{"id":21088,"date":"2025-04-19T04:12:02","date_gmt":"2025-04-18T20:12:02","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/21088.html"},"modified":"2025-04-19T04:12:02","modified_gmt":"2025-04-18T20:12:02","slug":"%e6%b7%b1%e5%ba%a6%e8%a7%a3%e6%9e%90%ef%bc%9a%e5%a4%a7%e6%a8%a1%e5%9e%8b%e5%9c%a8%e5%a4%9a%e6%98%be%e5%8d%a1%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8b%e7%9a%84%e9%80%9a%e4%bf%a1%e6%9c%ba%e5%88%b6%e4%b8%8e","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/21088.html","title":{"rendered":"\u6df1\u5ea6\u89e3\u6790\uff1a\u5927\u6a21\u578b\u5728\u591a\u663e\u5361\u670d\u52a1\u5668\u4e0b\u7684\u901a\u4fe1\u673a\u5236\u4e0e\u5206\u5e03\u5f0f\u8bad\u7ec3\u2014\u2014\u4ee5DeepSeek\u3001Ollama\u548cvLLM\u4e3a\u4f8b"},"content":{"rendered":"<h3>\u4e00\u3001\u5f15\u8a00&#xff1a;\u5927\u6a21\u578b\u4e0e\u591a\u663e\u5361\u7684\u5fc5\u7136\u7ed3\u5408<\/h3>\n<p>\u968f\u7740\u5927\u6a21\u578b\u53c2\u6570\u89c4\u6a21\u7a81\u7834\u5343\u4ebf\u7ea7&#xff08;\u5982GPT-4\u3001DeepSeek&#xff09;&#xff0c;\u5355\u663e\u5361\u7684\u663e\u5b58\u5bb9\u91cf\u4e0e\u7b97\u529b\u5df2\u65e0\u6cd5\u6ee1\u8db3\u9700\u6c42\u3002\u591a\u663e\u5361\u5e76\u884c\u8ba1\u7b97\u6210\u4e3a\u8bad\u7ec3\u4e0e\u63a8\u7406\u7684\u6838\u5fc3\u6280\u672f&#xff0c;\u5176\u6838\u5fc3\u6311\u6218\u5728\u4e8e\u9ad8\u6548\u901a\u4fe1\u4e0e\u8d1f\u8f7d\u5747\u8861\u3002\u672c\u6587\u4ee5\u56fd\u4ea7\u5927\u6a21\u578bDeepSeek\u4e3a\u4f8b&#xff0c;\u7ed3\u5408Ollama\u4e0evLLM\u63a8\u7406\u5f15\u64ce&#xff0c;\u6df1\u5ea6\u5256\u6790\u591a\u663e\u5361\u534f\u540c\u5de5\u4f5c\u7684\u6280\u672f\u5b9e\u73b0&#xff0c;\u5e76\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\u3001\u6027\u80fd\u6570\u636e\u4e0e\u67b6\u6784\u56fe\u5c55\u793a\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u3002<\/p>\n<hr \/>\n<h3>\u4e8c\u3001\u591a\u663e\u5361\u901a\u4fe1\u673a\u5236&#xff1a;\u4ece\u6570\u636e\u5e76\u884c\u5230\u6df7\u5408\u5e76\u884c<\/h3>\n<h4>1. \u6570\u636e\u5e76\u884c&#xff08;Data Parallelism&#xff09;<\/h4>\n<ul>\n<li>\n<p>\u6838\u5fc3\u601d\u60f3&#xff1a;\u5c06\u8bad\u7ec3\u6570\u636e\u5212\u5206\u4e3a\u591a\u4e2a\u6279\u6b21&#xff0c;\u6bcf\u4e2a\u663e\u5361\u6301\u6709\u5b8c\u6574\u7684\u6a21\u578b\u526f\u672c&#xff0c;\u72ec\u7acb\u8ba1\u7b97\u68af\u5ea6\u540e\u540c\u6b65\u66f4\u65b0\u3002<\/p>\n<\/li>\n<li>\n<p>\u901a\u4fe1\u6a21\u5f0f&#xff1a;<\/p>\n<p>    #mermaid-svg-7UdFfLXkbakEPXUt {font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-7UdFfLXkbakEPXUt .error-icon{fill:#552222;}#mermaid-svg-7UdFfLXkbakEPXUt .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-7UdFfLXkbakEPXUt .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-7UdFfLXkbakEPXUt .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-7UdFfLXkbakEPXUt 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path{fill:#ECECFF;stroke:#9370DB;stroke-width:1px;}#mermaid-svg-7UdFfLXkbakEPXUt .node .label{text-align:center;}#mermaid-svg-7UdFfLXkbakEPXUt .node.clickable{cursor:pointer;}#mermaid-svg-7UdFfLXkbakEPXUt .arrowheadPath{fill:#333333;}#mermaid-svg-7UdFfLXkbakEPXUt .edgePath .path{stroke:#333333;stroke-width:2.0px;}#mermaid-svg-7UdFfLXkbakEPXUt .flowchart-link{stroke:#333333;fill:none;}#mermaid-svg-7UdFfLXkbakEPXUt .edgeLabel{background-color:#e8e8e8;text-align:center;}#mermaid-svg-7UdFfLXkbakEPXUt .edgeLabel rect{opacity:0.5;background-color:#e8e8e8;fill:#e8e8e8;}#mermaid-svg-7UdFfLXkbakEPXUt .cluster rect{fill:#ffffde;stroke:#aaaa33;stroke-width:1px;}#mermaid-svg-7UdFfLXkbakEPXUt .cluster text{fill:#333;}#mermaid-svg-7UdFfLXkbakEPXUt .cluster span{color:#333;}#mermaid-svg-7UdFfLXkbakEPXUt div.mermaidTooltip{position:absolute;text-align:center;max-width:200px;padding:2px;font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:12px;background:hsl(80, 100%, 96.2745098039%);border:1px solid #aaaa33;border-radius:2px;pointer-events:none;z-index:100;}#mermaid-svg-7UdFfLXkbakEPXUt :root{&#8211;mermaid-font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;} <\/p>\n<p>           <span id=\"L-L-A-B\" class=\"edgeLabel L-LS-A&#039; L-LE-B\">Scatter-Reduce<\/span> <\/p>\n<p>           <span id=\"L-L-B-C\" class=\"edgeLabel