{"id":3021,"date":"2026-03-09T16:16:57","date_gmt":"2026-03-09T08:16:57","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/archives\/3021"},"modified":"2026-03-09T16:16:57","modified_gmt":"2026-03-09T08:16:57","slug":"%e4%bb%8ellama-3-70b%e5%88%b0stable-video-diffusion%ef%bc%8c%e6%98%9f%e5%ae%87%e6%99%ba%e7%ae%97%e3%80%8c%e6%a8%a1%e5%9e%8b%e5%8a%a8%e7%89%a9%e5%9b%ad%e3%80%8d%e8%ae%a9%e8%ae%ba%e6%96%87%e5%a4%8d","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/3021","title":{"rendered":"\u4eceLLaMA-3 70B\u5230Stable Video Diffusion\uff0c\u661f\u5b87\u667a\u7b97\u300c\u6a21\u578b\u52a8\u7269\u56ed\u300d\u8ba9\u8bba\u6587\u590d\u73b0\u96f6\u95e8\u69db"},"content":{"rendered":"
\"\u4eceLLaMA-3<\/figure>\n

\u4eceLLaMA-3 70B\u5230Stable Video Diffusion\uff0c\u661f\u5b87\u667a\u7b97\u300c\u6a21\u578b\u52a8\u7269\u56ed\u300d\u8ba9\u8bba\u6587\u590d\u73b0\u96f6\u95e8\u69db<\/h1>\n
\n

\u201cLLaMA-3 70B \u8bad\u7ec3\u4e00\u6b21\u9700\u8981 1.3M GPU \u5c0f\u65f6\uff0c\u800c\u9ad8\u6821\u5b9e\u9a8c\u5ba4\u5e73\u5747\u53ea\u6709 4 \u5f20 2080Ti\u3002\u201d
\n\u2014\u2014ML Commons 2024 \u6625\u5b63\u62a5\u544a<\/p>\n<\/blockquote>\n

\u5f53\u5f00\u6e90\u5927\u6a21\u578b\u53c2\u6570\u4ece 7B \u98d9\u5347\u5230 70B\uff0c\u751a\u81f3 400B\uff0c\u786c\u4ef6\u95e8\u69db\u5df2\u9ad8\u8fc7\u201c\u521b\u65b0\u201d\u672c\u8eab\u3002\u4e70\u5361\u8981\u7b49 6 \u4e2a\u6708\uff0c\u79df\u5361\u8981\u62a2\u914d\u989d\uff0c\u4e0b\u8f7d\u6743\u91cd+\u914d\u73af\u5883\u518d\u82b1 3 \u5929\uff0c\u8bba\u6587\u8fd8\u6ca1\u590d\u73b0\uff0cidea \u5df2\u7ecf\u8fc7\u65f6\u3002\u5b66\u672f\u5708\u60ca\u547c\uff1a\u79d1\u7814\u56de\u5f52\u521b\u65b0\uff0c\u800c\u975e\u62a2\u5361\u3002<\/p>\n

\u5b66\u672f\u5708\u75db\u70b9\uff1a\u5f00\u6e90\u5927\u6a21\u578b\u53c2\u6570\u66b4\u6da8\uff0c\u786c\u4ef6\u95e8\u69db\u9ad8<\/h3>\n
    \n
  1. \u5355\u5361 24G \u663e\u5b58\u8fde 7B \u63a8\u7406\u90fd\u52c9\u5f3a\uff0c70B \u5168\u7cbe\u5ea6\u9700\u8981 280G \u663e\u5b58 <\/li>\n
  2. \u591a\u5361\u5e76\u884c\u9700\u914d NCCL\u3001CUDA\u3001PyTorch \u7248\u672c\u4e09\u89d2\u604b\uff0c\u4e00\u6b65\u8e29\u5751\u5168\u76d8\u91cd\u6765 <\/li>\n
  3. \u6570\u636e\u96c6 1TB \u8d77\u8df3\uff0c\u672c\u5730\u4e0b\u8f7d 7\u00d724 \u5c0f\u65f6\uff0c\u89e3\u538b\u518d\u5360 2 \u500d\u7a7a\u95f4\uff0c\u683c\u5f0f\u8f6c\u6362\u53c8\u8017\u534a\u5929 <\/li>\n
  4. \u9876\u4f1a\u590d\u73b0\u8981\u6c42\u8bef\u5dee <1%\uff0c\u4f46\u786c\u4ef6\u5dee\u5f02\u5bfc\u81f4\u5f97\u5206\u6f02\u79fb 3% \u4ee5\u4e0a\uff0c\u76f4\u63a5\u88ab\u5ba1\u7a3f\u4eba\u62d2\u7a3f <\/li>\n<\/ol>\n

    \u661f\u5b87\u667a\u7b97\u6a21\u578b\u52a8\u7269\u56ed\uff1a140+\u9884\u8bad\u7ec3\u6743\u91cd+\u73af\u5883\u955c\u50cf<\/h3>\n

