{"id":80003,"date":"2026-03-04T00:32:55","date_gmt":"2026-03-03T16:32:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/80003.html"},"modified":"2026-03-04T00:32:55","modified_gmt":"2026-03-03T16:32:55","slug":"%e5%9c%a8%e4%bd%8e%e9%85%8d%e6%9c%8d%e5%8a%a1%e5%99%a8%e4%b8%8a%e8%bf%90%e8%a1%8cglm-4-6v-flash-web%e7%9a%84%e6%80%a7%e8%83%bd%e8%b0%83%e4%bc%98%e6%8a%80%e5%b7%a7","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/80003.html","title":{"rendered":"\u5728\u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u8fd0\u884cGLM-4.6V-Flash-WEB\u7684\u6027\u80fd\u8c03\u4f18\u6280\u5de7"},"content":{"rendered":"<h2>\u5728\u4f4e\u914d\u670d\u52a1\u5668\u4e0a\u8fd0\u884c GLM-4.6V-Flash-WEB \u7684\u6027\u80fd\u8c03\u4f18\u6280\u5de7<\/h2>\n<hr \/>\n<p>\u5728\u5f53\u524d AI \u5e94\u7528\u52a0\u901f\u843d\u5730\u7684\u80cc\u666f\u4e0b&#xff0c;\u8d8a\u6765\u8d8a\u591a\u4f01\u4e1a\u5e0c\u671b\u5c06\u591a\u6a21\u6001\u80fd\u529b\u96c6\u6210\u5230\u5b9e\u9645\u4e1a\u52a1\u4e2d\u2014\u2014\u6bd4\u5982\u7535\u5546\u4e2d\u7684\u56fe\u50cf\u5185\u5bb9\u8bc6\u522b\u3001\u5ba2\u670d\u7cfb\u7edf\u7684\u56fe\u6587\u95ee\u7b54\u3001\u6559\u80b2\u9886\u57df\u7684\u89c6\u89c9\u8f85\u52a9\u8bb2\u89e3\u3002\u4f46\u73b0\u5b9e\u5f80\u5f80\u5f88\u9aa8\u611f&#xff1a;\u5927\u591a\u6570\u89c6\u89c9\u8bed\u8a00\u6a21\u578b&#xff08;VLM&#xff09;\u52a8\u8f84\u9700\u8981 A100 \u663e\u5361\u548c\u6570\u5341 GB \u663e\u5b58&#xff0c;\u90e8\u7f72\u6210\u672c\u9ad8\u3001\u8fd0\u7ef4\u590d\u6742&#xff0c;\u4e2d\u5c0f\u4f01\u4e1a\u6839\u672c\u201c\u7528\u4e0d\u8d77\u201d\u3002<\/p>\n<p>\u6709\u6ca1\u6709\u4e00\u79cd\u53ef\u80fd&#xff0c;\u4e0d\u9760\u9876\u7ea7\u786c\u4ef6&#xff0c;\u4e5f\u80fd\u8dd1\u5f97\u52a8\u9ad8\u8d28\u91cf\u7684\u591a\u6a21\u6001\u6a21\u578b&#xff1f;<\/p>\n<p>\u7b54\u6848\u662f\u80af\u5b9a\u7684\u3002\u667a\u8c31AI\u63a8\u51fa\u7684 GLM-4.6V-Flash-WEB \u6b63\u662f\u4e3a\u6b64\u800c\u751f\u3002\u5b83\u4e0d\u662f\u5b9e\u9a8c\u5ba4\u91cc\u7684\u201c\u6027\u80fd\u602a\u517d\u201d&#xff0c;\u800c\u662f\u4e13\u4e3a\u771f\u5b9e\u751f\u4ea7\u73af\u5883\u6253\u78e8\u51fa\u7684\u8f7b\u91cf\u7ea7\u9009\u624b\u2014\u2014\u80fd\u5728\u5355\u5f20 RTX 3060 \u4e0a\u5b9e\u73b0\u4e9a\u79d2\u7ea7\u54cd\u5e94&#xff0c;\u663e\u5b58\u5360\u7528\u63a7\u5236\u5728 8GB \u4ee5\u5185&#xff0c;\u771f\u6b63\u8ba9\u201c\u5e73\u6c11\u5316 AI\u201d\u6210\u4e3a\u53ef\u80fd\u3002<\/p>\n<p>\u4f46\u8fd9\u5e76\u4e0d\u610f\u5473\u7740\u968f\u4fbf\u4e00\u88c5\u5c31\u80fd\u8dd1\u5f97\u5feb\u3002\u4f4e\u914d\u73af\u5883\u4e0b\u7684\u8d44\u6e90\u4e89\u62a2\u66f4\u6fc0\u70c8&#xff0c;\u4efb\u4f55\u4e00\u70b9\u6d6a\u8d39\u90fd\u4f1a\u76f4\u63a5\u5f71\u54cd\u670d\u52a1\u7a33\u5b9a\u6027\u3002\u5982\u4f55\u6700\u5927\u5316\u69a8\u5e72\u8fd9\u5f20 