L-LS-B&#039; L-LE-C\">Scatter-Reduce<\/span> <\/p>\n<p>           <span id=\"L-L-C-D\" class=\"edgeLabel L-LS-C&#039; L-LE-D\">Scatter-Reduce<\/span> <\/p>\n<p>           <span id=\"L-L-D-C\" class=\"edgeLabel L-LS-D&#039; L-LE-C\">AllGather<\/span> <\/p>\n<p>           <span id=\"L-L-C-B\" class=\"edgeLabel L-LS-C&#039; L-LE-B\">AllGather<\/span> <\/p>\n<p>           <span id=\"L-L-B-A\" class=\"edgeLabel L-LS-B&#039; L-LE-A\">AllGather<\/span> <\/p>\n<p>             \u53610\u68af\u5ea6 <\/p>\n<p>             \u53611 <\/p>\n<p>             \u53612 <\/p>\n<p>             \u53613 <\/p>\n<ul>\n<li>Ring AllReduce&#xff1a;\u901a\u8fc7\u73af\u5f62\u62d3\u6251\u5206\u4e24\u6b65\u805a\u5408\u68af\u5ea6&#xff08;Scatter-Reduce &#043; AllGather&#xff09;&#xff0c;\u5e26\u5bbd\u5229\u7528\u7387\u8fbe\u7406\u8bba\u5cf0\u503c\u7684<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\">\n<p>            2 <\/p>\n<p>            ( <\/p>\n<p>            N <\/p>\n<p>            \u2212 <\/p>\n<p>            1 <\/p>\n<p>            ) <\/p>\n<p>            \/ <\/p>\n<p>            N <\/p>\n<p>           2(N-1)\/N <\/p>\n<p>       <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\">2<\/span><span class=\"mopen\">(<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.109em\">N<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><span class=\"mbin\">\u2212<\/span><span class=\"mspace\" style=\"margin-right: 0.2222em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 1em;vertical-align: -0.25em\"><\/span><span class=\"mord\">1<\/span><span class=\"mclose\">)<\/span><span class=\"mord\">\/<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.109em\">N<\/span><\/span><\/span><\/span><\/span>\u500d&#xff08;N\u4e3a\u663e\u5361\u6570&#xff09;\u3002<\/li>\n<\/ul>\n<h5>\u672f\u8bed\u89e3\u91ca<\/h5>\n<p>\u68af\u5ea6&#xff08;Gradient&#xff09;<\/p>\n<ul>\n<li>\u5728\u673a\u5668\u5b66\u4e60\u4e2d&#xff0c;\u68af\u5ea6\u662f\u8861\u91cf\u635f\u5931\u51fd\u6570\u53d8\u5316\u7387\u7684\u91cf&#xff0c;\u5b83\u6307\u5bfc\u7740\u6a21\u578b\u53c2\u6570\u7684\u66f4\u65b0\u65b9\u5411&#xff0c;\u4ece\u800c\u5e2e\u52a9\u6a21\u578b\u627e\u5230\u6700\u5c0f\u5316\u635f\u5931\u7684\u6700\u4f73\u53c2\u6570\u914d\u7f6e\u3002<\/li>\n<\/ul>\n<p>AllReduce\u64cd\u4f5c<\/p>\n<ul>\n<li>\u5f53\u591a\u53f0\u8ba1\u7b97\u673a\u5171\u540c\u8bad\u7ec3\u4e00\u4e2a\u6a21\u578b\u65f6&#xff0c;AllReduce\u7528\u4e8e\u5c06\u6240\u6709\u8ba1\u7b97\u673a\u4e0a\u7684\u5c40\u90e8\u68af\u5ea6\u5408\u5e76\u4e3a\u4e00\u4e2a\u5168\u5c40\u68af\u5ea6&#xff0c;\u5e76\u786e\u4fdd\u6bcf\u4e2a\u8282\u70b9\u90fd\u62e5\u6709\u8fd9\u4e2a\u5168\u5c40\u68af\u5ea6\u3002<\/li>\n<li>\u4f8b\u5982&#xff0c;\u5982\u679c\u67094\u53f0\u8ba1\u7b97\u673a&#xff0c;\u6bcf\u53f0\u8ba1\u7b97\u673a\u90fd\u6709\u4e00\u4e2a\u6570\u503c&#xff0c;AllReduce\u4f1a\u5c06\u8fd9\u4e9b\u6570\u503c\u76f8\u52a0&#xff0c;\u7136\u540e\u5c06\u603b\u548c\u53d1\u9001\u56de\u6bcf\u53f0\u8ba1\u7b97\u673a\u3002<\/li>\n<\/ul>\n<h5>\u73af\u5f62\u62d3\u6251\u4e0eRing AllReduce\u8fc7\u7a0b<\/h5>\n<p>\u73af\u5f62\u62d3\u6251\u7b80\u4ecb<\/p>\n<ul>\n<li>\u8fd9\u662f\u4e00\u79cd\u7f51\u7edc\u7ed3\u6784&#xff0c;\u5176\u4e2d\u6bcf\u53f0\u8ba1\u7b97\u673a\u4ec5\u4e0e\u5176\u76f8\u90bb\u7684\u4e24\u53f0\u8ba1\u7b97\u673a\u76f8\u8fde&#xff0c;\u5f62\u6210\u4e00\u4e2a\u95ed\u5408\u7684\u73af\u8def\u3002<\/li>\n<\/ul>\n<p>Ring AllReduce\u6d41\u7a0b<\/p>\n<li>Scatter-Reduce\u9636\u6bb5&#xff1a;\u4ece\u67d0\u4e2a\u8d77\u59cb\u8282\u70b9\u5f00\u59cb&#xff0c;\u68af\u5ea6\u4fe1\u606f\u9010\u4e2a\u4f20\u9012\u5e76\u7d2f\u52a0\u3002<\/li>\n<li>AllGather\u9636\u6bb5&#xff1a;\u6700\u7ec8\u5f97\u5230\u7684\u5168\u5c40\u68af\u5ea6\u88ab\u5206\u53d1\u56de\u6240\u6709\u8282\u70b9\u3002<\/li>\n<h5>\u63d0\u9ad8\u5e26\u5bbd\u5229\u7528\u7387<\/h5>\n<p>\u901a\u8fc7\u4e0a\u8ff0\u8fc7\u7a0b&#xff0c;Ring AllReduce\u4e0d\u4ec5\u5b9e\u73b0\u4e86\u9ad8\u6548\u7684\u68af\u5ea6\u540c\u6b65&#xff0c;\u8fd8\u663e\u8457\u63d0\u5347\u4e86\u5e26\u5bbd\u5229\u7528\u7387\u3002\u5177\u4f53\u6765\u8bf4&#xff0c;\u5229\u7528\u516c\u5f0f ( \\\\frac{2(N &#8211; 1)}{N} ) &#xff08;( N ) \u8868\u793a\u8282\u70b9\u6570\u91cf&#xff09;&#xff0c;\u6211\u4eec\u53ef\u4ee5\u8ba1\u7b97\u51fa\u76f8\u5bf9\u4e8e\u7406\u8bba\u6700\u5927\u503c\u7684\u5b9e\u9645\u6548\u7387\u63d0\u5347\u6bd4\u4f8b\u3002<\/p>\n<h5>\u4e3e\u4e2a\u6817\u5b50<\/h5>\n<p>\u5047\u8bbe\u6211\u4eec\u67094\u53f0\u8ba1\u7b97\u673a\u53c2\u4e0e\u8bad\u7ec3&#xff0c;\u6839\u636e\u4e0a\u8ff0\u516c\u5f0f&#xff0c;\u5e26\u5bbd\u5229\u7528\u7387\u80fd\u8fbe\u52301.5\u500d\u7684\u7406\u60f3\u72b6\u6001\u3002\u8fd9\u610f\u5473\u7740\u76f8\u8f83\u4e8e\u4f20\u7edf\u65b9\u6cd5&#xff0c;Ring AllReduce\u80fd\u5728\u76f8\u540c\u65f6\u95f4\u5185\u4f20\u8f93\u66f4\u591a\u7684\u6570\u636e\u91cf&#xff0c;\u6781\u5927\u5730\u52a0\u5feb\u4e86\u8bad\u7ec3\u901f\u5ea6\u3002<\/p>\n<\/li>\n<li>\n<p>DeepSeek-7B\u793a\u4f8b&#xff1a;<\/p>\n<p> <span class=\"token comment\"># PyTorch DistributedDataParallel&#xff08;DDP&#xff09;\u5b8c\u6574\u914d\u7f6e<\/span><br \/>\n<span class=\"token keyword\">import<\/span> torch<br \/>\n<span class=\"token keyword\">import<\/span> torch<span class=\"token punctuation\">.<\/span>distributed <span class=\"token keyword\">as<\/span> dist<br \/>\n<span class=\"token keyword\">from<\/span> torch<span class=\"token punctuation\">.<\/span>nn<span class=\"token punctuation\">.<\/span>parallel <span class=\"token keyword\">import<\/span> DistributedDataParallel <span class=\"token keyword\">as<\/span> DDP<\/p>\n<p><span class=\"token keyword\">def<\/span> <span class=\"token function\">main<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    dist<span class=\"token punctuation\">.<\/span>init_process_group<span class=\"token punctuation\">(<\/span><span class=\"token string\">&#034;nccl&#034;<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    rank <span class=\"token operator\">&#061;<\/span> dist<span class=\"token punctuation\">.<\/span>get_rank<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    device <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>device<span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;cuda:<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>rank<span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span><br \/>\n    model <span class=\"token operator\">&#061;<\/span> DeepSeek7B<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>device<span class=\"token punctuation\">)<\/span><br \/>\n    model <span class=\"token operator\">&#061;<\/span> DDP<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> device_ids<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">[<\/span>rank<span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    optimizer <span class=\"token operator\">&#061;<\/span> torch<span class=\"token punctuation\">.<\/span>optim<span class=\"token punctuation\">.<\/span>Adam<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">.<\/span>parameters<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token comment\"># \u6570\u636e\u52a0\u8f7d\u5668\u9700\u914d\u5408DistributedSampler<\/span><br \/>\n    dataset <span class=\"token operator\">&#061;<\/span> MyDataset<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n    sampler <span class=\"token operator\">&#061;<\/span> DistributedSampler<span class=\"token punctuation\">(<\/span>dataset<span class=\"token punctuation\">)<\/span><br \/>\n    loader <span class=\"token operator\">&#061;<\/span> DataLoader<span class=\"token punctuation\">(<\/span>dataset<span class=\"token punctuation\">,<\/span> batch_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span> sampler<span class=\"token operator\">&#061;<\/span>sampler<span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">for<\/span> batch <span class=\"token keyword\">in<\/span> loader<span class=\"token punctuation\">:<\/span><br \/>\n        inputs <span class=\"token operator\">&#061;<\/span> batch<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>device<span class=\"token punctuation\">)<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span><br \/>\n        loss <span class=\"token operator\">&#061;<\/span> compute_loss<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">)<\/span><br \/>\n        loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        optimizer<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p><span