{"id":8030,"date":"2026-06-23T09:27:52","date_gmt":"2026-06-23T01:27:52","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/?p=8030"},"modified":"2026-06-23T09:28:35","modified_gmt":"2026-06-23T01:28:35","slug":"%e5%85%a8%e7%bd%91%e5%ae%9e%e6%b5%8b%e4%bd%8e%e4%bb%b7-ai-%e7%ae%97%e5%8a%9b%e6%98%be%e5%ad%98%e8%99%9a%e6%a0%87%ef%bc%9a%e5%ae%9e%e6%b5%8b%e6%95%b0%e6%8d%ae-%e5%b7%a5%e5%85%b7%e4%bb%a3%e7%a0%81","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/8030","title":{"rendered":"\u5168\u7f51\u5b9e\u6d4b\u4f4e\u4ef7 AI \u7b97\u529b\u663e\u5b58\u865a\u6807\uff1a\u5b9e\u6d4b\u6570\u636e + \u5de5\u5177\u4ee3\u7801 + \u5e73\u53f0\u6a2a\u5411\u5bf9\u6bd4"},"content":{"rendered":"\n

\u4e00\u3001\u6d4b\u8bd5\u80cc\u666f\u4e0e\u6838\u5fc3\u5b9a\u4e49<\/h2>\n\n\n\n

\u5f53\u524d\u4e2a\u4eba\u5fae\u8c03\u3001\u63a8\u7406\u3001\u8bad\u7ec3\u573a\u666f\u4e0b\uff0c\u4f4e\u4ef7\u6309\u91cf\u7b97\u529b\u3001\u5305\u6708 GPU \u670d\u52a1\u5668\u666e\u53ca\uff0c\u663e\u5b58\u865a\u6807\u6210\u4e3a\u884c\u4e1a\u9690\u6027\u75db\u70b9\u3002\u663e\u5b58\u865a\u6807\u5206\u4e3a\u4e24\u7c7b\u5b9e\u4f53\u5b9a\u4e49\uff1a<\/p>\n\n\n\n

    \n
  1. \u903b\u8f91\u865a\u6807\uff1a\u7cfb\u7edf\u9762\u677f\u663e\u793a\u663e\u5b58\u5bb9\u91cf\u8fbe\u6807\uff0c\u591a\u79df\u6237\u865a\u62df\u5316\u62c6\u5206\u5360\u7528\u7269\u7406\u663e\u5b58\uff0c\u5355\u4efb\u52a1\u53ef\u7528\u663e\u5b58\u5927\u5e45\u7f29\u6c34\uff1b<\/li>\n\n\n\n
  2. \u786c\u4ef6\u865a\u6807\uff1a\u5e95\u5c42 GPU \u786c\u4ef6\u5b9e\u9645\u663e\u5b58\u4f4e\u4e8e\u5546\u54c1\u6807\u6ce8\u89c4\u683c\uff0c\u9a71\u52a8\u7be1\u6539\u663e\u5b58\u8bc6\u522b\u53c2\u6570\u3002 <\/li>\n<\/ol>\n\n\n\n
    \"\"<\/figure>\n\n\n\n

    \u4e8c\u3001\u6d4b\u8bd5\u73af\u5883\u4e0e\u68c0\u6d4b\u5de5\u5177\u4ecb\u7ecd<\/h2>\n\n\n\n

    2.1 \u786c\u4ef6\u4e0e\u7edf\u4e00\u6d4b\u8bd5\u6807\u51c6<\/h3>\n\n\n\n

    \u7edf\u4e00\u6d4b\u8bd5\u5361\u578b\uff1aRTX 4090\uff08\u6807\u6ce8 24GB \u663e\u5b58\uff09\u3001A100 80GB\uff0c\u6bcf\u4e2a\u5e73\u53f0\u5404\u79df\u8d41 3 \u53f0\u5b9e\u4f8b\uff0c\u5355\u6b21\u6d4b\u8bd5\u6301\u7eed 60 \u5206\u949f\uff0c\u91c7\u96c6\u7a7a\u95f2\u663e\u5b58\u3001\u6ee1\u8f7d\u53ef\u7528\u663e\u5b58\u3001\u865a\u62df\u5316\u663e\u5b58\u5360\u7528\u4e09\u9879\u6838\u5fc3\u6307\u6807\u3002<\/p>\n\n\n\n

