{"id":822,"date":"2026-03-28T19:04:26","date_gmt":"2026-03-28T11:04:26","guid":{"rendered":"https:\/\/localarch.ai\/llama-cpp-evolution-harnessing-the-latest-quantization-techniques-for-faster-inference\/"},"modified":"2026-04-07T20:30:59","modified_gmt":"2026-04-07T12:30:59","slug":"llama-cpp-evolution-harnessing-the-latest-quantization-techniques-for-faster-inference","status":"publish","type":"post","link":"https:\/\/localarch.ai\/zh-hant\/llama-cpp-evolution-harnessing-the-latest-quantization-techniques-for-faster-inference\/","title":{"rendered":"llama.cpp 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\u4e2d\u6700\u65b0\u7684\u91cf\u5316\u65b9\u6cd5\u5982\u4f55\u5e36\u4f86\u524d\u6240\u672a\u6709\u7684\u901f\u5ea6\u548c\u6548\u7387\u63d0\u5347\uff0c\u4f7f\u5f37\u5927\u7684 AI \u80fd\u5920\u5728\u6bd4\u4ee5\u5f80\u4efb\u4f55\u6642\u5019\u90fd\u591a\u7684\u8a2d\u5099\u4e0a\u4f7f\u7528\u3002 <\/p>\n<p><strong>\u70ba\u4ec0\u9ebc\u91cf\u5316\u6bd4\u4ee5\u5f80\u4efb\u4f55\u6642\u5019\u90fd\u66f4\u91cd\u8981<\/strong><\/p>\n<p>\u91cf\u5316\u2014\u2014\u5373\u964d\u4f4e\u6a21\u578b\u6b0a\u91cd\u7684\u7cbe\u5ea6\u2014\u2014\u5df2\u7d93\u5f9e\u4e00\u7a2e\u5c0f\u773e\u7684\u58d3\u7e2e\u6280\u8853\u767c\u5c55\u6210\u70ba\u672c\u5730\u4eba\u5de5\u667a\u6167\u90e8\u7f72\u7684\u95dc\u9375\u7d44\u6210\u90e8\u5206\u3002\u5176\u539f\u56e0\u65e2\u6709\u5be6\u7528\u6027\uff0c\u4e5f\u6709\u7d93\u6fdf\u6027\uff1a <\/p>\n<ol>\n<li><strong>\u786c\u9ad4\u6c11\u4e3b\u5316\uff1a<\/strong>\u900f\u904e\u91cf\u5316\uff0c\u66fe\u7d93\u9700\u8981\u6602\u8cb4\u7684\u4f3a\u670d\u5668\u7d1a GPU \u7684\u6a21\u578b\u73fe\u5728\u53ef\u4ee5\u5728\u6d88\u8cbb\u7d1a\u786c\u9ad4\u4e0a\u6d41\u66a2\u904b\u884c\uff0c\u5927\u5927\u964d\u4f4e\u4e86\u672c\u5730 AI \u7684\u51c6\u5165\u9580\u6abb\u3002<\/li>\n<li><strong>\u901f\u5ea6\u8207\u6e96\u78ba\u6027\u7684\u6b0a\u8861\uff1a<\/strong>\u73fe\u4ee3\u91cf\u5316\u6280\u8853\u5df2\u7d93\u5c07\u9019\u7a2e\u5e73\u8861\u6539\u9032\u5230\u5982\u6b64\u7a0b\u5ea6\uff0c\u4ee5\u81f3\u65bc\u5728\u8a31\u591a\u5be6\u969b\u61c9\u7528\u4e2d\uff0c4 \u4f4d\u5143\u91cf\u5316\u6a21\u578b\u901a\u5e38\u8207 16 \u4f4d\u5143\u6a21\u578b\u7684\u6027\u80fd\u5e7e\u4e4e\u5b8c\u5168\u76f8\u540c\u3002<\/li>\n<li><strong>\u8a18\u61b6\u9ad4\u6548\u7387\uff1a<\/strong>\u96a8\u8457\u6a21\u578b\u529f\u80fd\u7684\u6210\u9577\uff0c\u5176\u9ad4\u7a4d\u5448\u6307\u6578\u7d1a\u589e\u9577\u3002\u91cf\u5316\u662f\u76ee\u524d\u5728\u986f\u5b58\u6709\u9650\u7684\u88dd\u7f6e\u4e0a\u904b\u884c\u6578\u5341\u5104\u53c3\u6578\u6a21\u578b\u7684\u552f\u4e00\u53ef\u884c\u65b9\u6cd5\u3002 <\/li>\n<\/ol>\n<p>\u6700\u65b0\u7684 llama.cpp \u66f4\u65b0\u5df2\u7d93\u63a5\u53d7\u4e86\u9019\u500b\u73fe\u5be6\uff0c\u5be6\u73fe\u4e86\u6700\u5148\u9032\u7684\u91cf\u5316\u65b9\u6cd5\uff0c\u800c\u9019\u4e9b\u65b9\u6cd5\u5728\u5e7e\u500b\u6708\u524d\u9084\u53ea\u662f\u7814\u7a76\u4e3b\u984c\u3002<\/p>\n<p><strong>\u65b0\u7684\u91cf\u5316\u683c\u5c40\uff1a\u5f9eGGUF\u5230EXL2<\/strong><\/p>\n<p><strong>GGUF\u683c\u5f0f\u6f14\u8b8a<\/strong><\/p>\n<p>GGUF\uff08GPT\u751f\u6210\u7684\u7d71\u4e00\u683c\u5f0f\uff09\u6587\u4ef6\u683c\u5f0f\u65bc\u53bb\u5e74\u63a8\u51fa\uff0c\u65e8\u5728\u53d6\u4ee3GGML\uff0c\u73fe\u5df2\u6210\u70ballama.cpp\u751f\u614b\u7cfb\u7d71\u4e2d\u91cf\u5316\u6a21\u578b\u7684\u6a19\u6e96\u683c\u5f0f\u3002\u5176\u6700\u65b0\u7248\u672c\u63d0\u4f9b\u4e86\u986f\u8457\u7684\u6539\u9032\uff1a <\/p>\n<ul>\n<li><strong>\u589e\u5f37\u578b\u5143\u8cc7\u6599\uff1a<\/strong>\u66f4\u8c50\u5bcc\u7684\u6a21\u578b\u8cc7\u8a0a\u76f4\u63a5\u5d4c\u5165\u6587\u4ef6\u4e2d\uff0c\u5f9e\u800c\u5be6\u73fe\u66f4\u667a\u6167\u7684\u8f09\u5165\u6c7a\u7b56\u3002<\/li>\n<li><strong>\u9748\u6d3b\u7684\u5f35\u91cf\u5206\u914d\uff1a<\/strong>\u66f4\u597d\u5730\u652f\u63f4\u5c07\u6a21\u578b\u62c6\u5206\u5230\u4e0d\u540c\u7684\u786c\u9ad4\uff08CPU\/GPU\uff09\u4e0a<\/li>\n<li><strong>\u6539\u9032\u7684\u91cf\u5316\u985e\u578b\uff1a<\/strong>\u652f\u63f4\u66f4\u8907\u96dc\u7684\u91cf\u5316\u6f14\u7b97\u6cd5\uff0c\u4e26\u80fd\u66f4\u597d\u5730\u4fdd\u6301\u7cbe\u5ea6\u3002<\/li>\n<\/ul>\n<p><strong>\u73fe\u4ee3\u91cf\u5316\u65b9\u6cd5\u5be6\u8e10<\/strong><\/p>\n<p>llama.cpp \u751f\u614b\u7cfb\u7d71\u73fe\u5728\u652f\u63f4\u4e00\u7cfb\u5217\u8907\u96dc\u7684\u91cf\u5316\u985e\u578b\uff0c\u6bcf\u7a2e\u985e\u578b\u90fd\u6709\u5176\u7368\u7279\u7684\u512a\u52e2\uff1a<\/p>\n<p><strong>llama.cpp \u751f\u614b\u7cfb\u7d71\u73fe\u5728\u652f\u63f4\u4e00\u7cfb\u5217\u8907\u96dc\u7684\u91cf\u5316\u985e\u578b\uff0c\u6bcf\u7a2e\u985e\u578b\u90fd\u6709\u5176\u7368\u7279\u7684\u512a\u52e2\uff1a<\/strong><\/p>\n<ul>\n<li><strong>Q4_0\uff1a<\/strong>\u57fa\u672c\u7684 