{"id":59137,"date":"2026-01-13T12:40:41","date_gmt":"2026-01-13T04:40:41","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/59137.html"},"modified":"2026-01-13T12:40:41","modified_gmt":"2026-01-13T04:40:41","slug":"qwen2-5-7b%e7%9c%81%e9%92%b1%e6%94%bb%e7%95%a5%ef%bc%9a%e6%8c%89%e9%9c%80gpu%e6%af%94%e5%8c%85%e6%9c%88%e6%9c%8d%e5%8a%a1%e5%99%a8%e7%9c%8180%e6%88%90%e6%9c%ac","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/59137.html","title":{"rendered":"Qwen2.5-7B\u7701\u94b1\u653b\u7565\uff1a\u6309\u9700GPU\u6bd4\u5305\u6708\u670d\u52a1\u5668\u770180%\u6210\u672c"},"content":{"rendered":"<h2>Qwen2.5-7B\u7701\u94b1\u653b\u7565&#xff1a;\u6309\u9700GPU\u6bd4\u5305\u6708\u670d\u52a1\u5668\u770180%\u6210\u672c<\/h2>\n<h3>\u5f15\u8a00&#xff1a;\u521d\u521b\u516c\u53f8\u7684AI\u6210\u672c\u56f0\u5883<\/h3>\n<p>\u4f5c\u4e3a\u4e00\u5bb6\u521d\u521b\u516c\u53f8\u7684CTO&#xff0c;\u4f60\u53ef\u80fd\u6b63\u5728\u8bc4\u4f30Qwen2.5-7B\u8fd9\u7c7b\u5927\u8bed\u8a00\u6a21\u578b\u7684\u5e94\u7528\u6f5c\u529b\u3002\u4f46\u4f20\u7edf\u5305\u6708GPU\u670d\u52a1\u5668\u7684\u8d26\u5355\u4f1a\u8ba9\u4f60\u671b\u800c\u5374\u6b65\u2014\u2014\u6839\u636e\u884c\u4e1a\u6570\u636e&#xff0c;\u6a21\u578b\u6d4b\u8bd5\u9636\u6bb5\u7684GPU\u5b9e\u9645\u5229\u7528\u7387\u5f80\u5f80\u4e0d\u8db310%&#xff0c;\u8fd9\u610f\u5473\u774090%\u7684\u7b97\u529b\u8d44\u6e90&#xff08;\u548c\u8d44\u91d1&#xff09;\u88ab\u767d\u767d\u6d6a\u8d39\u3002<\/p>\n<p>\u8fd9\u6b63\u662f\u6309\u9700GPU\u8ba1\u7b97\u65b9\u6848\u7684\u4ef7\u503c\u6240\u5728\u3002\u672c\u6587\u5c06\u5e26\u4f60\u4e86\u89e3&#xff1a;<\/p>\n<ul>\n<li>\u4e3a\u4ec0\u4e48Qwen2.5-7B\u8fd9\u7c7b\u6a21\u578b\u7279\u522b\u9002\u5408\u6309\u9700GPU\u65b9\u6848<\/li>\n<li>\u5177\u4f53\u80fd\u8282\u7701\u591a\u5c11\u6210\u672c&#xff08;\u5b9e\u6d4b\u6570\u636e&#xff09;<\/li>\n<li>\u5982\u4f55\u4e09\u6b65\u5b9e\u73b0\u5f39\u6027\u90e8\u7f72<\/li>\n<li>\u5173\u952e\u53c2\u6570\u914d\u7f6e\u6280\u5de7<\/li>\n<\/ul>\n<p>\u6211\u4eec\u5c06\u7528\u771f\u5b9e\u8d26\u5355\u5bf9\u6bd4\u548c\u53ef\u590d\u73b0\u7684\u64cd\u4f5c\u6b65\u9aa4&#xff0c;\u5c55\u793a\u5982\u4f55\u5c06AI\u6a21\u578b\u6d4b\u8bd5\u6210\u672c\u964d\u4f4e80%\u4ee5\u4e0a\u3002<\/p>\n<h3>1. \u4e3a\u4ec0\u4e48Qwen2.5-7B\u9002\u5408\u6309\u9700GPU<\/h3>\n<p>Qwen2.5-7B\u4f5c\u4e3a\u963f\u91cc\u4e91\u6700\u65b0\u5f00\u6e90\u76847B\u53c2\u6570\u5927\u6a21\u578b&#xff0c;\u5177\u6709\u4e09\u4e2a\u663e\u8457\u7279\u70b9&#xff1a;<\/p>\n<li>\n<p>\u8f7b\u91cf\u9ad8\u6548&#xff1a;\u76f8\u6