{"id":2909,"date":"2026-03-08T16:04:31","date_gmt":"2026-03-08T08:04:31","guid":{"rendered":"https:\/\/www.starverse-ai.com\/guide\/archives\/2909"},"modified":"2026-03-08T16:04:31","modified_gmt":"2026-03-08T08:04:31","slug":"rtx-4090%e4%b8%80%e5%8d%a1%e9%9a%be%e6%b1%82%ef%bc%9f%e6%98%9f%e5%ae%87%e6%99%ba%e7%ae%97%e3%80%8cgpu%e4%ba%91%e4%b8%bb%e6%9c%ba%e3%80%8d%e4%b8%80%e9%94%ae%e8%b7%91%e9%80%9astable-diffusion-3-5","status":"publish","type":"post","link":"https:\/\/www.starverse-ai.com\/guide\/archives\/2909","title":{"rendered":"RTX 4090\u4e00\u5361\u96be\u6c42\uff1f\u661f\u5b87\u667a\u7b97\u300cGPU\u4e91\u4e3b\u673a\u300d\u4e00\u952e\u8dd1\u901aStable Diffusion 3.5\uff0c\u5b9e\u6d4b\u51fa\u56fe\u901f\u5ea6\u63d0\u5347400%"},"content":{"rendered":"
\"RTX<\/figure>\n
\n

\u201c\u4e00\u5f201024\u00d71024\u7684\u56fe\uff0c50\u6b65\u91c7\u6837\uff0c\u672c\u5730RTX 3080 10G\u8dd1\u5230\u7b2c37\u6b65\u76f4\u63a5\u7206\u663e\u5b58\uff0cStable Diffusion 3.5\u5f00\u6e90\u5373\u529d\u9000\u3002\u201d
\n\u2014\u2014\u5fae\u535a\u70ed\u641c#SD35\u672c\u5730\u8dd1\u4e0d\u52a8#\u9ad8\u8d5e\u8bc4\u8bba<\/p>\n<\/blockquote>\n

\u4e00\u3001\u70ed\u70b9\u56de\u987e\uff1a\u5f00\u6e90SD 3.5\u53d1\u5e03\uff0c\u672c\u57308G\u663e\u5b58\u76f4\u63a5\u7206\u6389<\/h2>\n

10\u6708\u5e95\uff0cStability AI \u628a\u53f7\u79f0\u201c\u7ec6\u8282\u72c2\u9b54\u201d\u7684 Stable Diffusion 3.5 \u5f00\u6e90\uff0c\u53c2\u6570\u91cf 80 \u4ebf\uff0cFP16 \u6743\u91cd\u5355\u5361\u5c31\u8981 15.8 GB\u3002\u6d88\u606f\u4e00\u51fa\uff0cA \u7ad9\u3001\u63a8\u7279\u3001\u5373\u523b\u5168\u662f\u6652\u201c\u7206\u663e\u5b58\u201d\u84dd\u5c4f\u56fe\uff1a8G \u5361\u5168\u519b\u8986\u6ca1\uff0c12G \u5361\u52c9\u5f3a\u5728 512\u00d7512 \u4e0a\u5598\u6c14\uff0c\u60f3\u4e0a 2K \u56fe\uff1fRTX 4090 \u4ee5\u4e0b\u514d\u8c08\u3002\u4e8e\u662f\uff0c#4090\u4e00\u5361\u96be\u6c42#\u518d\u6b21\u51b2\u4e0a\u70ed\u641c\uff0c\u73b0\u8d27\u4ef7\u4ece\u539f\u4ef7 12999 \u5143\u88ab\u7092\u5230 18000 \u5143\uff0c\u4ecd\u6709\u4ef7\u65e0\u5e02\u3002<\/p>\n

\u4e8c\u3001\u75db\u70b9\u62c6\u89e3\uff1a\u81ea\u8d2d 4090 \u6ea2\u4ef7+\u65ad\u7535\u98ce\u9669\uff0c\u4f20\u7edf\u4e91\u5382\u5546\u6309\u5c0f\u65f6\u6536\u8d39\u9ad8\u6602<\/h2>\n
    \n
  1. \u6ea2\u4ef7\uff1a18000 \u5143\u53ea\u662f\u5165\u573a\u5238\uff0c\u914d\u9f50\u6574\u673a\u903c\u8fd1 3 \u4e07\uff0c\u8d28\u4fdd\u4ec5\u4e09\u5e74\u3002 <\/li>\n
  2. \u65ad\u7535\uff1a\u5bbf\u820d\u3001\u6c11\u7528\u7535\u8df3\u95f8\u4e00\u6b21\uff0c\u8bad\u7ec3 3 \u5929\u7684 LoRA \u76f4\u63a5\u5f52\u96f6\u3002 <\/li>\n
  3. \u4f20\u7edf\u4e91\uff1a\u5927\u578b\u516c\u6709\u4e91\u867d\u7136\u80fd\u79df\u5230 A100\uff0c\u4f46\u6309\u5c0f\u65f6\u8ba1\u8d39\u52a8\u8f84 3~5 \u5143\/\u5361\uff0cStable Diffusion \u8fd9\u79cd\u4ea4\u4e92\u5f0f\u521b\u4f5c\u4e00\u73a9\u5c31\u662f\u4e00\u4e0b\u5348\uff0c\u94b1\u5305\u625b\u4e0d\u4f4f\u3002 <\/li>\n
  4. \u8fd0\u7ef4\uff1aCUDA\u3001PyTorch\u3001xFormers\u3001\u6a21\u578b\u6743\u91cd\uff0c\u7248\u672c\u9519\u4e00\u4f4d\u5c31\u62a5\u9519\uff0c\u529d\u9000\u8bbe\u8ba1\u5e08\u3002 <\/li>\n<\/ol>\n

