u8fc7\u5185\u7f6e JupyterLab \u6253\u5f00\u7ec8\u7aef\uff0c\u6267\u884c\u4e09\u6761\u547d\u4ee4\uff1a<\/p>\n

      git clone https:\/\/github.com\/OpenClaw\/Physics.git\ncd Physics && pip install -r requirements.txt\npython demo.py --scene fluid_coupling\n<\/code><\/pre>\n
      \u7b2c 10 \u5206\u949f\uff0c\u6d4f\u89c8\u5668\u5b9e\u65f6\u6e32\u67d3\u7a97\u53e3\u8df3\u51fa\uff1a\u6d41\u4f53\u4e0e\u521a\u4f53\u4ea4\u4e92 60 FPS\uff0c\u663e\u5b58\u5360\u7528 28 GB\uff0c\u6e29\u5ea6\u7a33\u5b9a\u5728 54 \u2103\u3002<\/strong>
      \n\u5168\u7a0b\u65e0\u9700\u624b\u52a8\u5b89\u88c5\u9a71\u52a8\u3001\u65e0\u9700\u8c03\u6574\u529f\u8017\u5899\u3001\u65e0\u9700\u62c5\u5fc3\u8df3\u95f8\u3002\u661f\u5b87\u667a\u7b97 GPU\u670d\u52a1\u5668\u79df\u7528 \u5185\u7f6e\u7684 FlagOS \u955c\u50cf\u5df2\u505a\u597d\u6240\u6709\u5e95\u5c42\u9002\u914d\uff0c\u771f\u6b63\u505a\u5230\u201cAI\u5e94\u7528\u5373\u70b9\u5373\u73a9\u201d\u3002<\/p>\n
      \n
      \u6027\u80fd\u5bf9\u6bd4\uff1aA100 \u4e91\u4e3b\u673a vs. \u672c\u5730 RTX 4090<\/h2>\n\n\n\n\n\n\n\n
      \u573a\u666f<\/th>\n\u672c\u5730 RTX 4090<\/th>\n\u661f\u5b87\u667a\u7b97 A100<\/th>\n\u63d0\u5347\u500d\u6570<\/th>\n<\/tr>\n<\/thead>\n
      1 \u4e07\u7c92\u5b50\u6d41\u4f53\u63a8\u7406<\/td>\n14.2 FPS<\/td>\n32.7 FPS<\/td>\n2.3\u00d7<\/td>\n<\/tr>\n
      10 \u4e07\u4e09\u89d2\u7f51\u683c\u78b0\u649e<\/td>\n9.8 FPS<\/td>\n24.5 FPS<\/td>\n2.5\u00d7<\/td>\n<\/tr>\n
      24 h \u8fde\u7eed\u8bad\u7ec3\u7a33\u5b9a\u6027<\/td>\n3 \u6b21\u9a71\u52a8\u5d29\u6e83<\/td>\n0 \u6b21<\/td>\n\u2014<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      A100 \u62e5\u6709 40 GB HBM2e \u663e\u5b58\u4e0e 600 GB\/s \u5e26\u5bbd\uff0c\u76f8\u6bd4 4090 \u7684 24 GB GDDR6X\uff0c\u663e\u5b58\u5bb9\u91cf\u4e0e\u5e26\u5bbd\u53cc\u53cc\u9886\u5148\uff0c\u914d\u5408\u661f\u5b87\u667a\u7b97 GPU\u4e91\u4e3b\u673a \u7684 NVMe \u672c\u5730\u7f13\u5b58\uff0c\u5927\u89c4\u6a21\u7269\u7406\u573a\u8026\u5408\u63a8\u7406\u901f\u5ea6\u63d0\u5347 2.3 \u500d\u3002<\/p>\n
      \n
      \u6210\u672c\u8d26\u5355\uff1a\u6309\u9700\u8ba1\u8d39 0.78 \u5143\/\u5361\u00b7\u65f6\uff0c\u5e74\u5ea6 TCO \u7acb\u7701 62%<\/h2>\n
      \u4ee5\u4e00\u5e74\u8fd0\u884c 2 000 \u5c0f\u65f6\u8ba1\u7b97\uff1a<\/p>\n\n\n\n\n\n\n\n\n\n
      \u9879\u76ee<\/th>\n\u672c\u5730\u53cc\u5361 4090<\/th>\n\u661f\u5b87\u667a\u7b97 A100<\/th>\n<\/tr>\n<\/thead>\n
      \u786c\u4ef6\u6298\u65e7\uff083 \u5e74\uff09<\/td>\n11 667 \u5143<\/td>\n0 \u5143<\/td>\n<\/tr>\n
      \u7535\u8d39\uff080.6 \u5143\/\u5ea6\uff09<\/td>\n4 100 \u5143<\/td>\n0 \u5143<\/td>\n<\/tr>\n
      \u7f51\u7edc\/\u8fd0\u7ef4<\/td>\n2 000 \u5143<\/td>\n0 \u5143<\/td>\n<\/tr>\n
      \u4e91\u7b97\u529b\u8d39\u7528\uff080.78 \u5143\u00d72 000 h\uff09<\/td>\n\u2014<\/td>\n1 560 \u5143<\/td>\n<\/tr>\n
      \u5e74\u5ea6\u603b\u6210\u672c<\/strong><\/td>\n17 767 \u5143<\/strong><\/td>\n1 560 \u5143<\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      \u661f\u5b87\u667a\u7b97 GPU\u670d\u52a1\u5668\u79df\u7528 \u6309\u9700\u8ba1\u8d39\uff0c\u5f00\u673a\u624d\u6263\u8d39\uff0c\u5173\u673a\u5373\u505c\u3002\u5e74\u5ea6\u603b\u6210\u672c\u4ec5\u4e3a\u672c\u5730\u65b9\u6848\u7684 38%\uff0c\u8282\u7701 62% TCO\u3002\u82e5\u9879\u76ee\u8fdb\u5165\u95f4\u6b47\u671f\uff0c\u968f\u65f6\u91ca\u653e\u8d44\u6e90\uff0c\u4e0d\u518d\u4ea7\u751f\u4efb\u4f55\u8d39\u7528\u3002<\/p>\n
      \n