class=\"token keyword\">if<\/span> __name__ <span class=\"token operator\">&#061;&#061;<\/span> <span class=\"token string\">&#034;__main__&#034;<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    main<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n <\/li>\n<\/ul>\n<h4>2. \u6a21\u578b\u5e76\u884c&#xff08;Model Parallelism&#xff09;<\/h4>\n<ul>\n<li>\u5f20\u91cf\u5e76\u884c&#xff08;Tensor Parallelism&#xff09;&#xff1a; \u5c06\u77e9\u9635\u8fd0\u7b97\u6309\u7ef4\u5ea6\u62c6\u5206&#xff0c;\u4f8b\u5982\u5bf9\u7ebf\u6027\u5c42<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\">\n<p>          Y <\/p>\n<p>          &#061; <\/p>\n<p>          X <\/p>\n<p>          W <\/p>\n<p>         Y &#061; XW <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.2222em\">Y<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><span class=\"mrel\">&#061;<\/span><span class=\"mspace\" style=\"margin-right: 0.2778em\"><\/span><\/span><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.0785em\">X<\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">W<\/span><\/span><\/span><\/span><\/span>&#xff0c;\u5c06\u6743\u91cd\u77e9\u9635<span class=\"katex--inline\"><span class=\"katex\"><span class=\"katex-mathml\"> <\/p>\n<p>          W <\/p>\n<p>         W <\/p>\n<p>     <\/span><span class=\"katex-html\"><span class=\"base\"><span class=\"strut\" style=\"height: 0.6833em\"><\/span><span class=\"mord mathnormal\" style=\"margin-right: 0.1389em\">W<\/span><\/span><\/span><\/span><\/span>\u6309\u5217\u5207\u5206\u5230\u591a\u5361&#xff0c;\u6bcf\u5361\u8ba1\u7b97\u90e8\u5206\u7ed3\u679c\u540e\u62fc\u63a5\u3002<span class=\"token comment\"># DeepSpeed\u5f20\u91cf\u5e76\u884c\u914d\u7f6e&#xff08;\u4ee5DeepSeek-16B\u4e3a\u4f8b&#xff09;<\/span><br \/>\n<span class=\"token keyword\">from<\/span> deepspeed<span class=\"token punctuation\">.<\/span>runtime<span class=\"token punctuation\">.<\/span>pipe<span class=\"token punctuation\">.<\/span>engine <span class=\"token keyword\">import<\/span> PipelineEngine<br \/>\nengine <span class=\"token operator\">&#061;<\/span> PipelineEngine<span class=\"token punctuation\">(<\/span><br \/>\n    model<span class=\"token operator\">&#061;<\/span>deepseek_model<span class=\"token punctuation\">,<\/span><br \/>\n    config<span class=\"token operator\">&#061;<\/span>deepspeed_config<span class=\"token punctuation\">,<\/span><br \/>\n    tensor_parallel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span>  <span class=\"token comment\"># 4\u5361\u5f20\u91cf\u5e76\u884c<\/span><br \/>\n    pipeline_parallel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span> <span class=\"token comment\"># 2\u5361\u6d41\u6c34\u7ebf\u5e76\u884c<\/span><br \/>\n<span class=\"token punctuation\">)<\/span>\n <\/li>\n<li>\u4e13\u5bb6\u5e76\u884c&#xff08;Expert Parallelism&#xff09;&#xff1a; MoE\u6a21\u578b\u4e2d&#xff0c;\u6bcf\u4e2a\u4e13\u5bb6&#xff08;Expert&#xff09;\u5206\u914d\u5230\u4e0d\u540c\u663e\u5361\u3002\u4ee5DeepSeek-MoE-16B&#xff08;16\u4e13\u5bb6&#xff09;\u4e3a\u4f8b&#xff1a;<span class=\"token comment\"># DeepSeek-MoE\u7684\u4e13\u5bb6\u5206\u7247\u7b56\u7565<\/span><br \/>\n<span class=\"token keyword\">class<\/span> <span class=\"token class-name\">MoELayer<\/span><span class=\"token punctuation\">(<\/span>nn<span class=\"token punctuation\">.<\/span>Module<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    <span class=\"token keyword\">def<\/span> <span class=\"token function\">__init__<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> num_experts<span class=\"token operator\">&#061;<\/span><span class=\"token number\">16<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token builtin\">super<\/span><span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>__init__<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        self<span class=\"token punctuation\">.<\/span>experts <span class=\"token operator\">&#061;<\/span> nn<span class=\"token punctuation\">.