GPU \u7684\u6bcf\u4e00\u5206\u7b97\u529b&#xff1f;\u672c\u6587\u5c06\u4ece\u5b9e\u6218\u89d2\u5ea6\u51fa\u53d1&#xff0c;\u62c6\u89e3 GLM-4.6V-Flash-WEB \u7684\u6838\u5fc3\u8bbe\u8ba1\u903b\u8f91&#xff0c;\u5e76\u7ed3\u5408\u90e8\u7f72\u7ecf\u9a8c\u7ed9\u51fa\u53ef\u843d\u5730\u7684\u6027\u80fd\u8c03\u4f18\u7b56\u7565\u3002<\/p>\n<hr \/>\n<h4>\u6a21\u578b\u4e3a\u4f55\u80fd\u201c\u5c0f\u800c\u5feb\u201d&#xff1f;<\/h4>\n<p>GLM-4.6V-Flash-WEB \u80fd\u5728\u4f4e\u914d\u7f6e\u8bbe\u5907\u4e0a\u6d41\u7545\u8fd0\u884c&#xff0c;\u5e76\u975e\u5076\u7136\u3002\u5b83\u7684\u201c\u8f7b\u201d\u662f\u6709\u6280\u672f\u652f\u6491\u7684\u3002<\/p>\n<p>\u9996\u5148\u770b\u67b6\u6784\u8bbe\u8ba1&#xff1a;\u5b83\u91c7\u7528\u7684\u662f\u5178\u578b\u7684\u7f16\u7801\u5668-\u89e3\u7801\u5668\u7ed3\u6784&#xff0c;\u4f46\u6bcf\u4e00\u90e8\u5206\u90fd\u7ecf\u8fc7\u6df1\u5ea6\u7626\u8eab&#xff1a;<\/p>\n<ul>\n<li>\u89c6\u89c9\u7f16\u7801\u5668 \u4f7f\u7528\u7684\u662f\u84b8\u998f\u540e\u7684\u8f7b\u91cf ViT \u6216\u5c0f\u578b CNN \u4e3b\u5e72\u7f51\u7edc&#xff0c;\u6bd4\u5982 ViT-Tiny \u8fd9\u7c7b\u53c2\u6570\u91cf\u4ec5\u767e\u4e07\u7ea7\u522b\u7684\u7ed3\u6784&#xff0c;\u56fe\u50cf\u8f93\u5165\u7ecf\u5904\u7406\u540e\u88ab\u538b\u7f29\u4e3a\u4f4e\u7ef4\u7279\u5f81\u5411\u91cf&#xff1b;<\/li>\n<li>\u8bed\u8a00\u6a21\u578b\u90e8\u5206 \u57fa\u4e8e GLM \u67b6\u6784\u8fdb\u884c\u81ea\u56de\u5f52\u751f\u6210&#xff0c;\u63a5\u6536\u56fe\u50cf\u7279\u5f81\u4e0e\u6587\u672c\u63d0\u793a\u7684\u62fc\u63a5\u8868\u793a&#xff0c;\u9010 token \u8f93\u51fa\u56de\u7b54&#xff1b;<\/li>\n<li>\u4e2d\u95f4\u901a\u8fc7\u4e00\u4e2a\u7cbe\u7b80\u7248\u7684\u8de8\u6a21\u6001\u6ce8\u610f\u529b\u6a21\u5757\u5b8c\u6210\u56fe\u6587\u5bf9\u9f50&#xff0c;\u652f\u6301\u7ec6\u7c92\u5ea6\u7406\u89e3\u4efb\u52a1&#xff0c;\u5982\u201c\u6307\u51fa\u56fe\u4e2d\u7a7f\u7ea2\u8863\u670d\u7684\u4eba\u5728\u505a\u4ec0\u4e48\u201d\u3002<\/li>\n<\/ul>\n<p>\u6574\u4e2a\u6d41\u7a0b\u8d70\u4e0b\u6765&#xff0c;\u6ca1\u6709\u5197\u4f59\u8ba1\u7b97\u8def\u5f84\u3002\u66f4\u91cd\u8981\u7684\u662f&#xff0c;\u6a21\u578b\u672c\u8eab\u7ecf\u5386\u4e86\u77e5\u8bc6\u84b8\u998f\u8bad\u7ec3\u2014\u2014\u7528\u5927\u6a21\u578b\u6307\u5bfc\u5c0f\u6a21\u578b\u5b66\u4e60\u5176\u8f93\u51fa\u5206\u5e03&#xff0c;\u5728\u4fdd\u7559\u8bed\u4e49\u7406\u89e3\u80fd\u529b\u7684\u540c\u65f6\u5927\u5e45\u538b\u7f29\u53c2\u6570\u89c4\u6a21\u3002<\/p>\n<p>\u8fd9\u5c31\u5e26\u6765\u4e86\u51e0\u4e2a\u5173\u952e\u4f18\u52bf&#xff1a;<\/p>\n<ul>\n<li>\u663e\u5b58\u5cf0\u503c\u66f4\u4f4e&#xff1a;KV