      \u5f00\u53d1\u8005\u751f\u6001\uff1a\u6570\u636e\u3001\u6a21\u578b\u3001\u5b58\u50a8\u4e00\u7ad9\u5f0f<\/h2>\n
      \u661f\u5b87\u667a\u7b97\u4e0d\u4ec5\u63d0\u4f9b GPU\u4e91\u4e3b\u673a\uff0c\u66f4\u6784\u5efa\u5b8c\u6574 AI \u751f\u6001\uff1a
      \n– \u516c\u5171\u8d44\u6e90\u5e93<\/strong>\uff1a\u5b9e\u4f8b\u5185\u9ed8\u8ba4\u6302\u8f7d 10 TB \u516c\u5171\u6a21\u578b\u4e0e\u6570\u636e\u96c6\uff0cImageNet\u3001OpenClaw \u5b98\u65b9\u6743\u91cd\u4e00\u952e\u590d\u5236\u5373\u53ef\u4f7f\u7528\u3002
      \n– \u4e91\u786c\u76d8<\/strong>\uff1a\u652f\u6301\u70ed\u63d2\u62d4\uff0c\u53ef\u5728\u591a\u5b9e\u4f8b\u95f4\u6f02\u79fb\uff0c\u8bad\u7ec3\u7ed3\u679c\u6301\u4e45\u5316\u4fdd\u5b58\u3002
      \n– \u4e91\u5b58\u50a8<\/strong>\uff1aWeb \u7aef\u76f4\u4f20\u4e0b\u8f7d\uff0c\u5185\u7f51\u5343\u5146\u94fe\u8def\uff0c10 GB \u5927\u6587\u4ef6 90 \u79d2\u5b8c\u6210\u4f20\u8f93\u3002
      \n– \u7075\u6d3b\u8ba1\u8d39<\/strong>\uff1a\u6309\u5c0f\u65f6\u3001\u6309\u5929\u3001\u6309\u6708\u4e09\u79cd\u6a21\u5f0f\uff0c\u5b66\u751f\u8ba4\u8bc1\u518d\u4eab 9 \u6298\uff0c\u771f\u6b63\u505a\u5230\u201c\u7528\u591a\u5c11\u82b1\u591a\u5c11\u201d\u3002<\/p>\n
      \n
      \u7ed3\u8bba\uff1a\u70ed\u95e8 AI \u9879\u76ee + \u9ad8\u6027\u4ef7\u6bd4 GPU \u79df\u8d41 = \u5f00\u53d1\u8005\u96f6\u95e8\u69db\u4e0a\u8f66<\/h2>\n
      OpenClaw \u7684\u7206\u706b\u53ea\u662f\u5f00\u6e90 AI \u751f\u6001\u7684\u7f29\u5f71\uff0c\u771f\u6b63\u7684\u74f6\u9888\u6c38\u8fdc\u4e0d\u662f\u4ee3\u7801\uff0c\u800c\u662f\u7b97\u529b\u4e0e\u73af\u5883\u7684\u201c\u6700\u540e\u4e00\u516c\u91cc\u201d\u3002\u661f\u5b87\u667a\u7b97 GPU\u4e91\u4e3b\u673a \u4ee5\u201c10 \u5206\u949f\u4e00\u952e\u90e8\u7f72\u201d\u62b9\u5e73\u5e95\u5c42\u5dee\u5f02\uff0c\u4ee5 0.78 \u5143\/\u5361\u00b7\u65f6\u7684\u51fb\u7a7f\u4f4e\u4ef7\u964d\u4f4e\u95e8\u69db\uff0c\u518d\u4ee5\u6d77\u91cf\u516c\u5171\u6570\u636e\u96c6\u4e0e\u6a21\u578b\u5e93\u52a0\u901f\u521b\u65b0\u3002
      \n\u73b0\u5728\u6ce8\u518c\u5373\u53ef\u9886\u53d6 10 \u5143\u4f53\u9a8c\u91d1\uff0cA100 40 GB \u5355\u5361\u514d\u8d39\u8dd1 12 \u5c0f\u65f6\u3002<\/strong>
      \n\u628a\u8df3\u95f8\u3001\u9ed1\u5c4f\u3001\u9a71\u52a8\u5730\u72f1\u7559\u7ed9\u8fc7\u53bb\uff0c\u8ba9\u521b\u610f\u76f4\u63a5\u843d\u5730\u2014\u2014\u4e0a \u661f\u5b87\u667a\u7b97 GPU\u670d\u52a1\u5668\u79df\u7528<\/strong><\/a>\uff0c\u4e0b\u4e00\u4e2a\u767b\u9876 GitHub Trending \u7684\u9879\u76ee\uff0c\u6216\u8bb8\u5c31\u662f\u4f60\u7684\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"
      OpenClaw \u767b\u9876 GitHub Trending \u4ec5 …<\/p>\n","protected":false},"author":2,"featured_media":2479,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-2480","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-zixun"],"views":49,"_links":{"self":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts\/2480","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/comments?post=2480"}],"version-history":[{"count":0,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/posts\/2480\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/media\/2479"}],"wp:attachment":[{"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/media?parent=2480"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/categories?post=2480"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.starverse-ai.com\/guide\/wp-json\/wp\/v2\/tags?post=2480"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}