<\/span>ModuleList<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">[<\/span><br \/>\n            Expert<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span><span class=\"token string-interpolation\"><span class=\"token string\">f&#034;cuda:<\/span><span class=\"token interpolation\"><span class=\"token punctuation\">{<\/span>i <span class=\"token operator\">%<\/span> <span class=\"token number\">4<\/span><span class=\"token punctuation\">}<\/span><\/span><span class=\"token string\">&#034;<\/span><\/span><span class=\"token punctuation\">)<\/span>  <span class=\"token comment\"># 4\u5361\u5747\u5300\u5206\u914d\u4e13\u5bb6<\/span><br \/>\n            <span class=\"token keyword\">for<\/span> i <span class=\"token keyword\">in<\/span> <span class=\"token builtin\">range<\/span><span class=\"token punctuation\">(<\/span>num_experts<span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">)<\/span><\/p>\n<p>    <span class=\"token keyword\">def<\/span> <span class=\"token function\">forward<\/span><span class=\"token punctuation\">(<\/span>self<span class=\"token punctuation\">,<\/span> x<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n        <span class=\"token comment\"># \u52a8\u6001\u8def\u7531\u903b\u8f91<\/span><br \/>\n        gate_scores <span class=\"token operator\">&#061;<\/span> compute_gate<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">)<\/span><br \/>\n        selected_experts <span class=\"token operator\">&#061;<\/span> topk<span class=\"token punctuation\">(<\/span>gate_scores<span class=\"token punctuation\">,<\/span> k<span class=\"token operator\">&#061;<\/span><span class=\"token number\">2<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        outputs <span class=\"token operator\">&#061;<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token punctuation\">]<\/span><br \/>\n        <span class=\"token keyword\">for<\/span> expert_idx <span class=\"token keyword\">in<\/span> selected_experts<span class=\"token punctuation\">:<\/span><br \/>\n            expert <span class=\"token operator\">&#061;<\/span> self<span class=\"token punctuation\">.<\/span>experts<span class=\"token punctuation\">[<\/span>expert_idx<span class=\"token punctuation\">]<\/span><br \/>\n            outputs<span class=\"token punctuation\">.<\/span>append<span class=\"token punctuation\">(<\/span>expert<span class=\"token punctuation\">(<\/span>x<span class=\"token punctuation\">.<\/span>to<span class=\"token punctuation\">(<\/span>expert<span class=\"token punctuation\">.<\/span>device<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span><br \/>\n        <span class=\"token keyword\">return<\/span> <span class=\"token builtin\">sum<\/span><span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">)<\/span>\n <\/li>\n<\/ul>\n<h4>3. \u6df7\u5408\u5e76\u884c&#xff08;Hybrid Parallelism&#xff09;<\/h4>\n<ul>\n<li>DeepSeek\u7684\u6df7\u5408\u7b56\u7565&#xff1a; \u57284096\u5361\u96c6\u7fa4\u4e2d\u7ec4\u5408\u4e09\u79cd\u5e76\u884c\u6a21\u5f0f&#xff1a;\n<li>\u6570\u636e\u5e76\u884c&#xff1a;\u5168\u5c40\u6279\u6b21\u5927\u5c0f&#061;1024&#xff0c;\u6bcf\u7ec4256\u5361\u3002<\/li>\n<li>\u5f20\u91cf\u5e76\u884c&#xff1a;\u6bcf\u7ec4\u51854\u5361\u62c6\u5206\u6a21\u578b\u5c42\u3002<\/li>\n<li>\u6d41\u6c34\u7ebf\u5e76\u884c&#xff1a;\u8de8\u7ec4\u62c6\u5206\u6a21\u578b\u5c42\u4e3a8\u4e2a\u9636\u6bb5\u3002<\/li>\n<\/li>\n<li>\u901a\u4fe1\u4f18\u5316&#xff1a; \u4f7f\u7528NCCL\u7684IB_HCA&#061;mlx5\u53c2\u6570\u542f\u7528InfiniBand RDMA&#xff0c;\u964d\u4f4e\u8de8\u8282\u70b9\u901a\u4fe1\u5ef6\u8fdf\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u4e09\u3001\u5206\u5e03\u5f0f\u8bad\u7ec3\u6280\u672f&#xff1a;\u6027\u80fd\u74f6\u9888\u4e0e\u4f18\u5316<\/h3>\n<h4>1. \u6027\u80fd\u5206\u6790\u5de5\u5177<\/h4>\n<ul>\n<li>Nsight Systems&#xff1a; \u751f\u6210\u65f6\u95f4\u7ebf\u89c6\u56fe&#xff0c;\u5b9a\u4f4d\u901a\u4fe1\u4e0e\u8ba1\u7b97\u7684\u91cd\u53e0\u533a\u57df\u3002nsys profile <span class=\"token parameter variable\">-o<\/span> report.qdrep python train.py\n <\/li>\n<li>DeepSpeed Flops Profiler&#xff1a; \u7edf\u8ba1\u6bcf\u5c42\u7684\u6d6e\u70b9\u8fd0\u7b97\u91cf\u4e0e\u901a\u4fe1\u8017\u65f6\u3002<span class=\"token keyword\">from<\/span> deepspeed<span class=\"token punctuation\">.<\/span>profiling<span class=\"token punctuation\">.