4 \u4f4d\u5143\u91cf\u5316\uff0c\u901f\u5ea6\u6700\u5feb\uff0c\u4f46\u7cbe\u5ea6\u640d\u5931\u660e\u986f\u3002<\/li>\n<li><strong>Q4_K_S\uff1a<\/strong>\u63a1\u7528\u5206\u584a\u7e2e\u653e\u7684\u201cK-quant\u201d\u7248\u672c\uff0c\u5728\u901f\u5ea6\u640d\u5931\u6700\u5c0f\u7684\u60c5\u6cc1\u4e0b\u63d0\u9ad8\u4e86\u7cbe\u5ea6\u3002<\/li>\n<li><strong>Q4_K_M<\/strong>\uff1a\u66f4\u9ad8\u7d1a\u7684 K-\u91cf\u5316\u6a21\u578b\uff0c\u7d93\u904e\u984d\u5916\u6700\u4f73\u5316\uff0c\u70ba 4 \u4f4d\u5143\u904b\u7b97\u63d0\u4f9b\u4e86\u6700\u4f73\u5e73\u8861\u3002<\/li>\n<\/ul>\n<p><strong>INT5 \u548c INT6 \u91cf\u5316\uff08\u6700\u4f73\u9078\u64c7\uff09\uff1a<\/strong><\/p>\n<ul>\n<li><strong>Q5_0 \/ Q5_1\uff1a<\/strong>\u6a19\u6e96 5 \u4f4d\u5143\u9078\u9805\uff0c\u901f\u5ea6\/\u7cbe\u5ea6\u5e73\u8861\u826f\u597d\u3002<\/li>\n<li><strong>Q5_K_S \/ Q5_K_M<\/strong>\uff1a\u63a1\u7528\u5206\u584a\u7e2e\u653e\u7684\u9ad8\u968e 5 \u4f4d\u5143\u7cbe\u5ea6\uff0c\u8207 FP16 \u76f8\u6bd4\uff0c\u7cbe\u5ea6\u640d\u5931\u901a\u5e38\u5c0f\u65bc 1%\u3002<\/li>\n<li><strong>Q6_K\uff1a<\/strong>6 \u4f4d\u5143\u91cf\u5316\uff0c\u7cbe\u5ea6\u63a5\u8fd1 FP16\uff0c\u9069\u7528\u65bc\u654f\u611f\u61c9\u7528<\/li>\n<\/ul>\n<p><strong>INT8 \u4ee5\u4e0a\uff08\u6700\u9ad8\u4fdd\u771f\u5ea6\uff09\uff1a<\/strong><\/p>\n<ul>\n<li><strong>Q8_0\uff1a<\/strong>8 \u4f4d\u5143\u91cf\u5316\uff0c\u5e7e\u4e4e\u6c92\u6709\u53ef\u5bdf\u89ba\u7684\u8cea\u91cf\u640d\u5931\u3002<\/li>\n<li><strong>FP16\uff1a<\/strong>\u9ad8\u7cbe\u5ea6\uff0c\u4e3b\u8981\u7528\u65bc\u53c3\u8003\u6216\u5fae\u8abf<\/li>\n<\/ul>\n<p><strong>\u82f1\u5049\u9054\u7684\u5f71\u97ff\uff1aEXL2 \u7684\u7a81\u7834<\/strong><\/p>\n<p><strong>\u8fd1\u671f\u6700\u91cd\u8981\u7684\u9032\u5c55\u4e4b\u4e00\u662f\u900f\u904e\u76f8\u5bb9\u7684\u5f8c\u7aef\u5c07EXL2 (EXL2 \u683c\u5f0f)<\/strong> \u652f\u63f4\u6574\u5408\u5230 llama.cpp \u4e2d\u3002\u9019\u7a2e\u683c\u5f0f\u7531 ExLlamaV2 \u5c08\u6848\u9996\u5275\uff0c\u5be6\u73fe\u4e86\u7279\u5225\u9ad8\u6548\u7684 4 \u4f4d\u548c 8 \u4f4d\u91cf\u5316\u65b9\u5f0f\uff1a <\/p>\n<ul>\n<li><strong>\u6df7\u5408\u7cbe\u5ea6\u5206\u584a\uff1a<\/strong>\u57fa\u65bc\u654f\u611f\u5ea6\u5206\u6790\uff0c\u6a21\u578b\u7684\u4e0d\u540c\u90e8\u5206\u4f7f\u7528\u4e0d\u540c\u7684\u7cbe\u5ea6\u7b49\u7d1a\u9032\u884c\u6700\u4f73\u5316\u3002<\/li>\n<li><strong>\u6700\u4f73\u5316\u7684 