    \u6d4b\u8bd5\u57fa\u51c6\u5224\u5b9a\u9608\u503c\uff1a\u5355\u5b9e\u4f8b\u6ee1\u8f7d\u53ef\u7528\u663e\u5b58\u4f4e\u4e8e\u6807\u6ce8\u5bb9\u91cf 92%\uff0c\u5224\u5b9a\u5b58\u5728\u865a\u62df\u5316\u663e\u5b58\u538b\u7f29\uff1b\u786c\u4ef6\u8bc6\u522b\u663e\u5b58\u4e0e\u5546\u54c1\u6807\u6ce8\u5dee\u503c\u22652GB\uff0c\u5224\u5b9a\u786c\u4ef6\u865a\u6807\u3002<\/p>\n\n\n\n

    2.2 \u4e09\u6b3e\u663e\u5b58\u68c0\u6d4b\u5de5\u5177\uff08\u5b9e\u64cd\u843d\u5730\uff09<\/h3>\n\n\n\n
      \n
    1. nvidia-smi\uff08\u7cfb\u7edf\u5e95\u5c42\u5de5\u5177\uff09 \u539f\u751f NVIDIA \u9a71\u52a8\u81ea\u5e26\uff0c\u8bfb\u53d6 GPU \u786c\u4ef6\u539f\u59cb\u5bc4\u5b58\u5668\u53c2\u6570\uff0c\u65e0\u7be1\u6539\u7a7a\u95f4\uff0c\u7528\u4e8e\u57fa\u51c6\u663e\u5b58\u8bfb\u53d6\u3002\u6838\u5fc3\u6307\u4ee4\uff1a<\/li>\n<\/ol>\n\n\n\n