Cache \u7f13\u5b58\u673a\u5236\u907f\u514d\u91cd\u590d\u8ba1\u7b97\u6ce8\u610f\u529b\u5f20\u91cf&#xff0c;\u63a8\u7406\u65f6\u663e\u5b58\u589e\u957f\u8d8b\u4e8e\u5e73\u7f13&#xff1b;<\/li>\n<li>\u63a8\u7406\u901f\u5ea6\u5feb&#xff1a;\u7b97\u5b50\u878d\u5408\u4f18\u5316\u4f7f\u5f97 GPU \u5229\u7528\u7387\u66f4\u9ad8&#xff0c;\u5355\u6b21\u524d\u5411\u4f20\u64ad\u8017\u65f6\u663e\u8457\u4e0b\u964d&#xff1b;<\/li>\n<li>\u90e8\u7f72\u95e8\u69db\u4f4e&#xff1a;FP16 \u7cbe\u5ea6\u4e0b\u6700\u4f4e\u53ea\u9700 6GB \u663e\u5b58&#xff0c;GTX 1660\u3001RTX 3050 \u8fd9\u7c7b\u6d88\u8d39\u7ea7\u663e\u5361\u5373\u53ef\u80dc\u4efb\u3002<\/li>\n<\/ul>\n<p>\u5b98\u65b9\u6570\u636e\u663e\u793a&#xff0c;\u5728\u6807\u51c6\u56fe\u6587\u95ee\u7b54\u4efb\u52a1\u4e2d&#xff0c;\u8be5\u6a21\u578b\u5e73\u5747\u5ef6\u8fdf\u63a7\u5236\u5728 500ms \u4ee5\u5185&#xff0c;\u5b8c\u5168\u6ee1\u8db3 Web \u5b9e\u65f6\u4ea4\u4e92\u9700\u6c42\u3002\u5bf9\u6bd4 LLaVA\u3001MiniGPT-4 \u7b49\u540c\u7c7b\u6a21\u578b\u52a8\u8f84\u8d85\u8fc7 1 \u79d2\u7684\u54cd\u5e94\u65f6\u95f4&#xff0c;\u8fd9\u662f\u4e00\u4e2a\u5b9e\u5b9e\u5728\u5728\u7684\u4f53\u9a8c\u8dc3\u5347\u3002<\/p>\n<table>\n<tr>\u5bf9\u6bd4\u7ef4\u5ea6GLM-4.6V-Flash-WEB\u4f20\u7edf VLM \u6a21\u578b<\/tr>\n<tbody>\n<tr>\n<td>\u663e\u5b58\u5360\u7528<\/td>\n<td>\u22648GB<\/td>\n<td>\u226516GB<\/td>\n<\/tr>\n<tr>\n<td>\u63a8\u7406\u5ef6\u8fdf<\/td>\n<td>&lt;500ms<\/td>\n<td>&gt;1s<\/td>\n<\/tr>\n<tr>\n<td>\u90e8\u7f72\u96be\u5ea6<\/td>\n<td>\u63d0\u4f9b\u4e00\u952e\u955c\u50cf<\/td>\n<td>\u9700\u624b\u52a8\u914d\u7f6e\u4f9d\u8d56<\/td>\n<\/tr>\n<tr>\n<td>\u591a\u6a21\u6001\u4efb\u52a1\u652f\u6301<\/td>\n<td>\u56fe\u6587\u95ee\u7b54\u3001\u5185\u5bb9\u7406\u89e3\u3001\u5ba1\u6838<\/td>\n<td>\u591a\u96c6\u4e2d\u4e8e\u95ee\u7b54<\/td>\n<\/tr>\n<tr>\n<td>\u5f00\u6e90\u7a0b\u5ea6<\/td>\n<td>\u5b8c\u5168\u5f00\u6e90 &#043; \u53ef\u5546\u7528<\/td>\n<td>\u90e8\u5206\u95ed\u6e90\u6216\u53d7\u9650\u8bb8\u53ef<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>\u8fd9\u79cd\u201c\u5c0f\u800c\u7cbe\u201d\u7684\u5b9a\u4f4d&#xff0c;\u8ba9\u5b83\u7279\u522b\u9002\u5408\u5d4c\u5165\u5230\u5df2\u6709\u7cfb\u7edf\u4e2d\u4f5c\u4e3a\u529f\u80fd\u6a21\u5757\u4f7f\u7528&#xff0c;\u800c\u4e0d\u662f\u4f5c\u4e3a\u4e00\u4e2a\u72ec\u7acb\u91cd\u578b\u670d\u52a1\u5b58\u5728\u3002<\/p>\n<hr \/>\n<h4>\u5355\u5361\u63a8\u7406\u662f\u600e\u4e48\u5b9e\u73b0\u7684&#xff1f;<\/h4>\n<p>\u5f88\u591a\u4eba\u95ee&#xff1a;\u201c\u4e00\u5f20 8GB \u663e\u5361\u771f\u80fd\u625b\u4f4f\u4e00\u4e2a\u591a\u6a21\u6001\u6a21\u578b&#xff1f;\u201d<br \/>\n\u7b54\u6848\u5728\u4e8e\u4e09\u9879\u6838\u5fc3\u6280\u672f\u624b\u6bb5\u7684\u534f\u540c\u4f5c\u7528\u3002<\/p>\n<h5>1. \u6a21\u578b\u8f7b\u91cf\u5316&#xff1a;\u526a\u679d &#043; \u91cf\u5316 &#043; \u84b8\u998f<\/h5>\n<p>\u539f\u59cb\u5927\u6a21\u578b\u65e0\u6cd5\u76f4\u63a5\u90e8\u7f72&#xff0c;\u5fc5\u987b\u7ecf\u8fc7\u538b\u7f29\u3002GLM-4.6V-Flash-WEB \u91c7\u7528\u4e86\u4e09\u7ea7\u538b\u7f29\u7b56\u7565&#xff1a;<\/p>\n<ul>\n<li>\u7ed3\u6784\u526a\u679d&#xff1a;\u79fb\u9664\u4e0d\u91cd\u8981\u7684\u795e\u7ecf\u5143\u8fde\u63a5&#xff0c;\u51cf\u5c11\u8ba1\u7b97\u91cf&#xff1b;<\/li>\n<li>\u77e5\u8bc6\u84b8\u998f&#xff1a;\u7528\u66f4\u5927\u7684\u6559\u5e08\u6a21\u578b\u76d1\u7763\u8bad\u7ec3\u5b66\u751f\u6a21\u578b&#xff0c;\u4fdd\u7559\u5173\u952e\u80fd\u529b&#xff1b;<\/li>\n<li>INT8 \u91cf\u5316&#xff1a;\u5c06 FP32 \u6743\u91cd\u8f6c\u6362\u4e3a INT8 \u6574\u6570\u683c\u5f0f&#xff0c;\u4f53\u79ef\u7f29\u5c0f\u4e00\u534a&#xff0c;\u8fd0\u7b97\u901f\u5ea6\u63d0\u5347\u3002<\/li>\n<\/ul>\n<p>\u8fd9\u4e09\u6b65\u4e0b\u6765&#xff0c;\u6a21\u578b\u5927\u5c0f\u53ef\u80fd\u53ea\u6709\u539f\u7248\u7684 1\/3&#xff0c;\u4f46\u6027\u80fd\u4ecd\u80fd\u4fdd\u6301\u5728\u53ef\u7528\u8303\u56f4\u5185\u3002\u5c24\u5176\u5728\u56fe\u6587\u95ee\u7b54\u8fd9\u7c7b\u4efb\u52a1\u4e2d&#xff0c;\u7528\u6237\u611f\u77e5\u4e0d\u5230\u660e\u663e\u9000\u5316\u3002<\/p>\n<h5>2. \u5185\u5b58\u4f18\u5316&#xff1a;KV Cache \u662f\u5173\u952e<\/h5>\n<p>Transformer \u7c7b\u6a21\u578b\u6700\u5927\u7684\u663e\u5b58\u6740\u624b\u662f\u4ec0\u4e48&#xff1f;\u662f\u6bcf\u6b21 decoding \u90fd\u91cd\u65b0\u8ba1\u7b97\u6240\u6709\u5386\u53f2 token \u7684 attention \u5f20\u91cf\u3002<\/p>\n<p>\u89e3\u51b3\u529e\u6cd5\u5c31\u662f KV Cache \u2014\u2014\u628a\u5df2\u7ecf\u751f\u6210 token \u7684 Key \u548c Value \u7f13\u5b58\u8d77\u6765&#xff0c;\u4e0b\u6b21\u53ea\u9700\u8981\u8ba1\u7b97\u65b0 token \u4e0e\u5386\u53f2\u72b6\u6001\u4e4b\u95f4\u7684\u6ce8\u610f\u529b&#xff0c;\u6781\u5927\u964d\u4f4e\u663e\u5b58\u5cf0\u503c\u548c\u8ba1\u7b97\u5f00\u9500\u3002<\/p>\n<p>\u8fd9\u4e00\u70b9\u5728\u6279\u5904\u7406\u573a\u666f\u4e0b\u5c24\u4e3a\u660e\u663e\u3002\u5373\u4f7f batch size \u8fbe\u5230 2&#xff0c;\u53ea\u8981\u5408\u7406\u7ba1\u7406\u7f13\u5b58\u751f\u547d\u5468\u671f&#xff0c;\u4e5f\u4e0d\u4f1a\u8f7b\u6613 OOM\u3002<\/p>\n<h5>3. \u63a8\u7406\u5f15\u64ce\u52a0\u901f&#xff1a;TensorRT \u6216 ONNX Runtime<\/h5>\n<p>PyTorch \u539f\u751f\u63a8\u7406\u867d\u7136\u65b9\u4fbf&#xff0c;\u4f46\u5728\u8fb9\u7f18\u7aef\u6548\u7387\u4e0d\u9ad8\u3002\u4e3a\u4e86\u8fdb\u4e00\u6b65\u63d0\u901f&#xff0c;\u53ef\u4ee5\u501f\u52a9 TensorRT \u6216 ONNX Runtime \u5bf9\u8ba1\u7b97\u56fe\u8fdb\u884c\u4f18\u5316&#xff1a;<\/p>\n<ul>\n<li>\u7b97\u5b50\u878d\u5408&#xff08;\u5982 