<\/span>flops_profiler <span class=\"token keyword\">import<\/span> get_model_profile<br \/>\nflops<span class=\"token punctuation\">,<\/span> macs<span class=\"token punctuation\">,<\/span> params <span class=\"token operator\">&#061;<\/span> get_model_profile<span class=\"token punctuation\">(<\/span>model<span class=\"token punctuation\">,<\/span> input_shape<span class=\"token operator\">&#061;<\/span><span class=\"token punctuation\">(<\/span><span class=\"token number\">1<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">1024<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\n <\/li>\n<\/ul>\n<h4>2. \u901a\u4fe1\u4f18\u5316\u5b9e\u8df5<\/h4>\n<ul>\n<li>\u68af\u5ea6\u538b\u7f29&#xff1a; \u4f7f\u7528FP16\u6df7\u5408\u7cbe\u5ea6\u8bad\u7ec3&#xff0c;\u901a\u4fe1\u91cf\u51cf\u5c1150%\u3002<span class=\"token comment\"># PyTorch AMP\u81ea\u52a8\u6df7\u5408\u7cbe\u5ea6<\/span><br \/>\nscaler <span class=\"token operator\">&#061;<\/span> GradScaler<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\n<span class=\"token keyword\">with<\/span> autocast<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span><br \/>\n    outputs <span class=\"token operator\">&#061;<\/span> model<span class=\"token punctuation\">(<\/span>inputs<span class=\"token punctuation\">)<\/span><br \/>\n    loss <span class=\"token operator\">&#061;<\/span> compute_loss<span class=\"token punctuation\">(<\/span>outputs<span class=\"token punctuation\">)<\/span><br \/>\nscaler<span class=\"token punctuation\">.<\/span>scale<span class=\"token punctuation\">(<\/span>loss<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><br \/>\nscaler<span class=\"token punctuation\">.<\/span>step<span class=\"token punctuation\">(<\/span>optimizer<span class=\"token punctuation\">)<\/span><br \/>\nscaler<span class=\"token punctuation\">.<\/span>update<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>\n <\/li>\n<li>\u8ba1\u7b97\u4e0e\u901a\u4fe1\u91cd\u53e0&#xff1a; \u5728\u53cd\u5411\u4f20\u64ad\u65f6\u5f02\u6b65\u53d1\u9001\u68af\u5ea6\u3002<span class=\"token keyword\">with<\/span> model<span class=\"token punctuation\">.<\/span>no_sync<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">:<\/span>  <span class=\"token comment\"># \u4ec5\u9650DDP\u6a21\u5f0f<\/span><br \/>\n    loss<span class=\"token punctuation\">.<\/span>backward<span class=\"token punctuation\">(<\/span><span class=\"token punctuation\">)<\/span>     <span class=\"token comment\"># \u5ef6\u8fdf\u540c\u6b65<\/span>\n <\/li>\n<\/ul>\n<hr \/>\n<h3>\u56db\u3001\u63a8\u7406\u5f15\u64ce\u5bf9\u6bd4&#xff1a;Ollama\u4e0evLLM\u7684\u6df1\u5ea6\u89e3\u6790<\/h3>\n<h4>1. Ollama\u7684\u591a\u663e\u5361\u5b9e\u73b0<\/h4>\n<ul>\n<li>\u67b6\u6784\u8bbe\u8ba1&#xff1a;\n<p>    #mermaid-svg-6JQmdfsrDeDdIGbH {font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;fill:#333;}#mermaid-svg-6JQmdfsrDeDdIGbH .error-icon{fill:#552222;}#mermaid-svg-6JQmdfsrDeDdIGbH .error-text{fill:#552222;stroke:#552222;}#mermaid-svg-6JQmdfsrDeDdIGbH .edge-thickness-normal{stroke-width:2px;}#mermaid-svg-6JQmdfsrDeDdIGbH .edge-thickness-thick{stroke-width:3.5px;}#mermaid-svg-6JQmdfsrDeDdIGbH .edge-pattern-solid{stroke-dasharray:0;}#mermaid-svg-6JQmdfsrDeDdIGbH .edge-pattern-dashed{stroke-dasharray:3;}#mermaid-svg-6JQmdfsrDeDdIGbH .edge-pattern-dotted{stroke-dasharray:2;}#mermaid-svg-6JQmdfsrDeDdIGbH .marker{fill:#333333;stroke:#333333;}#mermaid-svg-6JQmdfsrDeDdIGbH .marker.cross{stroke:#333333;}#mermaid-svg-6JQmdfsrDeDdIGbH svg{font-family:\\&#8221;trebuchet ms\\&#8221;,verdana,arial,sans-serif;font-size:16px;}#mermaid-svg-6JQmdfsrDeDdIGbH 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L-LE-GPU0\">\u5206\u53d1<\/span> <\/p>\n<p>           <span id=\"L-L-Router-GPU1\" class=\"edgeLabel L-LS-Router&#039; L-LE-GPU1\">\u5206\u53d1<\/span> <\/p>\n<p>           <span id=\"L-L-Router-GPU2\" class=\"edgeLabel L-LS-Router&#039; L-LE-GPU2\">\u5206\u53d1<\/span> <\/p>\n<p>           <span id=\"L-L-GPU0-Response\" class=\"edgeLabel L-LS-GPU0&#039; L-LE-Response\">\u7ed3\u679c\u805a\u5408<\/span> <\/p>\n<p>             Client <\/p>\n<p>             Router <\/p>\n<p>             \u6a21\u578b\u526f\u672c0 <\/p>\n<p>             \u6a21\u578b\u526f\u672c1 <\/p>\n<p>             \u6a21\u578b\u526f\u672c2 <\/p>\n<p>             Response <\/p>\n<\/li>\n<li>\u6027\u80fd\u74f6\u9888&#xff1a; \u5355\u8bf7\u6c42\u65e0\u6cd5\u8de8\u5361\u52a0\u901f&#xff0c;\u9002\u5408\u9ad8\u5e76\u53d1\u4f46\u4f4e\u5ef6\u65f6\u4e0d\u654f\u611f\u573a\u666f\u3002<\/li>\n<\/ul>\n<h4>2. vLLM\u7684\u9ad8\u541e\u5410\u79d8\u5bc6<\/h4>\n<ul>\n<li>PagedAttention\u5b9e\u73b0&#xff1a; \u5c06KV Cache\u5212\u5206\u4e3a\u56fa\u5b9a\u5927\u5c0f\u7684\u9875&#xff08;\u59824MB&#xff09;&#xff0c;\u52a8\u6001\u5206\u914d\u663e\u5b58\u3002<span class=\"token comment\"># vLLM\u7684KV Cache\u5206\u9875\u7ba1\u7406<\/span><br \/>\n<span class=\"token keyword\">from<\/span> vllm <span class=\"token keyword\">import<\/span> LLMEngine<br \/>\nengine <span class=\"token operator\">&#061;<\/span> LLMEngine<span class=\"token punctuation\">(<\/span><br \/>\n    model<span class=\"token operator\">&#061;<\/span><span class=\"token string\">&#034;deepseek-16b&#034;<\/span><span class=\"token punctuation\">,<\/span><br \/>\n    tensor_parallel_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span>     <span class=\"token comment\"># 4\u5361\u5f20\u91cf\u5e76\u884c<\/span><br \/>\n    block_size<span class=\"token operator\">&#061;<\/span><span class=\"token number\">64<\/span><span class=\"token punctuation\">,<\/span>             <span class=\"token comment\"># \u6bcf\u9875\u5b58\u50a864\u4e2atoken<\/span><br \/>\n    gpu_memory_utilization<span class=\"token operator\">&#061;<\/span><span class=\"token number\">0.9<\/span> <span class=\"token comment\"># \u663e\u5b58\u5229\u7528\u7387\u8fbe90%<\/span><br \/>\n<span class=\"token punctuation\">)<\/span>\n <\/li>\n<li>\u8fde\u7eed\u6279\u5904\u7406&#xff08;Continuous Batching&#xff09;&#xff1a; \u52a8\u6001\u5408\u5e76\u591a\u4e2a\u8bf7\u6c42\u7684\u6ce8\u610f\u529b\u8ba1\u7b97\u56fe&#xff0c;GPU\u5229\u7528\u7387\u63d0\u5347\u81f380%\u4ee5\u4e0a\u3002<\/li>\n<\/ul>\n<h4>3. \u5b9e\u6d4b\u6027\u80fd\u5bf9\u6bd4<\/h4>\n<table>\n<tr>\u573a\u666fOllama&#xff08;A100\u00d74&#xff09;vLLM&#xff08;A100\u00d74&#xff09;<\/tr>\n<tbody>\n<tr>\n<td>\u5355\u8bf7\u6c42\u5ef6\u8fdf&#xff08;1K tokens&#xff09;<\/td>\n<td>120ms<\/td>\n<td>75ms<\/td>\n<\/tr>\n<tr>\n<td>\u541e\u5410\u91cf&#xff08;QPS&#xff09;<\/td>\n<td>850<\/td>\n<td>3200<\/td>\n<\/tr>\n<tr>\n<td>\u663e\u5b58\u5360\u7528&#xff08;16B\u6a21\u578b&#xff09;<\/td>\n<td>32GB<\/td>\n<td>24GB<\/td>\n<\/tr>\n<tr>\n<td>\u6269\u5c55\u6548\u7387&#xff08;1\u21924\u5361&#xff09;<\/td>\n<td>2.8x<\/td>\n<td>3.6x<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<h3>\u4e94\u3001\u672a\u6765\u65b9\u5411&#xff1a;\u786c\u4ef6\u4e0e\u8f6f\u4ef6\u7684\u534f\u540c\u8fdb\u5316<\/h3>\n<h4>1. \u901a\u4fe1\u786c\u4ef6\u521b\u65b0<\/h4>\n<ul>\n<li>NVSwitch 3.0&#xff1a; \u652f\u630118\u5757GPU\u5168\u4e92\u8054&#xff0c;\u53cc\u5411\u5e26\u5bbd\u63d0\u5347\u81f3900GB\/s\u3002<\/li>\n<li>CXL 3.0\u5185\u5b58\u6c60\u5316&#xff1a; \u5141\u8bb8GPU\u901a\u8fc7CXL\u534f\u8bae\u5171\u4eabCPU\u5185\u5b58&#xff0c;\u7a81\u7834\u663e\u5b58\u5bb9\u91cf\u9650\u5236\u3002<\/li>\n<\/ul>\n<h4>2. \u8f6f\u4ef6\u6808\u4f18\u5316<\/h4>\n<ul>\n<li>\u7f16\u8bd1\u4f18\u5316&#xff1a; \u4f7f\u7528MLIR\u7edf\u4e00\u4e2d\u95f4\u8868\u793a&#xff0c;\u81ea\u52a8\u751f\u6210\u5f20\u91cf\u5e76\u884c\u4ee3\u7801\u3002<span class=\"token comment\"># \u4f7f\u7528IREE\u7f16\u8bd1\u5668\u4f18\u5316\u6a21\u578b<\/span><br \/>\niree-compile &#8211;iree-hal-target-backends<span class=\"token operator\">&#061;<\/span>cuda model.mlir <span class=\"token parameter variable\">-o<\/span> compiled.vmfb\n <\/li>\n<li>\u81ea\u9002\u5e94\u5e76\u884c\u7b56\u7565&#xff1a; DeepSeek\u7684\u81ea\u52a8\u5207\u5206\u5de5\u5177\u6839\u636e\u6a21\u578b\u7ed3\u6784\u9009\u62e9\u6700\u4f18\u5e76\u884c\u65b9\u6848\u3002<\/li>\n<\/ul>\n<hr \/>\n<h3>\u516d\u3001\u603b\u7ed3&#xff1a;\u591a\u663e\u5361\u65b9\u6848\u7684\u9009\u578b\u6307\u5357<\/h3>\n<h4>1. \u8bad\u7ec3\u573a\u666f<\/h4>\n<ul>\n<li>\u4e2d\u5c0f\u6a21\u578b&#xff08;&lt;10B&#xff09;&#xff1a;\u6570\u636e\u5e76\u884c&#xff08;PyTorch