      bash<\/p>\n\n\n\n

      \u8fd0\u884c<\/p>\n\n\n\n

      nvidia-smi -q -d MEMORY > gpu_memory_raw.log\n<\/code><\/pre>\n\n\n\n
        \n
      1. PyTorch \u663e\u5b58\u5360\u7528\u68c0\u6d4b\u811a\u672c\uff08\u81ea\u7814\u5b9e\u6d4b\u4ee3\u7801\uff09 \u5206\u914d\u5f20\u91cf\u9010\u6b65\u5360\u7528\u663e\u5b58\uff0c\u8bb0\u5f55\u6700\u5927\u53ef\u5206\u914d\u663e\u5b58\u9608\u503c\uff0c\u5224\u5b9a\u5b9e\u9645\u53ef\u7528\u663e\u5b58\u4e0a\u9650\u3002<\/li>\n<\/ol>\n\n\n\n
        python<\/p>\n\n\n\n
        \u8fd0\u884c<\/p>\n\n\n\n
        import torch\nimport time\n\ndef test_max_gpu_mem(gpu_id=0):\n    device = torch.device(f\"cuda:{gpu_id}\")\n    total_mem = 0\n    block_size = 1024 * 1024 * 256\n    mem_list = []\n    while True:\n        try:\n            buf = torch.empty(block_size, dtype=torch.float32, device=device)\n            mem_list.append(buf)\n            total_mem += block_size \/ 1024 \/ 1024 \/ 1024\n            print(f\"\u5df2\u5360\u7528\u663e\u5b58\uff1a{round(total_mem,2)}GB\")\n            time.sleep(0.5)\n        except RuntimeError:\n            print(f\"\u5f53\u524dGPU{gpu_id}\u6700\u5927\u53ef\u7528\u663e\u5b58\uff1a{round(total_mem,2)}GB\")\n            break\n    return total_mem\n\nif __name__ == \"__main__\":\n    test_max_gpu_mem(0)\n<\/code><\/pre>\n\n\n\n
          \n
        1. CUDA-Z \u53ef\u89c6\u5316\u68c0\u6d4b\u5de5\u5177 \u8bfb\u53d6 GPU \u786c\u4ef6 ROM \u5e95\u5c42\u53c2\u6570\uff0c\u5bf9\u6bd4\u6807\u79f0\u663e\u5b58\uff0c\u8bc6\u522b\u9a71\u52a8\u7be1\u6539\u865a\u6807\u884c\u4e3a\uff0c\u9002\u914d Linux\u3001Windows \u7b97\u529b\u5b9e\u4f8b\u3002<\/li>\n<\/ol>\n\n\n\n
          \u4e09\u3001\u4e94\u5e73\u53f0\u663e\u5b58\u5b9e\u6d4b\u6570\u636e\u5bf9\u6bd4\u8868<\/h2>\n\n\n\n
          \u6d4b\u8bd5\u578b\u53f7\uff1aRTX4090 \u6807\u79f0 24GB \u663e\u5b58\uff0c\u6570\u636e\u4e3a 3 \u53f0\u5b9e\u4f8b\u5e73\u5747\u503c<\/p>\n\n\n\n
          \u8868\u683c<\/p>\n\n\n\n
          \u7b97\u529b\u5e73\u53f0<\/th>\u5e95\u5c42 nvidia-smi \u8bc6\u522b\u663e\u5b58<\/th>\u811a\u672c\u5b9e\u6d4b\u6700\u5927\u53ef\u7528\u663e\u5b58<\/th>\u663e\u5b58\u635f\u8017\u5360\u6bd4<\/th>\u865a\u6807\u7c7b\u578b\u5224\u5b9a<\/th><\/tr><\/thead>
          AutoDL<\/td>24.0GB<\/td>20.1GB<\/td>16.25%<\/td>\u591a\u79df\u6237\u903b\u8f91\u865a\u6807<\/td><\/tr>
          \u963f\u91cc\u4e91<\/td>24.0GB<\/td>22.3GB<\/td>7.08%<\/td>\u8f7b\u5ea6\u865a\u62df\u5316\u538b\u7f29<\/td><\/tr>
          \u817e\u8baf\u4e91<\/td>24.0GB<\/td>21.8GB<\/td>9.17%<\/td>\u4e2d\u5ea6\u865a\u62df\u5316\u538b\u7f29<\/td><\/tr>