GPU \u6838\u5fc3\uff1a<\/strong>\u53ef\u6700\u5927\u9650\u5ea6\u63d0\u9ad8 NVIDIA GPU \u541e\u5410\u91cf\u7684\u786c\u9ad4\u611f\u77e5\u578b\u5be6\u73fe<\/li>\n<li><strong>\u66f4\u5feb\u7684\u8f09\u5165\u901f\u5ea6\uff1a<\/strong>\u7cbe\u7c21\u7684\u683c\u5f0f\u53ef\u6e1b\u5c11\u6a21\u578b\u521d\u59cb\u5316\u958b\u92b7<\/li>\n<\/ul>\n<p><strong>\u5be6\u7528\u6307\u5357\uff1a\u5be6\u65bd\u73fe\u4ee3\u91cf\u5316<\/strong><\/p>\n<p><strong>\u9010\u6b65\u91cf\u5316\u904e\u7a0b<\/strong><\/p>\n<p>\u5c0d\u65bc\u90a3\u4e9b\u5e0c\u671b\u91cf\u5316\u81ea\u8eab\u6a21\u578b\u7684\u4eba\u4f86\u8aaa\uff0c\u9019\u500b\u904e\u7a0b\u8b8a\u5f97\u66f4\u52a0\u5bb9\u6613\u4e86\uff1a<\/p>\n<ol>\n<li><strong>\u74b0\u5883\u8a2d\u5b9a\uff1a<\/strong><\/li>\n<\/ol>\n<table>\n<tbody>\n<tr>\n<td width=\"553\"># \u514b\u9686\u5e36\u6709\u65b0\u91cf\u5316\u652f\u6301\u7684\u6700\u65b0 llama.cpp<\/p>\n<p>git clone https:\/\/github.com\/ggerganov\/llama.cpp<\/p>\n<p>cd llama.cpp<\/p>\n<p>make clean &amp;&amp; make -j<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ol start=\"2\">\n<li><strong>\u6a21\u578b\u8f49\u63db<\/strong>\uff08\u9069\u7528\u65bc\u975eGGUF\u683c\u5f0f\uff09\uff1a<\/li>\n<\/ol>\n<table>\n<tbody>\n<tr>\n<td width=\"553\"># \u5c07 Hugging Face \u6a21\u578b\u8f49\u63db\u70ba GGUF \u683c\u5f0f<\/p>\n<p>python convert.py &#8211;outfile .\/models\/model_f16.gguf .\/input_model\/<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<ol start=\"3\">\n<li><strong>\u91cf\u5316\u57f7\u884c\uff1a<\/strong><\/li>\n<\/ol>\n<table>\n<tbody>\n<tr>\n<td width=\"553\"># \u91cf\u5316\u5230\u4e0d\u540c\u7684\u7cbe\u5ea6\u7d1a\u5225<\/p>\n<p>.\/quantize .\/models\/model_f16.gguf .\/models\/model_q4_k_m.gguf q4_k_m<\/p>\n<p>.\/quantize .\/models\/model_f16.gguf .\/models\/model_q5_k_m.gguf q5_k_m<\/p>\n<p>.\/quantize .\/models\/model_f16.gguf .\/models\/model_q8_0.gguf