DDP&#xff09;&#xff0c;\u4ee3\u7801\u7b80\u5355\u4e14\u6269\u5c55\u9ad8\u6548\u3002<\/li>\n<li>\u8d85\u5927\u6a21\u578b&#xff08;&gt;100B&#xff09;&#xff1a;\u6df7\u5408\u5e76\u884c&#xff08;DeepSpeed &#043; Megatron-LM&#xff09;&#xff0c;\u9700\u7cbe\u7ec6\u8c03\u4f18\u901a\u4fe1\u7b56\u7565\u3002<\/li>\n<\/ul>\n<h4>2. \u63a8\u7406\u573a\u666f<\/h4>\n<ul>\n<li>\u9ad8\u5e76\u53d1API\u670d\u52a1&#xff1a;Ollama\u4efb\u52a1\u7ea7\u5e76\u884c&#xff0c;\u5feb\u901f\u6269\u5c55\u5b9e\u4f8b\u6570\u3002<\/li>\n<li>\u4f4e\u5ef6\u8fdf\u5b9e\u65f6\u63a8\u7406&#xff1a;vLLM\u5f20\u91cf\u5e76\u884c &#043; PagedAttention&#xff0c;\u6700\u5927\u5316\u786c\u4ef6\u5229\u7528\u7387\u3002<\/li>\n<\/ul>\n<h4>3. \u786c\u4ef6\u9009\u578b\u5efa\u8bae<\/h4>\n<table>\n<tr>\u9700\u6c42\u63a8\u8350\u914d\u7f6e<\/tr>\n<tbody>\n<tr>\n<td>\u4f4e\u6210\u672c\u8bad\u7ec3<\/td>\n<td>8\u00d7RTX 4090&#xff08;NVLink\u6865\u63a5&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u9ad8\u6027\u80fd\u63a8\u7406<\/td>\n<td>4\u00d7A100 80GB&#xff08;NVSwitch\u4e92\u8054&#xff09;<\/td>\n<\/tr>\n<tr>\n<td>\u8d85\u5927\u89c4\u6a21\u8bad\u7ec3<\/td>\n<td>\u534e\u4e3a\u6607\u817e910\u96c6\u7fa4 &#043; 200G IB\u7f51\u7edc<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<hr \/>\n<p>\u9644\u5f55<\/p>\n<ul>\n<li>DeepSeek\u5f00\u6e90\u4ee3\u7801\u5e93<\/li>\n<li>vLLM\u5b98\u65b9\u6587\u6863<\/li>\n<li>NVIDIA Nsight\u5de5\u5177\u6307\u5357<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1.5k\u6b21\uff0c\u70b9\u8d5e9\u6b21\uff0c\u6536\u85cf33\u6b21\u3002\u968f\u7740\u5927\u6a21\u578b\u53c2\u6570\u89c4\u6a21\u7a81\u7834\u5343\u4ebf\u7ea7\uff08\u5982GPT-4\u3001DeepSeek\uff09\uff0c\u5355\u663e\u5361\u7684\u663e\u5b58\u5bb9\u91cf\u4e0e\u7b97\u529b\u5df2\u65e0\u6cd5\u6ee1\u8db3\u9700\u6c42\u3002\u591a\u663e\u5361\u5e76\u884c\u8ba1\u7b97\u6210\u4e3a\u8bad\u7ec3\u4e0e\u63a8\u7406\u7684\u6838\u5fc3\u6280\u672f\uff0c\u5176\u6838\u5fc3\u6311\u6218\u5728\u4e8e\u4e0e\u3002\u672c\u6587\u4ee5\u56fd\u4ea7\u5927\u6a21\u578bDeepSeek\u4e3a\u4f8b\uff0c\u7ed3\u5408Ollama\u4e0evLLM\u63a8\u7406\u5f15\u64ce\uff0c\u6df1\u5ea6\u5256\u6790\u591a\u663e\u5361\u534f\u540c\u5de5\u4f5c\u7684\u6280\u672f\u5b9e\u73b0\uff0c\u5e76\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\u3001\u6027\u80fd\u6570\u636e\u4e0e\u67b6\u6784\u56fe\u5c55\u793a\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u3002\uff1a\u5c06\u8bad\u7ec3\u6570\u636e\u5212\u5206\u4e3a\u591a\u4e2a\u6279\u6b21\uff0c\u6bcf\u4e2a\u663e\u5361\u6301\u6709\u5b8c\u6574\u7684\u6a21\u578b\u526f\u672c\uff0c\u72ec\u7acb\u8ba1\u7b97\u68af\u5ea6\u540e\u540c\u6b65\u66f4\u65b0\u3002fill:#333;color:#333;color:#333;fill:none;AllGather\u3002_vllm<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[208],"topic":[],"class_list":["post-21088","post","type-post","status-publish","format-standard","hentry","category-server","tag-gpu"],"yoast_head":"<!-- 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\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\u6587\u7ae0\u6d4f\u89c8\u9605\u8bfb1.5k\u6b21\uff0c\u70b9\u8d5e9\u6b21\uff0c\u6536\u85cf33\u6b21\u3002\u968f\u7740\u5927\u6a21\u578b\u53c2\u6570\u89c4\u6a21\u7a81\u7834\u5343\u4ebf\u7ea7\uff08\u5982GPT-4\u3001DeepSeek\uff09\uff0c\u5355\u663e\u5361\u7684\u663e\u5b58\u5bb9\u91cf\u4e0e\u7b97\u529b\u5df2\u65e0\u6cd5\u6ee1\u8db3\u9700\u6c42\u3002\u591a\u663e\u5361\u5e76\u884c\u8ba1\u7b97\u6210\u4e3a\u8bad\u7ec3\u4e0e\u63a8\u7406\u7684\u6838\u5fc3\u6280\u672f\uff0c\u5176\u6838\u5fc3\u6311\u6218\u5728\u4e8e\u4e0e\u3002\u672c\u6587\u4ee5\u56fd\u4ea7\u5927\u6a21\u578bDeepSeek\u4e3a\u4f8b\uff0c\u7ed3\u5408Ollama\u4e0evLLM\u63a8\u7406\u5f15\u64ce\uff0c\u6df1\u5ea6\u5256\u6790\u591a\u663e\u5361\u534f\u540c\u5de5\u4f5c\u7684\u6280\u672f\u5b9e\u73b0\uff0c\u5e76\u901a\u8fc7\u4ee3\u7801\u793a\u4f8b\u3001\u6027\u80fd\u6570\u636e\u4e0e\u67b6\u6784\u56fe\u5c55\u793a\u5b8c\u6574\u89e3\u51b3\u65b9\u6848\u3002\uff1a\u5c06\u8bad\u7ec3\u6570\u636e\u5212\u5206\u4e3a\u591a\u4e2a\u6279\u6b21\uff0c\u6bcf\u4e2a\u663e\u5361\u6301\u6709\u5b8c\u6574\u7684\u6a21\u578b\u526f\u672c\uff0c\u72ec\u7acb\u8ba1\u7b97\u68af\u5ea6\u540e\u540c\u6b65\u66f4\u65b0\u3002fill:#333;color:#333;color:#333;fill:none;AllGather\u3002_vllm\" 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