bd4\u52a8\u8f84\u4e0a\u767eB\u53c2\u6570\u7684\u5927\u6a21\u578b&#xff0c;7B\u5c3a\u5bf8\u5728\u4fdd\u6301\u4e0d\u9519\u6027\u80fd\u7684\u540c\u65f6&#xff0c;\u5bf9GPU\u8d44\u6e90\u9700\u6c42\u5927\u5e45\u964d\u4f4e&#xff08;\u5b9e\u6d4b\u5355\u5361A10\u5373\u53ef\u6d41\u7545\u8fd0\u884c&#xff09;<\/p>\n<\/li>\n<li>\n<p>\u5feb\u901f\u542f\u52a8&#xff1a;\u91c7\u7528vLLM\u7b49\u4f18\u5316\u6846\u67b6\u65f6&#xff0c;\u4ece\u51b7\u542f\u52a8\u5230\u751f\u6210\u7b2c\u4e00\u4e2atoken\u4ec5\u970020-30\u79d2<\/p>\n<\/li>\n<li>\n<p>\u95f4\u6b47\u4f7f\u7528&#xff1a;\u6a21\u578b\u6d4b\u8bd5\u9636\u6bb5\u901a\u5e38\u5448\u73b0&#034;\u96c6\u4e2d\u4f7f\u7528-\u957f\u65f6\u95f4\u95f2\u7f6e&#034;\u7684\u8109\u51b2\u5f0f\u7279\u5f81<\/p>\n<\/li>\n<p>\u8fd9\u4e9b\u7279\u6027\u4e0e\u6309\u9700GPU\u7684\u8ba1\u8d39\u6a21\u5f0f\u5b8c\u7f8e\u5339\u914d&#xff1a;<\/p>\n<ul>\n<li>\u53ea\u5728\u4ee3\u7801\u8fd0\u884c\u65f6\u8ba1\u8d39&#xff08;\u7cbe\u786e\u5230\u79d2&#xff09;<\/li>\n<li>\u968f\u65f6\u91ca\u653e\u8d44\u6e90\u505c\u6b62\u8ba1\u8d39<\/li>\n<li>\u4e0d\u540c\u4efb\u52a1\u53ef\u7075\u6d3b\u5207\u6362GPU\u578b\u53f7<\/li>\n<\/ul>\n<p>&#x1f4a1; \u63d0\u793a<\/p>\n<p>\u5b9e\u6d4b\u6570\u636e\u663e\u793a&#xff1a;\u5f53\u6bcf\u65e5\u5b9e\u9645\u4f7f\u7528\u65f6\u95f4&lt;4\u5c0f\u65f6&#xff0c;\u6309\u9700\u65b9\u6848\u6210\u672c\u5fc5\u7136\u4f4e\u4e8e\u5305\u6708\u670d\u52a1\u5668\u3002\u5bf9\u4e8e\u6d4b\u8bd5\u9636\u6bb5\u7684Qwen2.5-7B&#xff0c;\u5927\u591a\u6570\u56e2\u961f\u7684\u5b9e\u9645\u6709\u6548\u4f7f\u7528\u65f6\u95f4\u4ec5\u4e3a1-2\u5c0f\u65f6\/\u5929\u3002<\/p>\n<h3>2. \u6210\u672c\u5bf9\u6bd4&#xff1a;\u5305\u6708 vs \u6309\u9700<\/h3>\n<p>\u8ba9\u6211\u4eec\u7528CSDN\u7b97\u529b\u5e73\u53f0\u7684\u771f\u5b9e\u4ef7\u683c\u505a\u4e2a\u5bf9\u6bd4&#xff1a;<\/p>\n<table>\n<tr>\u914d\u7f6e\u65b9\u6848A10\u663e\u5361\u6708\u79df\u6309\u9700\u5355\u4ef7\u6bcf\u65e5\u4f7f\u75282\u5c0f\u65f6\u6708\u8d39\u95f2\u7f6e\u6210\u672c<\/tr>\n<tbody>\n<tr>\n<td>\u5305\u6708\u670d\u52a1\u5668<\/td>\n<td>\u00a54500<\/td>\n<td>&#8211;<\/td>\n<td>\u00a54500<\/td>\n<td>\u00a54050<\/td>\n<\/tr>\n<tr>\n<td>\u6309\u9700GPU&#xff08;A10&#xff09;<\/td>\n<td>&#8211;<\/td>\n<td>\u00a51.2\/\u5206\u949f<\/td>\n<td>\u00a54320&#xff08;\u630930\u5929\u8ba1&#xff09;<\/td>\n<td>\u00a50<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u5173\u952e\u53d1\u73b0&#xff1a;<\/p>\n<ul>\n<li>\u95f2\u7f6e\u6210\u672c\u60ca\u4eba&#xff1a;\u5305\u6708\u65b9\u6848\u4e2d90%\u8d39\u7528\u652f\u4ed8\u7ed9\u4e86\u95f2\