{"id":1037,"date":"2026-06-28T00:00:53","date_gmt":"2026-06-27T16:00:53","guid":{"rendered":"https:\/\/localarch.ai\/the-rise-of-hybrid-cpu-gpu-inference-when-and-how-to-offload-layers-effectively\/"},"modified":"2026-07-09T12:10:43","modified_gmt":"2026-07-09T04:10:43","slug":"the-rise-of-hybrid-cpu-gpu-inference-when-and-how-to-offload-layers-effectively","status":"publish","type":"post","link":"https:\/\/localarch.ai\/zh-hant\/the-rise-of-hybrid-cpu-gpu-inference-when-and-how-to-offload-layers-effectively\/","title":{"rendered":"\u6df7\u5408 CPU-GPU \u63a8\u7406\u7684\u5d1b\u8d77\uff1a\u4f55\u6642\u4ee5\u53ca\u5982\u4f55\u6709\u6548\u5378\u8f09\u5c64"},"content":{"rendered":"

\u6df7\u5408 CPU-GPU <\/strong>\u63a8\u7406\u7684\u5d1b\u8d77\uff1a\u4f55\u6642\u4ee5\u53ca\u5982\u4f55\u6709\u6548\u5378\u8f09\u5c64<\/strong><\/h2>\n

\u5730\u65b9\u4eba\u5de5\u667a\u6167\u7684\u6548\u7387\u524d\u6cbf<\/strong><\/h2>\n

\u96a8\u8457\u672c\u5730 AI \u6a21\u578b\u7684\u80fd\u529b\u63d0\u5347\uff0c\u5b83\u5011\u7684\u9ad4\u7a4d\u4e5f\u96a8\u4e4b\u589e\u5927\uff0c\u5f80\u5f80\u751a\u81f3\u6703\u4f7f\u9ad8\u968e\u6d88\u8cbb\u7d1a GPU \u7684\u8a18\u61b6\u9ad4\u627f\u53d7\u58d3\u529b\u3002\u66fe\u7d93\u7c21\u55ae\u7684\u554f\u984c\uff1a\u201c\u6211\u7684 GPU \u80fd\u904b\u884c\u9019\u500b\u6a21\u578b\u55ce\uff1f\u201d\u73fe\u5df2\u6f14\u8b8a\u70ba\u66f4\u70ba\u7d30\u7dfb\u7684\u512a\u5316\u6311\u6230\u3002\u7b54\u6848\u8d8a\u4f86\u8d8a\u591a\u5730\u4e0d\u518d\u662f\u9078\u64c7 CPU \u9084\u662f GPU\uff0c\u800c\u662f\u5728\u65bc\u7b56\u7565\u6027\u5730\u540c\u6642\u5229\u7528\u5169\u8005<\/strong>\u3002\u6df7\u5408 CPU-GPU \u63a8\u7406\u2013\u5c07\u6a21\u578b\u5de7\u5999\u5730\u5206\u914d\u5728 GPU \u7684\u9ad8\u901f\u904b\u7b97\u80fd\u529b\u4ee5\u53ca CPU \u548c RAM \u7684\u8c50\u5bcc\u8a18\u61b6\u9ad4\u4e4b\u9593\u2013\u5df2\u6210\u70ba\u5728\u6709\u9650\u786c\u9ad4\u4e0a\u9ad8\u6548\u57f7\u884c\u5927\u578b\u6a21\u578b\u7684\u95dc\u9375\u6280\u8853\u3002\u9019\u7a2e\u65b9\u6cd5\u4ee3\u8868\u4e86\u65b0\u7684\u6548\u7387\u524d\u6cbf\uff0c\u5c07\u66fe\u88ab\u8a8d\u70ba\u6027\u80fd\u4e0d\u8db3\u7684\u6a5f\u5668\u8f49\u8b8a\u70ba\u80fd\u52dd\u4efb\u7684 AI \u5de5\u4f5c\u7ad9\uff0c\u4e26\u4f5c\u70ba\u901a\u5411\u53ef\u64f4\u5c55\u3001\u5177\u6210\u672c\u6548\u76ca\u7684\u672c\u5730\u90e8\u7f72\u7684\u91cd\u8981\u6a4b\u6a11\u3002<\/p>\n