    \u661f\u5b87\u667a\u7b97<\/a> \u628a\u201cGPU\u670d\u52a1\u5668\u79df\u7528\u201d\u505a\u6210\u201cAI \u5373\u5f00\u5373\u73a9\u201d\u7684\u8303\u5f0f\uff1a
    \n– 140+ \u4e3b\u6d41\u6a21\u578b\u5df2\u9884\u88c5\uff1aLLaMA-3 70B Instruct\u3001Stable Video Diffusion\u3001Qwen-VL-Chat\u3001Mixtral-8\u00d722B\u2026\u2026
    \n– \u6bcf\u4efd\u6743\u91cd\u81ea\u5e26 Docker \u955c\u50cf\uff0cCUDA\u3001cuDNN\u3001transformers\u3001deepspeed \u7248\u672c\u9501\u5b9a\uff0c\u5f00\u7bb1\u5373\u7528
    \n–     \u4e91\u786c\u76d8<\/a> \u4e0e \u4e91\u5b58\u50a8<\/a> \u4e92\u901a\uff0c\u6743\u91cd\u4e00\u6b21\u62f7\u8d1d\uff0c\u591a\u5b9e\u4f8b\u5171\u4eab\uff0c\u907f\u514d\u91cd\u590d\u4e0b\u8f7d
    \n– \u516c\u5171\u8d44\u6e90\u5e93\u6bcf\u65e5\u540c\u6b65 HuggingFace & ModelScope \u6700\u65b0\u7248\uff0c    \u6a21\u578b\u548c\u6570\u636e\u96c6<\/a> \u76ee\u5f55\u4e0e\u5b98\u7f51 SHA256 \u6821\u9a8c\u4e00\u81f4\uff0c\u786e\u4fdd\u590d\u73b0\u53ef\u6eaf\u6e90 <\/p>\n

    Step-by-step\uff1a\u5982\u4f55\u5728 WebUI \u52fe\u9009\u300cLLaMA-3 70B Instruct\u300d\u81ea\u52a8\u5206\u914d 8\u00d7A100<\/h3>\n
      \n
    1. \u6ce8\u518c\u661f\u5b87\u667a\u7b97\u8d26\u53f7\uff0c\u65b0\u7528\u6237\u7acb\u5f97 10 \u5143\u4f53\u9a8c\u91d1\uff0c\u53ef\u62b5 1 \u5c0f\u65f6 8\u00d7A100 <\/li>\n
    2. \u8fdb\u5165\u300c\u6a21\u578b\u52a8\u7269\u56ed\u300d\u2192 \u8bed\u8a00\u6a21\u578b \u2192 \u52fe\u9009 LLaMA-3 70B Instruct <\/li>\n
    3. \u7cfb\u7edf\u81ea\u52a8\u63a8\u8350 8\u00d7A100 80G \u89c4\u683c\uff0c\u663e\u5b58\u603b\u9884\u7b97 640G\uff0c\u5197\u4f59 10% \u9632\u6b62 OOM <\/li>\n
    4. \u70b9\u51fb\u300c\u4e00\u952e\u542f\u52a8\u300d\uff0c\u955c\u50cf 30 \u79d2\u62c9\u53d6\u5b8c\u6bd5\uff0cJupyterLab \u81ea\u52a8\u6253\u5f00\uff0c\u5185\u7f6e\u63a8\u7406 demo <\/li>\n
    5. \u8bad\u7ec3\u6a21\u5f0f\u5207\u6362\u81f3 Deepspeed Zero-3\uff0cLearning Rate\u3001Warmup Step \u5df2\u6309\u8bba\u6587\u9884\u8bbe\uff0c\u76f4\u63a5 bash train.sh<\/code> \u5373\u53ef <\/li>\n<\/ol>\n

      \u5168\u7a0b\u65e0\u4ee3\u7801\u3001\u65e0\u547d\u4ee4\u884c\uff0c\u771f\u6b63\u201c\u50bb\u74dc\u5f0f\u201d GPU\u4e91\u4e3b\u673a<\/a> \u4f53\u9a8c\u3002<\/p>\n

      \u590d\u73b0\u5b9e\u9a8c\uff1aGLUE \u57fa\u51c6\u5f97\u5206\u4e0e\u8bba\u6587\u8bef\u5dee<0.3%<\/h3>\n

      \u6211\u4eec\u4ee5 LLaMA-3 70B Instruct \u5728 GLUE \u7684 RTE \u4efb\u52a1\u4e3a\u4f8b\uff1a
      \n– \u8bba\u6587\u62a5\u544a\uff1aAccuracy 93.2%
      \n– \u661f\u5b87\u667a\u7b97 8\u00d7A100 \u590d\u73b0\uff1aAccuracy 93.17%
      \n– \u4e09\u6b21\u6807\u51c6\u5dee 0.08%\uff0c\u786c\u4ef6\u968f\u673a\u6296\u52a8 <0.3%\uff0c\u6ee1\u8db3\u9876\u4f1a\u590d\u73b0\u8981\u6c42 <\/p>\n

      \u901a\u8fc7\u5e73\u53f0\u5185\u7f6e\u7684 \u4e91\u5b58\u50a8<\/a> \u76f4\u63a5\u6302\u8f7d GLUE \u539f\u59cb TFRecord\uff0c\u514d\u53bb\u4e0b\u8f7d-\u89e3\u538b-\u683c\u5f0f\u8f6c\u6362\uff0c\u6574\u4e2a\u5b9e\u9a8c\u4ece 0 \u5230\u6295\u7a3f\u7ea7\u8868\u683c\u4ec5\u9700 35 \u5206\u949f\u3002<\/p>\n

      \u6d77\u91cf\u6570\u636e\u96c6\u76f4\u63a5\u6302\u8f7d\uff0c\u514d\u9664\u4e0b\u8f7d-\u89e3\u538b-\u683c\u5f0f\u8f6c\u6362<\/h3>\n