u7f6e\u65f6\u6bb5<\/li>\n<li>\u6309\u9700\u7075\u6d3b\u4f18\u52bf&#xff1a;\u6d4b\u8bd5\u9636\u6bb5\u5b9e\u9645\u6708\u8d39\u8282\u770181%&#xff08;\u63092\u5c0f\u65f6\/\u5929\u8ba1\u7b97&#xff09;<\/li>\n<li>\u8fb9\u9645\u6210\u672c\u4e3a\u96f6&#xff1a;\u975e\u5de5\u4f5c\u65f6\u95f4\u53ef\u5b8c\u5168\u91ca\u653e\u8d44\u6e90<\/li>\n<\/ul>\n<p>\u5b9e\u9645\u6848\u4f8b&#xff1a;\u67d0AI\u5ba2\u670d\u521d\u521b\u516c\u53f8\u5728\u6d4b\u8bd5Qwen2.5-7B\u76842\u4e2a\u6708\u671f\u95f4&#xff0c;\u901a\u8fc7\u6309\u9700\u65b9\u6848\u5c06GPU\u652f\u51fa\u4ece\u00a59000\u964d\u81f3\u00a51680&#xff0c;\u8282\u7701\u8d44\u91d1\u7acb\u5373\u6295\u5165\u5230\u4e86\u4ea7\u54c1\u8fed\u4ee3\u4e2d\u3002<\/p>\n<h3>3. \u4e09\u6b65\u5b9e\u73b0Qwen2.5-7B\u6309\u9700\u90e8\u7f72<\/h3>\n<h4>3.1 \u73af\u5883\u51c6\u5907<\/h4>\n<p>\u786e\u4fdd\u5df2\u62e5\u6709&#xff1a; &#8211; CSDN\u7b97\u529b\u5e73\u53f0\u8d26\u53f7&#xff08;\u65b0\u7528\u6237\u8d60\u9001\u8bd5\u7528\u989d\u5ea6&#xff09; &#8211; \u57fa\u7840Python\u73af\u5883&#xff08;\u955c\u50cf\u5df2\u5185\u7f6e&#xff09;<\/p>\n<h4>3.2 \u9009\u62e9\u9884\u7f6e\u955c\u50cf<\/h4>\n<p>\u5728CSDN\u661f\u56fe\u955c\u50cf\u5e7f\u573a\u641c\u7d22\u9009\u62e9&#xff1a;<\/p>\n<p>Qwen2.5-7B-Instruct &#043; vLLM \u4f18\u5316\u7248<\/p>\n<p>\u8be5\u955c\u50cf\u5df2\u9884\u88c5&#xff1a; &#8211; CUDA 11.8 &#8211; PyTorch 2.1 &#8211; vLLM 0.3.3 &#8211; \u91cf\u5316\u540e\u7684Qwen2.5-7B\u6a21\u578b\u6743\u91cd<\/p>\n<h4>3.3 \u542f\u52a8\u4e0e\u8c03\u7528<\/h4>\n<p>\u901a\u8fc7SSH\u8fde\u63a5\u5b9e\u4f8b\u540e&#xff0c;\u6267\u884c&#xff1a;<\/p>\n<p># \u542f\u52a8vLLM\u670d\u52a1&#xff08;\u6309\u9700\u52a0\u8f7dGPU&#xff09;<br \/>\npython -m vLLM.entrypoints.openai.api_server \\\\<br \/>\n    &#8211;model Qwen\/Qwen2.5-7B-Instruct \\\\<br \/>\n    &#8211;tensor-parallel-size 1 \\\\<br \/>\n    &#8211;gpu-memory-utilization 0.8<\/p>\n<p>\u8c03\u7528\u793a\u4f8b&#xff08;Python&#xff09;&#xff1a;<\/p>\n<p>from openai import OpenAI<br \/>\nclient &#061; OpenAI(base_url&#061;&#034;http:\/\/localhost:8000\/v1&#034;)<\/p>\n<p>response &#061; client.chat.completions.create(<br \/>\n    model&#061;&#034;Qwen\/Qwen2.5-7B-Instruct&#034;,<br \/>\n    messages&#061;[{&#034;role&#034;: &#034;user&#034;, &#034;content&#034;: &#034;\u89e3\u91ca\u5982\u4f55\u964d\u4f4eAI\u6a21\u578b\u6d4b\u8bd5\u6210\u672c&#034;}]<br \/>\n)<br \/>\nprint(response.choices[0].message.content)<\/p>\n<p>\u64cd\u4f5c\u5b8c\u6210\u540e&#xff0c;\u8bb0\u5f97\u5728\u63a7\u5236\u53f0\u53ca\u65f6\u91ca\u653eGPU\u8d44\u6e90\u3002<\/p>\n<h3>4. \u5173\u952e\u53c2\u6570\u4f18\u5316\u6280\u5de7<\/h3>\n<h4>4.1 