LayerNorm &#043; GELU \u5408\u5e76&#xff09;<\/li>\n<li>\u5185\u5b58\u590d\u7528\u8c03\u5ea6<\/li>\n<li>\u786c\u4ef6\u7279\u5f02\u6027\u6307\u4ee4\u96c6\u8c03\u7528&#xff08;\u5982 Tensor Core&#xff09;<\/li>\n<\/ul>\n<p>\u8fd9\u4e9b\u5e95\u5c42\u4f18\u5316\u80fd\u8ba9 GPU \u5229\u7528\u7387\u4ece 60% \u63d0\u5347\u5230 85% \u4ee5\u4e0a&#xff0c;\u5c24\u5176\u5728\u8fde\u7eed\u8bf7\u6c42\u573a\u666f\u4e0b\u8868\u73b0\u7a81\u51fa\u3002<\/p>\n<p>\u5b9e\u9645\u90e8\u7f72\u65f6\u7684\u5173\u952e\u53c2\u6570\u5982\u4e0b&#xff1a;<\/p>\n<ul>\n<li>\u6700\u4f4e\u663e\u5b58\u8981\u6c42&#xff1a;6GB&#xff08;FP16 \u63a8\u7406&#xff09;<\/li>\n<li>\u652f\u6301\u6700\u4f4e GPU \u578b\u53f7&#xff1a;NVIDIA GTX 1660 \/ RTX 3050<\/li>\n<li>\u63a8\u7406\u7cbe\u5ea6\u6a21\u5f0f&#xff1a;FP16 \/ INT8&#xff08;\u53ef\u9009&#xff09;<\/li>\n<li>\u6279\u5904\u7406\u5927\u5c0f&#xff08;batch size&#xff09;&#xff1a;\u9ed8\u8ba4\u4e3a 1&#xff0c;\u6700\u5927\u652f\u6301 2&#xff08;\u89c6\u663e\u5b58\u800c\u5b9a&#xff09;<\/li>\n<\/ul>\n<p># \u793a\u4f8b&#xff1a;\u542f\u52a8 FP16 \u6a21\u5f0f\u63a8\u7406<br \/>\npython infer.py \\\\<br \/>\n    &#8211;model-path Zhipu\/GLM-4.6V-Flash-WEB \\\\<br \/>\n    &#8211;image-path .\/test.jpg \\\\<br \/>\n    &#8211;prompt &#034;\u8bf7\u63cf\u8ff0\u8fd9\u5f20\u56fe\u7247\u7684\u5185\u5bb9&#034; \\\\<br \/>\n    &#8211;precision fp16 \\\\<br \/>\n    &#8211;device cuda:0<\/p>\n<p>\u5982\u679c\u4f60\u7684\u663e\u5b58\u7d27\u5f20&#xff0c;\u8fd8\u53ef\u4ee5\u52a0\u4e0a &#8211;quantize int8 \u53c2\u6570\u542f\u7528\u91cf\u5316\u7248\u672c\u3002\u4e0d\u8fc7\u8981\u6ce8\u610f&#xff0c;\u9a71\u52a8\u9700\u652f\u6301 Tensor Core&#xff0c;\u5426\u5219\u53cd\u800c\u4f1a\u53d8\u6162\u3002<\/p>\n<p>\u53e6\u5916\u5efa\u8bae\u5173\u95ed\u4e0d\u5fc5\u8981\u7684\u540e\u53f0\u8fdb\u7a0b&#xff0c;\u5c24\u5176\u662f\u5176\u4ed6 AI \u670d\u52a1\u6216 Docker \u5bb9\u5668&#xff0c;\u9632\u6b62\u8d44\u6e90\u4e89\u62a2\u5bfc\u81f4\u63a8\u7406\u4e2d\u65ad\u3002<\/p>\n<hr \/>\n<h4>\u4e00\u952e\u811a\u672c\u771f\u7684\u201c\u96f6\u95e8\u69db\u201d\u5417&#xff1f;<\/h4>\n<p>\u5b98\u65b9\u63d0\u4f9b\u4e86\u4e00\u4e2a\u540d\u4e3a 1\u952e\u63a8\u7406.sh \u7684\u81ea\u52a8\u5316\u811a\u672c&#xff0c;\u53f7\u79f0\u201c\u4e00\u884c\u547d\u4ee4\u542f\u52a8\u670d\u52a1\u201d\u3002\u8fd9\u5bf9\u4e8e\u65b0\u624b\u5f00\u53d1\u8005\u6765\u8bf4\u6781\u5177\u5438\u5f15\u529b&#xff0c;\u4f46\u80cc\u540e\u4e5f\u6709\u4e0d\u5c11\u7ec6\u8282\u9700\u8981\u6ce8\u610f\u3002<\/p>\n<p>\u8fd9\u4e2a\u811a\u672c\u672c\u8d28\u4e0a\u662f\u4e00\u4e2a\u5c01\u88c5\u5b8c\u6574\u7684\u90e8\u7f72\u6d41\u6c34\u7ebf&#xff0c;\u4e3b\u8981\u5305\u62ec\u4ee5\u4e0b\u6b65\u9aa4&#xff1a;<\/p>\n<li>\u68c0\u67e5 