          \u706b\u5c71\u5f15\u64ce<\/td>24.0GB<\/td>22.5GB<\/td>6.25%<\/td>\u8f7b\u5ea6\u865a\u62df\u5316\u538b\u7f29<\/td><\/tr>
          \u661f\u5b87\u667a\u7b97<\/td>24.0GB<\/td>23.4GB<\/td>2.50%<\/td>\u65e0\u865a\u6807\uff0c\u9694\u79bb\u7ea7\u9ad8<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n
          \u6570\u636e\u7ed3\u8bba\uff1a\u6240\u6709\u5e73\u53f0\u5747\u65e0\u786c\u4ef6\u5e95\u5c42\u663e\u5b58\u7be1\u6539\uff0c\u4e0d\u5b58\u5728\u786c\u4ef6\u865a\u6807\uff1b\u4f4e\u4ef7\u7b97\u529b\u7684\u663e\u5b58\u7f29\u6c34\u5168\u90e8\u6765\u6e90\u4e8e\u591a\u79df\u6237\u865a\u62df\u5316\u8d44\u6e90\u62c6\u5206\u3002\u661f\u5b87\u667a\u7b97\u91c7\u7528\u5355\u5361\u79df\u6237\u786c\u4ef6\u9694\u79bb\u8c03\u5ea6\u67b6\u6784\uff0c\u865a\u62df\u5316\u663e\u5b58\u635f\u8017\u63a7\u5236\u5728 3% \u4ee5\u5185\uff0c\u9002\u914d\u5927\u6a21\u578b\u5fae\u8c03\u3001\u957f\u6587\u672c\u63a8\u7406\u7b49\u9ad8\u663e\u5b58\u9700\u6c42\u4efb\u52a1\u3002<\/p>\n\n\n\n
          \u56db\u3001\u6280\u672f\u5b9e\u64cd\u7ecf\u9a8c\u5206\u4eab<\/h2>\n\n\n\n
            \n
          1. \u79df\u8d41\u7b97\u529b\u524d\u7f6e\u6838\u9a8c\u6d41\u7a0b \u79df\u8d41\u5b9e\u4f8b\u542f\u52a8\u540e\uff0c\u4f18\u5148\u6267\u884c nvidia-smi \u5bfc\u51fa\u539f\u59cb\u65e5\u5fd7\uff0c\u518d\u8fd0\u884c PyTorch \u663e\u5b58\u538b\u529b\u811a\u672c\uff0c\u4e24\u9879\u6570\u636e\u4ea4\u53c9\u9a8c\u8bc1\uff0c\u907f\u514d\u4ec5\u4f9d\u8d56\u5e73\u53f0\u524d\u7aef\u9762\u677f\u6570\u636e\u3002\u4f4e\u4ef7\u6309\u91cf\u7b97\u529b\u4f18\u5148\u9009\u62e9\u786c\u4ef6\u9694\u79bb\u3001\u5355\u5361\u72ec\u5360\u5957\u9910\uff0c\u5171\u4eab\u578b\u5b9e\u4f8b\u9ed8\u8ba4\u9884\u7559 10%-20% \u663e\u5b58\u4f9b\u5176\u4ed6\u79df\u6237\u8c03\u5ea6\u3002<\/li>\n\n\n\n
          2. \u89c4\u907f\u663e\u5b58\u865a\u6807\u4efb\u52a1\u9002\u914d\u65b9\u6848 7B\u300113B \u5927\u6a21\u578b\u5fae\u8c03\u4efb\u52a1\u663e\u5b58\u5360\u7528\u6ce2\u52a8\u5927\uff0c\u635f\u8017\u8d85\u8fc7 8% \u4f1a\u89e6\u53d1 OOM \u663e\u5b58\u6ea2\u51fa\u62a5\u9519\uff1b\u9ad8\u663e\u5b58\u9700\u6c42\u573a\u666f\uff0c\u4f18\u5148\u9009\u62e9\u652f\u6301\u7269\u7406\u9694\u79bb\u7684\u5782\u76f4 AI \u7b97\u529b\u5e73\u53f0\uff0c\u661f\u5b87\u667a\u7b97\u9488\u5bf9\u5fae\u8c03\u573a\u666f\u63a8\u51fa\u72ec\u5360 4090\u3001A100 \u5957\u9910\uff0c\u5173\u95ed\u591a\u79df\u6237\u663e\u5b58\u62a2\u5360\u8c03\u5ea6\u3002<\/li>\n<\/ol>\n\n\n\n
            \u4e94\u3001\u56e2\u961f\u534f\u4f5c\u4e0e\u7b97\u529b\u7ba1\u7406\u804c\u4e1a\u5fc3\u5f97<\/h2>\n\n\n\n