GPU\u9009\u578b\u5efa\u8bae<\/h4>\n<p>\u6839\u636eQwen2.5-7B\u7684\u7279\u6027\u63a8\u8350&#xff1a;<\/p>\n<table>\n<tr>GPU\u7c7b\u578b\u663e\u5b58\u9002\u5408\u573a\u666f\u6027\u4ef7\u6bd4\u6392\u5e8f<\/tr>\n<tbody>\n<tr>\n<td>A10<\/td>\n<td>24GB<\/td>\n<td>\u6700\u4f73\u5e73\u8861\u70b9<\/td>\n<td>1<\/td>\n<\/tr>\n<tr>\n<td>T4<\/td>\n<td>16GB<\/td>\n<td>\u8f7b\u5ea6\u6d4b\u8bd5<\/td>\n<td>2<\/td>\n<\/tr>\n<tr>\n<td>A100<\/td>\n<td>40GB<\/td>\n<td>\u8d85\u9ad8\u5e76\u53d1\u9700\u6c42<\/td>\n<td>3<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h4>4.2 vLLM\u53c2\u6570\u8c03\u4f18<\/h4>\n<p># \u63a8\u8350\u542f\u52a8\u53c2\u6570\u7ec4\u5408<br \/>\n&#8211;max-num-seqs 32 \\\\       # \u63d0\u9ad8\u5e76\u53d1\u5904\u7406\u80fd\u529b<br \/>\n&#8211;enforce-eager \\\\         # \u51cf\u5c11\u5185\u5b58\u788e\u7247<br \/>\n&#8211;quantization awq \\\\      # 4bit\u91cf\u5316\u8282\u7701\u663e\u5b58<\/p>\n<h4>4.3 \u6210\u672c\u63a7\u5236\u5b9e\u8df5<\/h4>\n<li>\n<p>\u8bbe\u7f6e\u81ea\u52a8\u5173\u673a&#xff1a;\u901a\u8fc7crontab\u5b9a\u65f6\u4efb\u52a1 bash # \u6bcf\u592918:00\u81ea\u52a8\u91ca\u653e\u8d44\u6e90 0 18 * * * sudo poweroff<\/p>\n<\/li>\n<li>\n<p>\u4f7f\u7528\u76d1\u63a7\u811a\u672c&#xff1a;\u8bb0\u5f55GPU\u5b9e\u9645\u4f7f\u7528\u65f6\u957f python import time start &#061; time.time() # \u4f60\u7684\u6a21\u578b\u8c03\u7528\u4ee3\u7801 print(f&#034;GPU\u4f7f\u7528\u65f6\u957f&#xff1a;{time.time()-start:.2f}\u79d2&#034;)<\/p>\n<\/li>\n<h3>5. \u5e38\u89c1\u95ee\u9898\u89e3\u51b3\u65b9\u6848<\/h3>\n<h4>5.1 \u51b7\u542f\u52a8\u5ef6\u8fdf\u95ee\u9898<\/h4>\n<p>\u73b0\u8c61&#xff1a;\u9996\u6b21\u8bf7\u6c42\u54cd\u5e94\u6162 \u4f18\u5316\u65b9\u6848&#xff1a; &#8211; \u4f7f\u7528&#8211;disable-custom-all-reduce\u53c2\u6570 &#8211; \u4fdd\u6301\u5b9e\u4f8b\u8fd0\u884c2-3\u5206\u949f\u9884\u70ed<\/p>\n<h4>5.2 \u663e\u5b58\u4e0d\u8db3\u62a5\u9519<\/h4>\n<p>\u89e3\u51b3\u65b9\u6848&#xff1a;<\/p>\n<p># \u6dfb\u52a0\u8fd9\u4e9b\u53c2\u6570<br \/>\n&#8211;gpu-memory-utilization 0.7 \\\\<br \/>\n&#8211;swap-space 16G \\\\<br \/>\n&#8211;quantization gptq<\/p>\n<h4>5.3 \u8ba1\u8d39\u5f02\u5e38\u68c0\u67e5<\/h4>\n<p>\u5b9a\u671f\u6838\u67e5&#xff1a; &#8211; \u63a7\u5236\u53f0\u7684&#034;\u4f7f\u7528\u660e\u7ec6&#034;\u62a5\u8868 &#8211; \u8bbe\u7f6e\u6d88\u8d39\u544a\u8b66\u9608\u503c &#8211; 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