Python \u4e0e CUDA \u73af\u5883\u662f\u5426\u5c31\u7eea&#xff1b;<\/li>\n<li>\u5b89\u88c5\u5fc5\u8981\u4f9d\u8d56&#xff08;transformers\u3001torch\u3001gradio \u7b49&#xff09;&#xff1b;<\/li>\n<li>\u4e0b\u8f7d\u6216\u52a0\u8f7d\u672c\u5730\u6a21\u578b\u6743\u91cd&#xff1b;<\/li>\n<li>\u542f\u52a8\u57fa\u4e8e Gradio \u7684 Web \u63a8\u7406\u754c\u9762&#xff1b;<\/li>\n<li>\u8f93\u51fa\u8bbf\u95ee\u94fe\u63a5\u4f9b\u6d4f\u89c8\u5668\u8c03\u7528\u3002<\/li>\n<p>#!\/bin\/bash<br \/>\n# 1\u952e\u63a8\u7406.sh \u5185\u5bb9\u8282\u9009<\/p>\n<p>export CUDA_VISIBLE_DEVICES&#061;0<br \/>\npip install -r requirements.txt<\/p>\n<p>python -m gradio_app \\\\<br \/>\n    &#8211;host 0.0.0.0 \\\\<br \/>\n    &#8211;port 7860 \\\\<br \/>\n    &#8211;model-path \/root\/models\/GLM-4.6V-Flash-WEB<\/p>\n<p>\u8fd0\u884c\u540e\u4f1a\u81ea\u52a8\u6253\u5f00\u4e00\u4e2a\u56fe\u5f62\u5316\u9875\u9762&#xff0c;\u652f\u6301\u62d6\u62fd\u4e0a\u4f20\u56fe\u7247\u3001\u8f93\u5165\u95ee\u9898\u5e76\u5b9e\u65f6\u67e5\u770b\u7ed3\u679c&#xff0c;\u975e\u5e38\u9002\u5408\u5feb\u901f\u9a8c\u8bc1\u548c\u8c03\u8bd5\u3002<\/p>\n<p>\u4f46\u5b83\u5e76\u4e0d\u662f\u4e07\u80fd\u7684\u3002\u6211\u4eec\u5728\u5b9e\u9645\u6d4b\u8bd5\u4e2d\u53d1\u73b0\u51e0\u4e2a\u5e38\u89c1\u5751\u70b9&#xff1a;<\/p>\n<ul>\n<li>\u9996\u6b21\u8fd0\u884c\u8017\u65f6\u8f83\u957f&#xff1a;\u5982\u679c\u6a21\u578b\u672a\u9884\u4e0b\u8f7d&#xff0c;\u811a\u672c\u4f1a\u81ea\u52a8\u62c9\u53d6\u6743\u91cd\u6587\u4ef6&#xff08;\u7ea6 4\u20136GB&#xff09;&#xff0c;\u7f51\u7edc\u5dee\u7684\u60c5\u51b5\u4e0b\u53ef\u80fd\u5361\u4f4f&#xff1b;<\/li>\n<li>\u9632\u706b\u5899\u9650\u5236&#xff1a;&#8211;host 0.0.0.0 \u8868\u793a\u76d1\u542c\u6240\u6709 IP&#xff0c;\u4f46\u670d\u52a1\u5668\u9632\u706b\u5899\u82e5\u672a\u5f00\u653e 7860 \u7aef\u53e3&#xff0c;\u5219\u5916\u90e8\u65e0\u6cd5\u8bbf\u95ee&#xff1b;<\/li>\n<li>Gradio \u4e0d\u9002\u5408\u751f\u4ea7\u73af\u5883&#xff1a;\u5176\u5185\u7f6e\u670d\u52a1\u5668\u57fa\u4e8e Flask&#xff0c;\u53ea\u9002\u7528\u4e8e\u5f00\u53d1\u8c03\u8bd5&#xff0c;\u9ad8\u5e76\u53d1\u4e0b\u5bb9\u6613\u5d29\u6e83\u3002<\/li>\n<\/ul>\n<p>\u56e0\u6b64&#xff0c;\u6211\u4eec\u5efa\u8bae\u7684\u505a\u6cd5\u662f&#xff1a;<br \/>\n&#8211; \u5f00\u53d1\u9636\u6bb5\u7528 1\u952e\u63a8\u7406.sh \u5feb\u901f\u8bd5\u9519&#xff1b;<br \/>\n&#8211; \u751f\u4ea7\u90e8\u7f72\u65f6\u66ff\u6362\u4e3a FastAPI &#043; Uvicorn &#043; Gunicorn \u7ec4\u5408&#xff0c;\u914d\u5408 Nginx \u505a\u53cd\u5411\u4ee3\u7406&#xff0c;\u63d0\u5347\u7a33\u5b9a\u6027\u548c\u541e\u5410\u80fd\u529b\u3002<\/p>\n<p>\u6b64\u5916&#xff0c;\u811a\u672c\u5185\u5bb9\u5b8c\u5168\u5f00\u653e&#xff0c;\u4f60\u53ef\u4ee5\u81ea\u7531\u4fee\u6539\u6a21\u578b\u8def\u5f84\u3001\u7aef\u53e3\u53f7\u3001\u65e5\u5fd7\u7ea7\u522b\u7b49\u53c2\u6570&#xff0c;\u7075\u6d3b\u6027\u5f88\u9ad8\u3002<\/p>\n<hr \/>\n<h4>\u5178\u578b\u90e8\u7f72\u67b6\u6784\u957f\u4ec0\u4e48\u6837&#xff1f;<\/h4>\n<p>\u5728\u4e00\u4e2a\u5178\u578b\u7684\u4f4e\u914d\u670d\u52a1\u5668\u90e8\u7f72\u573a\u666f\u4e2d&#xff0c;\u7cfb\u7edf\u67b6\u6784\u901a\u5e38\u662f\u8fd9\u6837\u7684&#xff1a;<\/p>\n<p>[\u5ba2\u6237\u7aef]<br \/>\n   \u2193 (HTTP \u8bf7\u6c42)<br \/>\n[Web \u6d4f\u89c8\u5668 \/ \u79fb\u52a8App]<br \/>\n   \u2193<br \/>\n[Nginx \u53cd\u5411\u4ee3\u7406] \u2192 [\u9632\u706b\u5899 &amp; SSL \u52a0\u5bc6]<br \/>\n   \u2193<br \/>\n[FastAPI\/Gradio \u670d\u52a1\u5c42]<br \/>\n   \u2193<br \/>\n[GLM-4.6V-Flash-WEB \u63a8\u7406\u5f15\u64ce]<br \/>\n   \u2193<br \/>\n[CUDA Runtime &#043; GPU Driver]<br \/>\n   \u2193<br \/>\n[NVIDIA GPU (e.g., RTX 3060)]<\/p>\n<p>Jupyter Notebook \u4e00\u822c\u53ea\u7528\u4e8e\u524d\u671f\u8c03\u8bd5&#xff0c;\u6b63\u5f0f\u4e0a\u7ebf\u5e94\u4f18\u5148\u8003\u8651\u5bb9\u5668\u5316\u90e8\u7f72\u3002\u4f8b\u5982\u4f7f\u7528 Docker \u5c06\u6574\u4e2a\u670d\u52a1\u6253\u5305\u6210\u955c\u50cf&#xff0c;\u4e0d\u4ec5\u4fbf\u4e8e\u8fc1\u79fb&#xff0c;\u8fd8\u80fd\u7edf\u4e00\u73af\u5883\u4f9d\u8d56\u3002<\/p>\n<p>\u5de5\u4f5c\u6d41\u7a0b\u4e5f\u5f88\u6e05\u6670&#xff1a;<\/p>\n<li>\u7528\u6237\u4e0a\u4f20\u4e00\u5f20\u5546\u54c1\u56fe\u5e76\u63d0\u95ee&#xff1a;\u201c\u8fd9\u662f\u4ec0\u4e48\u724c\u5b50\u7684\u5305&#xff1f;\u201d<\/li>\n<li>\u524d\u7aef\u5c06\u56fe\u50cf\u548c\u6587\u672c\u6253\u5305\u6210 JSON \u53d1\u9001\u5230\u540e\u7aef API&#xff1b;<\/li>\n<li>\u670d\u52a1\u7aef\u8c03\u7528 GLM-4.6V-Flash-WEB \u8fdb\u884c\u63a8\u7406&#xff1b;<\/li>\n<li>\u6a21\u578b\u8bc6\u522b\u51fa LOGO \u7279\u5f81\u5e76\u4e0e\u5185\u90e8\u77e5\u8bc6\u5e93\u5339\u914d&#xff1b;<\/li>\n<li>\u8fd4\u56de\u7ed3\u6784\u5316\u7ed3\u679c&#xff1a;\u201c\u8be5\u5305\u4e3a Gucci \u54c1\u724c&#xff0c;\u98ce\u683c\u4e3a\u7ecf\u5178\u53cc G \u56fe\u6848\u3002\u201d<\/li>\n<p>\u6574\u4e2a\u8fc7\u7a0b\u8017\u65f6\u7ea6 400\u2013600ms&#xff0c;\u7b26\u5408 Web \u5b9e\u65f6\u54cd\u5e94\u6807\u51c6\u3002<\/p>\n<p>\u66f4\u91cd\u8981\u7684\u662f&#xff0c;\u8fd9\u5957\u65b9\u6848\u89e3\u51b3\u4e86\u51e0\u4e2a\u957f\u671f\u56f0\u6270\u4e2d\u5c0f\u56e2\u961f\u7684\u5b9e\u9645\u75db\u70b9&#xff1a;<\/p>\n<ul>\n<li>\u6210\u672c\u592a\u9ad8&#xff1a;\u4f20\u7edf\u65b9\u6848\u4f9d\u8d56 A100\/H100&#xff0c;\u5355\u5361\u4ef7\u683c\u6570\u4e07\u5143&#xff1b;\u800c\u73b0\u5728\u4e00\u53f0\u5e26 