q8_0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>\u9078\u64c7\u5408\u9069\u7684\u91cf\u5316\u65b9\u6cd5<\/strong><\/p>\n<p>\u6700\u4f73\u91cf\u5316\u7b56\u7565\u53d6\u6c7a\u65bc\u60a8\u7684\u7279\u5b9a\u786c\u9ad4\u548c\u4f7f\u7528\u60c5\u5883\uff1a<\/p>\n<table>\n<thead>\n<tr>\n<td>\u5be6\u4f8b<\/td>\n<td>\u5efa\u8b70\u91cf\u5316<\/td>\n<td>\u9810\u671f\u5c3a\u5bf8\u6e1b\u5c0f<\/td>\n<td>\u5178\u578b\u7cbe\u5ea6\u4fdd\u6301\u7387<\/td>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>\u79fb\u52d5\/\u908a\u7de3\u90e8\u7f72<\/td>\n<td>Q4_K_S<\/td>\n<td>\u539f\u4ef6\u768475%<\/td>\n<td>\u539f\u4ef6\u768485-90%<\/td>\n<\/tr>\n<tr>\n<td>\u5e73\u8861\u7684\u684c\u9762\u4f7f\u7528<\/td>\n<td>Q5_K_M<\/td>\n<td>\u539f\u4ef6\u768465%<\/td>\n<td>\u539f\u4ef6\u768495-98%<\/td>\n<\/tr>\n<tr>\n<td>\u9ad8\u4fdd\u771f\u61c9\u7528<\/td>\n<td>Q6_K<\/td>\n<td>\u539f\u503c\u768460%<\/td>\n<td>\u539f\u4ef6\u768498-99.5%<\/td>\n<\/tr>\n<tr>\n<td>\u6700\u9ad8\u7cbe\u5ea6<\/td>\n<td>Q8_0<\/td>\n<td>\u539f\u50f9\u768450%<\/td>\n<td>99.8%\u4ee5\u4e0a\u70ba\u539f\u4ef6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><strong>\u6027\u80fd\u57fa\u6e96\uff1a\u5be6\u969b\u5f71\u97ff<\/strong><\/p>\n<p>\u6700\u8fd1\u4f7f\u7528 Llama 3 70B \u9032\u884c\u7684\u6e2c\u8a66\u8b49\u660e\u4e86\u73fe\u4ee3\u91cf\u5316\u6280\u8853\u7684\u5be6\u969b\u512a\u52e2\uff1a<\/p>\n<ul>\n<li><strong>Q4_K_M\uff1a<\/strong>\u5728 24GB \u8a18\u61b6\u9ad4\u4e0b\u4ee5\u6bcf\u79d2 25 \u500b\u4ee3\u5e63\u7684\u901f\u5ea6\u904b\u4f5c\uff08\u4e4b\u524d\u9700\u8981 48GB \u8a18\u61b6\u9ad4\uff09<\/li>\n<li><strong>Q5_K_M\uff1a<\/strong>\u5728\u7dad\u6301\u539f\u670998%\u6e96\u78ba\u7387\u7684\u540c\u6642\uff0c\u63a8\u7406\u901f\u5ea6\u63d0\u5347\u4e00\u500d<\/li>\n<li><strong>\u8a18\u61b6\u9ad4\u6548\u7387\uff1a<\/strong>700 \u5104\u53c3\u6578\u6a21\u578b\u73fe\u5728\u53ef\u4ee5\u5728\u6d88\u8cbb\u7d1a RTX 4090 \u986f\u793a\u5361\u4e0a\u904b\u884c\uff0c\u4e26\u9032\u884c\u4e86\u9069\u7576\u7684\u91cf\u5316\u3002<\/li>\n<\/ul>\n<p><strong>\u5148\u9032\u6280\u8853\u548c\u6700\u4f73\u5be6\u8e10<\/strong><\/p>\n<p><strong>\u9010\u5c64\u91cf\u5316\u9748\u654f\u5ea6<\/strong><\/p>\n<p>\u4e26\u975e\u6240\u6709\u6a21\u578b\u5c64\u90fd\u80fd\u5f9e\u6fc0\u9032\u7684\u91cf\u5316\u4e2d\u7372\u76ca\u3002\u9032\u968e\u7528\u6236\u53ef\u4ee5\u5be6\u73fe\uff1a <\/p>\n<ol>\n<li><strong>\u654f\u611f\u5ea6\u5206\u6790\uff1a<\/strong>\u78ba\u5b9a\u54ea\u4e9b\u5c64\u53ef\u4ee5\u5bb9\u5fcd\u66f4\u6fc0\u9032\u7684\u91cf\u5316<\/li>\n<li>\u6df7\u5408\u7cbe\u5ea6\u6a21\u578b\uff1a\u5c0d\u4e0d\u540c\u7684\u6a21\u578b\u90e8\u5206\u4f7f\u7528\u4e0d\u540c\u7684\u91cf\u5316\u7d1a\u5225<\/li>\n<li><strong>\u6821\u6e96\u6578\u64da\uff1a<\/strong>\u5229\u7528\u7279\u5b9a\u9818\u57df\u7684\u6821\u6e96\u6578\u64da\u96c6\u63d0\u9ad8\u91cf\u5316\u7cbe\u5ea6<\/li>\n<\/ol>\n<p><strong>\u786c\u9ad4\u611f\u77e5\u512a\u5316<\/strong><\/p>\n<p>\u4e0d\u540c\u7684\u786c\u9ad4\u53ef\u4ee5\u5f9e\u4e0d\u540c\u7684\u91cf\u5316\u7b56\u7565\u4e2d\u7372\u5f97\u512a\u52e2\uff1a<\/p>\n<ul>\n<li><strong>NVIDIA GPU\uff08RTX 40\/50 \u7cfb\u5217\uff09\uff1a<\/strong>\u63a1\u7528 4 \u4f4d\u5143\u7cbe\u5ea6\u7684 EXL2 \u683c\u5f0f\u901a\u5e38\u53ef\u63d0\u4f9b\u6700\u4f73\u541e\u5410\u91cf<\/li>\n<li><strong>\u860b\u679c\u6676\u7247\uff08M\u7cfb\u5217\uff09\uff1a<\/strong>Q5_K_M \u901a\u5e38\u80fd\u63d0\u4f9b\u6700\u4f73\u7684\u6027\u80fd\/\u7cbe\u5ea6\u5e73\u8861\u3002<\/li>\n<li><strong>\u82f1\u7279\u723e\/AMD CPU\uff1a<\/strong>Q4_K_S \u5728\u6c92\u6709\u5c08\u7528 AI \u52a0\u901f\u5668\u7684\u7cfb\u7d71\u4e0a\u53ef\u63d0\u4f9b\u6700\u9ad8\u901f\u5ea6<\/li>\n<\/ul>\n<p><strong>\u63a8\u7406\u512a\u5316\u53c3\u6578<\/strong><\/p>\n<p>\u9664\u4e86\u91cf\u5316\u4e4b\u5916\uff0cllama.cpp \u9084\u63d0\u4f9b\u4e86\u984d\u5916\u7684\u6700\u4f73\u5316\u6a19\u8a8c\uff0c\u9032\u4e00\u6b65\u589e\u5f37\u4e86\u6548\u80fd\u512a\u52e2\uff1a<\/p>\n<table>\n<tbody>\n<tr>\n<td width=\"553\"># \u4f7f\u7528\u73fe\u4ee3\u8a2d\u5b9a\u512a\u5316\u63a8\u7406\u6307\u4ee4<\/p>\n<p>.\/main -m .\/models\/llama3-8b-q5_k_m.gguf \\<\/p>\n<p>-n 512 \\<\/p>\n<p>-t 8 \\ # \u91dd\u5c0d\u60a8\u7684 CPU \u6700\u4f73\u5316\u7684\u57f7\u884c\u7dd2<\/p>\n<p>-c 4096 \\ # \u4e0a\u4e0b\u6587\u5927\u5c0f<\/p>\n<p>-b 512 \\ # \u6700\u4f73\u541e\u5410\u91cf\u7684\u6279\u6b21\u5927\u5c0f<\/p>\n<p>&#8211;mlock \\ # \u5c07\u6a21\u578b\u5132\u5b58\u5728\u8a18\u61b6\u9ad4\u4e2d<\/p>\n<p>&#8211;no-mmap \\ #\u7981\u7528\u8a18\u61b6\u9ad4\u6620\u5c04\u4ee5\u7372\u5f97\u66f4\u53ef\u9810\u6e2c\u7684\u6548\u80fd<\/p>\n<p>-ngl 99 \\ # \u8981\u5378\u8f09\u5230 GPU \u7684\u5c64\uff08\u5982\u679c\u53ef\u7528\uff09<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>&nbsp;<\/p>\n<p><strong>\u70ba\u6625\u5b63\u65b0\u8eca\u578b\u767c\u5e03\u505a\u6e96\u5099<\/strong><\/p>\n<p>llama.cpp \u4e2d\u7684\u91cf\u5316\u6539\u9032\u53ef\u8b02\u6070\u9022\u5176\u6642\uff0c\u56e0\u70ba\u5404\u5927\u4eba\u5de5\u667a\u6167\u5be6\u9a57\u5ba4\u6b63\u6e96\u5099\u767c\u5e03\u6625\u5b63\u65b0\u6a21\u578b\u3002\u4ee5\u4e0b\u662f\u6e96\u5099\u5de5\u4f5c\uff1a <\/p>\n<p><strong>\u65b0\u6a21\u578b\u7684\u9810\u671f\u8da8\u52e2<\/strong><\/p>\n<ol>\n<li><strong>\u66f4\u5927\u7684\u4e0a\u4e0b\u6587\u8996\u7a97\uff1a<\/strong>\u64da\u50b3\u5373\u5c07\u63a8\u51fa\u7684\u578b\u865f\u5c07\u652f\u63f4 128K+ \u4e0a\u4e0b\u6587\uff0c\u9019\u4f7f\u5f97\u9ad8\u6548\u7684\u91cf\u5316\u5c0d\u65bc\u8a18\u61b6\u9ad4\u7ba1\u7406\u8b8a\u5f97\u66f4\u52a0\u91cd\u8981\u3002<\/li>\n<li>\u5c08\u7528\u67b6\u69cb\uff1a\u65b0\u7684\u6a21\u578b\u7cfb\u5217\u53ef\u80fd\u9700\u8981\u66f4\u65b0\u7684\u91cf\u5316\u65b9\u6cd5\u624d\u80fd\u7372\u5f97\u6700\u4f73\u7d50\u679c\u3002<\/li>\n<li><strong>\u591a\u6a21\u614b\u80fd\u529b\uff1a<\/strong>\u8996\u89ba\u8a9e\u8a00\u6a21\u578b\u5c07\u53d7\u76ca\u65bc\u91dd\u5c0d\u4e0d\u540c\u6a21\u614b\u7d44\u4ef6\u7684\u5c08\u9580\u91cf\u5316\u7b56\u7565\u3002<\/li>\n<\/ol>\n<p><strong>\u78ba\u4fdd\u60a8\u7684\u91cf\u5316\u6d41\u7a0b\u9762\u5411\u672a\u4f86<\/strong><\/p>\n<p>\u70ba\u5373\u5c07\u63a8\u51fa\u7684\u8eca\u578b\u505a\u6e96\u5099\uff1a<\/p>\n<ol>\n<li>\u4fdd\u6301\u95dc\u6ce8\uff1a\u8acb\u7559\u610f llama.cpp GitHub \u7a0b\u5f0f\u78bc\u5eab\uff0c\u4ee5\u53d6\u5f97\u65b0\u7684\u91cf\u5316\u65b9\u6cd5\u3002<\/li>\n<li><strong>\u5efa\u7acb\u9a57\u8b49\u5957\u4ef6\uff1a<\/strong>\u5efa\u7acb\u9a57\u8b49\u5957\u4ef6\uff1a\u5efa\u7acb\u6e2c\u8a66\u6848\u4f8b\u4ee5\u9a57\u8b49\u4e0d\u540c\u6a21\u578b\u985e\u578b\u7684\u91cf\u5316\u8cea\u91cf<\/li>\n<li>\u5617\u8a66\u524d\u6cbf\u683c\u5f0f\uff1a\u5728 EXL2 \u548c\u5176\u4ed6\u65b0\u8208\u683c\u5f0f\u6210\u70ba\u6a19\u6e96\u4e4b\u524d\u9032\u884c\u6e2c\u8a66\u3002<\/li>\n<li>\u786c\u9ad4\u898f\u5283\uff1a\u8003\u616e\u5177\u5099\u589e\u5f37\u578b AI \u529f\u80fd\u7684\u4e0b\u4e00\u4ee3 GPU \u5c07\u5982\u4f55\u6539\u8b8a\u60a8\u7684\u91cf\u5316\u7b56\u7565<\/li>\n<\/ol>\n<p><strong>\u793e\u5340\u8cc7\u6e90\u548c\u5de5\u5177<\/strong><\/p>\n<ul>\n<li><strong>TheBloke on Hugging Face\uff1a<\/strong>\u6301\u7e8c\u63d0\u4f9b\u591a\u7a2e\u91cf\u5316\u683c\u5f0f\u7684\u6700\u65b0\u6a21\u578b<\/li>\n<li><strong>oobabooga \u7684\u6587\u5b57\u7522\u751f Web UI\uff1a<\/strong>\u5c07\u6700\u65b0\u7684 llama.cpp \u529f\u80fd\u6574\u5408\u5230\u4e00\u500b\u7528\u6236\u53cb\u597d\u7684\u4ecb\u9762\u4e2d<\/li>\n<li><strong>LM Studio<\/strong>:\uff1a\u5546\u696d\u89e3\u6c7a\u65b9\u6848\uff0c\u5c0d\u91cf\u5316\u6a21\u578b\u63d0\u4f9b\u5353\u8d8a\u7684\u652f\u6301<\/li>\n<\/ul>\n<p><strong>\u7d50\u8ad6\uff1a\u6548\u7387\u524d\u6cbf<\/strong><\/p>\n<p>llama.cpp \u4e2d\u91cf\u5316\u6280\u8853\u7684\u6f14\u9032\u4e0d\u50c5\u662f\u6280\u8853\u4e0a\u7684\u6700\u4f73\u5316\uff0c\u66f4\u662f\u63a8\u52d5\u672c\u5730\u4eba\u5de5\u667a\u6167\u9769\u547d\u7684\u6839\u672c\u56e0\u7d20\u3002\u9019\u4e9b\u9032\u6b65\u5728\u5927\u5e45\u964d\u4f4e\u786c\u9ad4\u9700\u6c42\u7684\u540c\u6642\uff0c\u4e5f\u80fd\u7dad\u6301\u6a21\u578b\u8cea\u91cf\uff0c\u4f7f\u5f97\u500b\u4eba\u958b\u767c\u8005\u3001\u5c0f\u578b\u4f01\u696d\u548c\u6ce8\u91cd\u96b1\u79c1\u7684\u7d44\u7e54\u4e5f\u80fd\u8f15\u9b06\u4f7f\u7528\u6700\u5148\u9032\u7684\u4eba\u5de5\u667a\u6167\u6280\u8853\u3002 <\/p>\n<p>\u96a8\u8457\u6625\u5b63\u65b0\u6a5f\u578b\u7684\u767c\u5e03\uff0c\u90a3\u4e9b\u638c\u63e1\u4e86\u73fe\u4ee3\u91cf\u5316\u6280\u8853\u7684\u7528\u6236\u5c07\u80fd\u5920\u7acb\u5373\u5229\u7528\u9019\u4e9b\u65b0\u529f\u80fd\u3002\u66f4\u9ad8\u6548\u7684\u6a21\u578b\u8207\u66f4\u5148\u9032\u7684\u91cf\u5316\u6280\u8853\u7684\u7d50\u5408\u5f62\u6210\u4e86\u4e00\u500b\u826f\u6027\u5faa\u74b0\uff0c\u4e0d\u65b7\u63a8\u52d5\u672c\u5730\u786c\u9ad4\u7684\u6027\u80fd\u63d0\u5347\u3002 <\/p>\n<p>\u8a0a\u606f\u5f88\u660e\u78ba\uff1a\u6a21\u578b\u539f\u59cb\u898f\u6a21\u4e0d\u518d\u662f\u6c7a\u5b9a\u5176\u80fd\u529b\u7684\u4e3b\u8981\u56e0\u7d20\u3002\u900f\u904e\u667a\u6167\u91cf\u5316\uff0c\u6211\u5011\u73fe\u5728\u53ef\u4ee5\u7528\u66f4\u5c11\u7684\u8cc7\u6e90\u5b8c\u6210\u66f4\u591a\u7684\u5de5\u4f5c\u2014\u2014\u5728\u914d\u7f6e\u8d8a\u4f86\u8d8a\u4f4e\u7684\u786c\u9ad4\u4e0a\u904b\u884c\u8907\u96dc\u7684AI\uff0c\u540c\u6642\u4fdd\u6301\u6578\u64da\u4e3b\u6b0a\u548c\u6210\u672c\u63a7\u5236\uff0c\u800c\u9019\u4e9b\u6b63\u662f\u672c\u5730AI\u5982\u6b64\u5f15\u4eba\u6ce8\u76ee\u7684\u539f\u56e0\u3002 <\/p>\n<p><a href=\"https:\/\/localarch.ai\/zh-hant\/\"><em>LocalArch.ai<\/em><\/a><em> \u5354\u52a9\u4f01\u696d\u904b\u7528\u6700\u65b0\u7684\u9ad8\u6548\u80fd\u6280\u8853\u90e8\u7f72\u6700\u4f73\u5316\u7684\u672c\u5730 AI 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