\u70ba\u4ec0\u9ebc\u9078\u64c7\u6df7\u5408\u63a8\u8ad6\uff1f\u8d85\u8d8a GPU <\/strong>\u8a18\u61b6\u9ad4\u7246<\/strong><\/h2>\n

\u6df7\u5408\u63a8\u7406\u7684\u4e3b\u8981\u9a45\u52d5\u56e0\u7d20\u662f GPU <\/strong>\u8a18\u61b6\u9ad4\u5c4f\u969c<\/strong>\u3002\u6a21\u578b\u5fc5\u9808\u9069\u5408\u5728\u986f\u793a\u5361\u7684 VRAM \u4e2d\u904b\u884c\uff0c\u624d\u80fd\u5b8c\u5168\u5728 GPU \u4e0a\u904b\u884c\uff0c\u9019\u6a23\u53ef\u4ee5\u63d0\u4f9b\u6700\u5feb\u7684\u63a8\u7406\u901f\u5ea6\u3002\u7576\u6a21\u578b\u8d85\u51fa\u6b64\u9650\u5236\u6642\uff0c\u50b3\u7d71\u89e3\u6c7a\u65b9\u6848\u662f\u5b8c\u5168\u5728 CPU \u4e0a\u904b\u884c\uff0c\u5118\u7ba1 CPU \u64c1\u6709\u66f4\u591a\u7684\u7cfb\u7d71 RAM\uff0c\u4f46\u5176\u904b\u7b97\u901f\u5ea6\u6162\u4e86\u6578\u500b\u6578\u91cf\u7d1a\uff0c\u5c0e\u81f4\u5ef6\u9072\u7121\u6cd5\u4f7f\u7528\u3002<\/p>\n

\u6df7\u5408\u63a8\u7406\u512a\u96c5\u5730\u89e3\u6c7a\u4e86\u9019\u4e00\u96e3\u984c\u3002\u900f\u904e\u5c07\u6a21\u578b\u4e2d\u8a08\u7b97\u6700\u5bc6\u96c6\u7684\u5c64\u52a0\u8f09\u5230 GPU\uff0c\u800c\u5c07\u5176\u9918\u90e8\u5206\u4fdd\u5b58\u5728\u7cfb\u7d71 RAM \u7531 CPU \u8655\u7406\uff0c\u4f60\u53ef\u4ee5\u904b\u884c\u6bd4 VRAM \u901a\u5e38\u5141\u8a31\u7684\u66f4\u5927\u7684\u6a21\u578b\uff0c\u540c\u6642\u4fdd\u7559 GPU \u7684\u5927\u90e8\u5206\u901f\u5ea6\u512a\u52e2\u3002<\/p>\n

\u6838\u5fc3\u985e\u6bd4\uff1a<\/strong>\u628a\u5b83\u60f3\u50cf\u6210\u4e00\u500b\u5c08\u696d\u5eda\u623f\u3002GPU \u662f\u4f60\u7684\u8d85\u7d1a\u660e\u661f\u4e3b\u5eda\uff08\u5728\u7279\u5b9a\u4efb\u52d9\u4e0a\u6975\u5feb\uff0c\u4f46\u5de5\u4f5c\u7a7a\u9593\u6709\u9650\u2013VRAM\uff09\u3002CPU \u5247\u662f\u4e00\u7fa4\u52a9\u7406\u5eda\u5e2b\uff08\u500b\u5225\u8f03\u6162\uff0c\u4f46\u53ef\u4ee5\u4f7f\u7528\u6574\u500b\u98df\u54c1\u5132\u85cf\u5ba4\u2013\u7cfb\u7d71 RAM\uff09\u3002\u6df7\u5408\u63a8\u7406\u5c31\u50cf\u5eda\u623f\u7d93\u7406\u7b56\u7565\u6027\u5730\u6c7a\u5b9a\u54ea\u4e9b\u4efb\u52d9\uff08\u6a21\u578b\u5c64\uff09\u7531\u660e\u661f\u4e3b\u5eda\u8655\u7406\uff0c\u54ea\u4e9b\u4ea4\u7d66\u52a9\u7406\u5718\u968a\uff0c\u4ee5\u6700\u5927\u5316\u6574\u9ad4\u83dc\u54c1\u7522\u51fa\u3002<\/p>\n