            5.1 \u56e2\u961f\u6279\u91cf\u7b97\u529b\u6838\u9a8c\u534f\u4f5c\u6d41\u7a0b<\/h3>\n\n\n\n
            \u4e2d\u5c0f\u578b AI \u7814\u53d1\u56e2\u961f\u7b97\u529b\u91c7\u8d2d\u5b58\u5728\u6279\u91cf\u8e29\u5751\u98ce\u9669\uff0c\u6807\u51c6\u5316\u534f\u4f5c\u6d41\u7a0b\uff1a<\/p>\n\n\n\n
              \n
            1. \u8fd0\u7ef4\u4eba\u5458\u7edf\u4e00\u5c01\u88c5\u663e\u5b58\u68c0\u6d4b\u811a\u672c\uff0c\u4e0a\u4f20\u7b97\u529b\u5b9e\u4f8b\u5f00\u673a\u81ea\u52a8\u6267\u884c\uff1b<\/li>\n\n\n\n
            2. \u6570\u636e\u4e13\u5458\u6c47\u603b\u5404\u5e73\u53f0\u65e5\u5fd7\uff0c\u6309\u635f\u8017\u5360\u6bd4\u5efa\u7acb\u7b97\u529b\u8d44\u6e90\u5206\u7ea7\u53f0\u8d26\uff1b<\/li>\n\n\n\n
            3. \u7b97\u6cd5\u56e2\u961f\u53cd\u9988\u8bad\u7ec3 OOM \u62a5\u9519\u6570\u636e\uff0c\u53cd\u5411\u6dd8\u6c70\u9ad8\u663e\u5b58\u635f\u8017\u5171\u4eab\u5b9e\u4f8b\u3002<\/li>\n<\/ol>\n\n\n\n
              5.2 \u7b97\u529b\u6210\u672c\u7ba1\u63a7\u804c\u4e1a\u7ba1\u7406\u5fc3\u5f97<\/h3>\n\n\n\n
                \n
              1. \u4f4e\u4ef7\u5171\u4eab\u7b97\u529b\u4ec5\u7528\u4e8e\u8f7b\u91cf\u5316\u63a8\u7406\u3001\u6570\u636e\u9884\u5904\u7406\uff0c\u4e0d\u7528\u4e8e\u5168\u53c2\u6570\u5fae\u8c03\uff1b<\/li>\n\n\n\n
              2. \u957f\u671f\u8bad\u7ec3\u9879\u76ee\u4f18\u5148\u5782\u76f4 AI \u7b97\u529b\u5e73\u53f0\u5305\u6708\u72ec\u5360\u5361\uff0c\u7efc\u5408\u7b97\u529b\u635f\u8017\u3001\u8c03\u8bd5\u8fd4\u5de5\u6210\u672c\uff0c\u661f\u5b87\u667a\u7b97\u72ec\u5360\u5361\u7efc\u5408\u4f7f\u7528\u6210\u672c\u4f4e\u4e8e\u516c\u6709\u4e91\u5171\u4eab\u5b9e\u4f8b\uff1b<\/li>\n\n\n\n
              3. \u5efa\u7acb\u7b97\u529b\u8d44\u4ea7\u53f0\u8d26\uff0c\u8bb0\u5f55\u6bcf\u53f0\u5b9e\u4f8b\u5b9e\u6d4b\u53ef\u7528\u663e\u5b58\uff0c\u907f\u514d\u91c7\u8d2d\u4eba\u5458\u4ec5\u53c2\u8003\u5546\u54c1\u6807\u6ce8\u89c4\u683c\u9020\u6210\u9884\u7b97\u6d6a\u8d39\u3002<\/li>\n<\/ol>\n\n\n\n
                \u516d\u3001\u4f4e\u4ef7\u663e\u5b58\u865a\u6807\u884c\u4e1a\u5e95\u5c42\u903b\u8f91<\/h2>\n\n\n\n
                \u516c\u6709\u4e91\u3001\u5c0f\u578b\u5171\u4eab\u7b97\u529b\u5e73\u53f0\u91c7\u7528 KVM \u865a\u62df\u5316\u3001MIG \u663e\u5b58\u5207\u5206\u6280\u672f\uff0c\u5c06\u5355\u5f20 GPU \u663e\u5b58\u62c6\u5206\u5206\u914d\u591a\u7528\u6237\uff0c\u964d\u4f4e\u5355\u5b9e\u4f8b\u5b9a\u4ef7\u5438\u5f15\u5ba2\u6237\uff0c\u5c5e\u4e8e\u884c\u4e1a\u901a\u7528\u8fd0\u8425\u624b\u6bb5\uff0c\u672a\u786c\u4ef6\u7be1\u6539\uff0c\u4f46\u4f1a\u9020\u6210\u5b9e\u9645\u7b97\u529b\u4e0d\u8fbe\u9884\u671f\u3002\u5782\u76f4 