RTX 3060 \u7684\u4e3b\u673a\u603b\u4ef7\u4e0d\u8fc7\u4e03\u516b\u5343&#xff1b;<\/li>\n<li>\u5ef6\u8fdf\u592a\u957f&#xff1a;\u4ee5\u524d\u6a21\u578b\u54cd\u5e94\u5e38\u8d85 1 \u79d2&#xff0c;\u7528\u6237\u4f53\u9a8c\u5dee&#xff1b;\u73b0\u5728\u505a\u5230\u4e9a\u79d2\u7ea7\u54cd\u5e94&#xff1b;<\/li>\n<li>\u96c6\u6210\u592a\u96be&#xff1a;\u5f88\u591a\u5f00\u6e90\u6a21\u578b\u7f3a\u4e4f\u5b8c\u6574\u90e8\u7f72\u5305&#xff0c;\u9700\u8981\u81ea\u5df1\u5199\u670d\u52a1\u4ee3\u7801&#xff1b;\u800c GLM-4.6V-Flash-WEB \u63d0\u4f9b\u4e86\u955c\u50cf&#043;\u811a\u672c\u4e00\u4f53\u5316\u89e3\u51b3\u65b9\u6848&#xff1b;<\/li>\n<li>\u7ef4\u62a4\u592a\u96be&#xff1a;\u975e\u4e13\u4e1a\u4eba\u5458\u96be\u4ee5\u8c03\u8bd5&#xff1b;\u901a\u8fc7 Jupyter \u548c Gradio \u63d0\u4f9b\u53ef\u89c6\u5316\u5165\u53e3&#xff0c;\u5927\u5927\u964d\u4f4e\u4e86\u4f7f\u7528\u95e8\u69db\u3002<\/li>\n<\/ul>\n<hr \/>\n<h4>\u5982\u4f55\u8fdb\u4e00\u6b65\u69a8\u5e72\u6027\u80fd&#xff1f;<\/h4>\n<p>\u5373\u4fbf\u6a21\u578b\u672c\u8eab\u5df2\u7ecf\u5f88\u8f7b&#xff0c;\u4f46\u5728\u4f4e\u914d\u73af\u5883\u4e0b&#xff0c;\u4ecd\u9700\u7cbe\u7ec6\u5316\u8c03\u4f18\u624d\u80fd\u53d1\u6325\u6700\u5927\u6548\u80fd\u3002\u4ee5\u4e0b\u662f\u6211\u4eec\u5728\u591a\u4e2a\u9879\u76ee\u4e2d\u603b\u7ed3\u51fa\u7684\u6700\u4f73\u5b9e\u8df5&#xff1a;<\/p>\n<h5>\u2705 \u4f18\u5148\u4f7f\u7528 FP16 \u63a8\u7406<\/h5>\n<p>\u9664\u975e\u4f60\u5bf9\u7cbe\u5ea6\u6709\u6781\u7aef\u8981\u6c42&#xff0c;\u5426\u5219\u4e00\u5b9a\u8981\u5f00\u542f FP16\u3002\u5b83\u4e0d\u4ec5\u80fd\u8282\u7701\u7ea6 40% \u663e\u5b58&#xff0c;\u8fd8\u80fd\u5229\u7528 GPU \u7684\u534a\u7cbe\u5ea6\u8ba1\u7b97\u5355\u5143\u52a0\u901f\u8fd0\u7b97\u3002\u5bf9\u4e8e\u7edd\u5927\u591a\u6570\u56fe\u6587\u4efb\u52a1\u800c\u8a00&#xff0c;\u8089\u773c\u51e0\u4e4e\u770b\u4e0d\u51fa\u5dee\u5f02\u3002<\/p>\n<h5>\u2705 \u63a7\u5236\u5e76\u53d1\u6570\u91cf<\/h5>\n<p>\u4e0d\u8981\u8d2a\u5fc3\u8bbe high batch size\u3002\u5728 8GB \u663e\u5b58\u4e0b&#xff0c;batch size&#061;2 \u5df2\u63a5\u8fd1\u6781\u9650\u3002\u5efa\u8bae\u8bbe\u7f6e\u6700\u5927 worker \u6570\u4e3a 2&#xff0c;\u8d85\u51fa\u5219\u6392\u961f\u6216\u62d2\u7edd&#xff0c;\u9632\u6b62 OOM \u5bfc\u81f4\u670d\u52a1\u91cd\u542f\u3002<\/p>\n<p>\u53ef\u4ee5\u7528 nvidia-smi \u5b9e\u65f6\u76d1\u63a7\u663e\u5b58\u4f7f\u7528\u60c5\u51b5&#xff1a;<\/p>\n<p>watch -n 1 nvidia-smi<\/p>\n<p>\u89c2\u5bdf\u201cMemory-Usage\u201d\u548c\u201cGPU-Util\u201d\u4e24\u9879\u6307\u6807&#xff0c;\u786e\u4fdd\u5229\u7528\u7387\u7a33\u5b9a\u4e14\u65e0\u7a81\u589e\u3002<\/p>\n<h5>\u2705 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