\u7b56\u7565\u6027\u7684\u300c\u4f55\u6642\u300d\uff1a\u6df7\u5408\u5378\u8f09\u60c5\u5883<\/strong><\/h2>\n

\u6df7\u5408\u63a8\u7406\u4e26\u4e0d\u7e3d\u662f\u6700\u4f73\u9078\u64c7\u3002\u4f7f\u7528\u6b64\u6c7a\u7b56\u6846\u67b6\u4f86\u5224\u65b7\u4f55\u6642\u5b83\u80fd\u63d0\u4f9b\u6700\u5927\u7684\u512a\u52e2\u3002<\/p>\n\n\n\n\n\n\n\n\n\n
\u60c5\u5883<\/td>\n\u5efa\u8b70\u7684\u65b9\u6cd5<\/td>\n\u57fa\u672c\u539f\u7406<\/td>\n<\/tr>\n<\/thead>\n
\u6a21\u578b\u5927\u5c0f > <\/strong>\u986f\u793a\u8a18\u61b6\u9ad4<\/strong><\/td>\n\u6df7\u5408\u63a8\u7406<\/strong><\/td>\n\u5178\u578b\u7684\u4f7f\u7528\u6848\u4f8b\u3002\u76f8\u8f03\u65bc\u5b8c\u5168\u4f7f\u7528 CPU\uff0c\u5728\u53ef\u7ba1\u7406\u7684\u6027\u80fd\u640d\u8017\u4e0b\uff0c\u80fd\u5920\u904b\u884c\u6bd4\u4f60\u7684 VRAM \u5927 20-50% \u7684\u6a21\u578b\u3002<\/td>\n<\/tr>\n
\u512a\u5148\u8003\u616e\u541e\u5410\u91cf<\/strong><\/td>\n\u5b8c\u6574 GPU <\/strong>\u63a8\u8ad6<\/strong><\/td>\n\u5982\u679c\u4f60\u7684\u6a21\u578b\u53ef\u4ee5\u5b8c\u5168\u653e\u5165 VRAM\uff0c\u5c31\u4fdd\u6301\u5728\u90a3\u88e1\u3002\u9019\u6a23\u53ef\u4ee5\u6700\u5927\u5316\u6bcf\u79d2\u8655\u7406\u7684\u8a5e\u5143\u6578\u3002<\/td>\n<\/tr>\n
\u6700\u5927\u5316\u6a21\u578b\u5c3a\u5bf8<\/strong><\/td>\n\u5168 CPU <\/strong>\u63a8\u8ad6<\/strong><\/td>\n\u70ba\u4e86\u5728 VRAM \u975e\u5e38\u6709\u9650\u4f46 RAM \u5145\u8db3\u7684\u7cfb\u7d71\u4e0a\u904b\u884c\u53ef\u80fd\u7684\u6700\u5927\u6a21\u578b\uff0c\u5118\u7ba1\u901f\u5ea6\u5f88\u6162\u3002<\/td>\n<\/tr>\n
\u670d\u52d9\u591a\u500b\u6a21\u578b<\/strong><\/td>\n\u6df7\u5408 + <\/strong>\u5168 GPU<\/strong><\/td>\n\u5c0d\u65bc\u5927\u578b\u3001\u4f7f\u7528\u983b\u7387\u8f03\u4f4e\u7684\u6a21\u578b\u4f7f\u7528\u6df7\u5408\u6a21\u5f0f\uff0c\u540c\u6642\u4fdd\u6301\u5c0f\u578b\u3001\u9ad8\u9700\u6c42\u7684\u6a21\u578b\u5b8c\u5168\u52a0\u8f09\u5728 GPU \u4e0a\u3002<\/td>\n<\/tr>\n
\u80fd\u91cf\/<\/strong>\u71b1\u91cf\u53d7\u9650<\/strong><\/td>\n\u4fdd\u5b88\u6df7\u5408<\/strong><\/td>\n\u5c07\u66f4\u591a\u5c64\u5378\u8f09\u5230 CPU\uff0c\u4ee5\u964d\u4f4e GPU \u4f7f\u7528\u7387\u548c\u529f\u8017\uff0c\u72a7\u7272\u4e00\u4e9b\u901f\u5ea6\u4f86\u63db\u53d6\u6548\u7387\u3002<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u95dc\u9375\u6307\u6a19\uff1a<\/strong>\u5982\u679c\u5728\u4f7f\u7528 Ollama \u6216 llama.cpp \u7b49\u5de5\u5177\u5617\u8a66\u8f09\u5165\u6a21\u578b\u6642\u9047\u5230\u300c\u8a18\u61b6\u9ad4\u4e0d\u8db3\u300d\u932f\u8aa4\uff0c\u6df7\u5408\u5378\u8f09\u662f\u4f60\u4e0b\u4e00\u500b\u5408\u4e4e\u908f\u8f2f\u7684\u6b65\u9a5f\u3002<\/p>\n