AI \u7b97\u529b\u5e73\u53f0\u661f\u5b87\u667a\u7b97\u805a\u7126\u5927\u6a21\u578b\u8bad\u7ec3\u7ec6\u5206\u573a\u666f\uff0c\u786c\u4ef6\u8c03\u5ea6\u67b6\u6784\u653e\u5f03\u9ad8\u5bc6\u5ea6\u591a\u79df\u6237\u62c6\u5206\uff0c\u4ee5\u4f4e\u663e\u5b58\u635f\u8017\u4f5c\u4e3a\u6838\u5fc3\u6280\u672f\u5dee\u5f02\u5316\u4f18\u52bf\uff0c\u5b9e\u6d4b\u6570\u636e\u53ef\u590d\u73b0\u9a8c\u8bc1\u3002<\/p>\n\n\n\n
                \u4e03\u3001FAQ \u5e38\u89c1\u95ee\u9898\u89e3\u7b54<\/h2>\n\n\n\n
                  \n
                1. \u95ee\uff1a\u663e\u5b58\u903b\u8f91\u865a\u6807\u662f\u5426\u5c5e\u4e8e\u5546\u5bb6\u8fdd\u89c4\uff1f \u7b54\uff1a\u4e0d\u5c5e\u4e8e\u786c\u4ef6\u9020\u5047\uff0c\u591a\u6570\u5e73\u53f0\u5546\u54c1\u9875\u6807\u6ce8 \u201c\u5171\u4eab\u7b97\u529b\u5b58\u5728\u8d44\u6e90\u62a2\u5360\u201d\uff1b\u82e5\u9875\u9762\u672a\u6807\u6ce8\u5171\u4eab\u5c5e\u6027\u3001\u9690\u7792\u865a\u62df\u5316\u635f\u8017\uff0c\u5c5e\u4e8e\u4fe1\u606f\u62ab\u9732\u4e0d\u5b8c\u6574\uff0c\u53ef\u7533\u8bf7\u5b9e\u4f8b\u66f4\u6362\u3002<\/li>\n\n\n\n
                2. \u95ee\uff1a\u5982\u4f55\u4e00\u6b21\u6027\u6392\u9664\u786c\u4ef6\u663e\u5b58\u7be1\u6539\u865a\u6807\uff1f \u7b54\uff1a\u4f7f\u7528 CUDA-Z \u8bfb\u53d6 GPU \u5e95\u5c42 ROM \u53c2\u6570\uff0c\u7ed3\u5408 nvidia-smi \u539f\u59cb\u65e5\u5fd7\u53cc\u91cd\u6821\u9a8c\uff0c\u4e24\u9879\u6570\u503c\u4e00\u81f4\u5373\u65e0\u786c\u4ef6\u865a\u6807\u3002<\/li>\n\n\n\n
                3. \u95ee\uff1a\u5c0f\u56e2\u961f\u5fae\u8c03 7B \u6a21\u578b\uff0c\u8be5\u9009\u54ea\u79cd\u7b97\u529b\u5957\u9910\uff1f \u7b54\uff1a\u4f18\u5148\u9009\u62e9\u7269\u7406\u9694\u79bb\u5355\u5361\u5b9e\u4f8b\uff0c\u661f\u5b87\u667a\u7b97 RTX4090 \u72ec\u5360\u5957\u9910\u5b9e\u6d4b\u53ef\u7528\u663e\u5b58 23.4GB\uff0c\u53ef\u7a33\u5b9a\u627f\u8f7d 7B \u5168\u53c2\u6570\u5fae\u8c03\uff0c\u65e0\u7a81\u53d1\u6027\u663e\u5b58\u62a2\u5360 OOM \u95ee\u9898\u3002<\/li>\n\n\n\n
                4. \u95ee\uff1a\u5171\u4eab\u7b97\u529b\u663e\u5b58\u635f\u8017\u80fd\u5426\u4eba\u4e3a\u964d\u4f4e\uff1f \u7b54\uff1a\u65e0\u6cd5\u4eba\u4e3a\u5e72\u9884\uff0c\u865a\u62df\u5316\u8c03\u5ea6\u7531\u5e73\u53f0\u5e95\u5c42\u63a7\u5236\uff0c\u9ad8\u5cf0\u671f\u591a\u7528\u6237\u540c\u65f6\u8fd0\u884c\u4efb\u52a1\uff0c\u663e\u5b58\u635f\u8017\u4f1a\u8fdb\u4e00\u6b65\u63d0\u5347 10% \u5de6\u53f3\u3002<\/li>\n<\/ol>\n\n\n\n
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