    \u4e09\u3001\u5b9e\u6d4b\u5bf9\u6bd4\uff1a\u540c\u6837 1024\u00d71024 50 \u6b65\uff0c\u672c\u5730 3080 vs \u661f\u5b87 RTX 4090 GPU\u670d\u52a1\u5668\u79df\u7528<\/h2>\n\n\n\n\n\n\n\n
    \u73af\u5883<\/th>\n\u673a\u578b<\/th>\n\u663e\u5b58\u5360\u7528<\/th>\n\u51fa\u56fe\u65f6\u95f4<\/th>\n\u5355\u5f20\u6210\u672c<\/th>\n<\/tr>\n<\/thead>\n
    \u672c\u5730<\/td>\ni7-12700+RTX 3080 10G<\/td>\n9.8G\uff08\u7206\u663e\u5b58\uff09<\/td>\n\u5931\u8d25<\/td>\n\u2014<\/td>\n<\/tr>\n
    \u672c\u5730<\/td>\ni9-13900+RTX 4090 24G<\/td>\n15.1G<\/td>\n58 \u79d2<\/td>\n3.2 \u5143\/\u5f20\uff08\u6309\u6574\u673a 3 \u4e07\u5143\u00f73 \u5e74\u00f7\u6bcf\u5929 100 \u5f20\uff09<\/td>\n<\/tr>\n
    \u661f\u5b87\u667a\u7b97<\/td>\nGPU\u4e91\u4e3b\u673a<\/a> RTX 4090 24G<\/td>\n15.1G<\/td>\n11 \u79d2<\/td>\n0.12 \u5143\/\u5f20\uff080.8 \u5143\/\u5361\u00b7\u65f6\u00f73600\u00d711\uff09<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u5b9e\u6d4b\u7ed3\u8bba\uff1a\u4e91\u7aef GPU\u670d\u52a1\u5668\u79df\u7528<\/a> \u51fa\u56fe\u901f\u5ea6\u63d0\u5347 400%<\/strong>\uff0c\u5355\u5f20\u6210\u672c\u4ec5\u4e3a\u672c\u5730\u81ea\u8d2d\u7684 1\/27<\/strong>\u3002<\/p>\n

    \u56db\u3001\u4e00\u952e\u5373\u73a9\uff1a\u5e73\u53f0\u5185\u7f6e SD 3.5 \u955c\u50cf\uff0c3 \u5206\u949f\u542f\u52a8\uff0c\u514d\u88c5\u9a71\u52a8\u3001\u514d\u8c03 CUDA<\/h2>\n

    \u6253\u5f00 \u661f\u5b87\u667a\u7b97<\/a> \u63a7\u5236\u53f0 \u2192 \u9009\u62e9\u300cAI \u5e94\u7528\u300d\u2192 \u52fe\u9009\u300cStable Diffusion 3.5 \u5b98\u65b9\u7248\u300d\u955c\u50cf \u2192 \u542f\u52a8 RTX 4090 \u5b9e\u4f8b\uff0c\u5168\u7a0b 180 \u79d2\u3002\u7cfb\u7edf\u5df2\u9884\u88c5\uff1a
    \n– Ubuntu 22.04 + NVIDIA Driver 535
    \n– CUDA 12.2 + cuDNN 8.9
    \n– PyTorch 2.1 + xFormers 0.0.22
    \n– \u6a21\u578b\u6743\u91cd\u5df2\u7f13\u5b58\u81f3\u672c\u5730\u9ad8\u901f NVMe\uff0c\u65e0\u9700\u518d\u6b21\u4e0b\u8f7d\u3002
    \n\u6d4f\u89c8\u5668\u81ea\u52a8\u5f39\u51fa WebUI \u5730\u5740\uff0c\u8f93\u5165\u63d0\u793a\u8bcd\u5373\u523b\u51fa\u56fe\uff0c\u8bbe\u8ba1\u5e08\u7ec8\u4e8e\u544a\u522b\u201c\u914d\u73af\u5883 2 \u5c0f\u65f6\uff0c\u8dd1\u56fe 5 \u5206\u949f\u201d\u7684\u9b54\u5492\u3002<\/p>\n

    \u4e94\u3001\u6210\u672c\u8d26\u5355\uff1a\u6309\u9700\u4f4e\u81f3 0.8 \u5143\/\u5361\u00b7\u65f6\uff0c\u6708\u79df\u6bd4\u81ea\u638f\u7535\u8d39\u8fd8\u4fbf\u5b9c<\/h2>\n