\u5be6\u7528\u300c\u5982\u4f55\u300d\uff1a\u5be6\u65bd\u5c64\u5378\u8f09<\/strong><\/h2>\n

\u503c\u5f97\u6176\u5e78\u7684\u662f\uff0c\u9019\u500b\u5be6\u4f5c\u6b63\u8b8a\u5f97\u66f4\u52a0\u5bb9\u6613\u7372\u53d6\uff0c\u4e3b\u8981\u662f\u901a\u904e\u4e00\u500b\u53c3\u6578\u4f86\u7ba1\u7406\uff1angl\uff08GPU \u5c64\u6578\uff09\u6216\u5728\u5404\u7a2e\u5de5\u5177\u4e2d\u7684\u7b49\u6548\u53c3\u6578\u3002<\/p>\n

    \n
  1. \u4f7f\u7528 Ollama<\/strong>\uff1a\u6700\u7c21\u55ae\u7684\u65b9\u6cd5<\/strong><\/li>\n<\/ol>\n

    Ollama \u5167\u5efa\u652f\u6301\u5c64\u5378\u8f09\u3002\u5728\u904b\u884c\u6216\u5275\u5efa\u6a21\u578b\u6642\uff0c\u4f60\u53ef\u4ee5\u6307\u5b9a\u5c07\u5176\u4e2d\u591a\u5c11\u5c64\u653e\u7f6e\u5728 GPU \u4e0a\u3002<\/p>\n\n\n\n
    # Run a model, offloading 40 of its layers to the GPU<\/p>\n

    ollama run llama3.1:8b –num-gpu-layers 40<\/p>\n

     <\/p>\n

    # To make this permanent, create a Modelfile<\/p>\n

    FROM llama3.1:8b<\/p>\n

    PARAMETER num_gpu_layers 40<\/p>\n

    PARAMETER num_thread 8 # Can also optimize CPU threads<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u00a0<\/strong><\/p>\n

    \u5982\u4f55\u627e\u5230\u5408\u9069\u7684\u6578\u5b57\uff1f<\/strong>\u5f9e\u4e00\u500b\u8f03\u5927\u7684\u6578\u5b57\u958b\u59cb\uff08\u4f8b\u5982 50\uff09\u3002\u5982\u679c\u51fa\u73fe VRAM \u932f\u8aa4\uff0c\u5247\u9010\u6b65\u6e1b\u5c0f\u6578\u5b57\u3002\u50cf nvtop\uff08Linux\uff09\u6216\u4efb\u52d9\u7ba1\u7406\u5668\u7684\u6548\u80fd\u6a19\u7c64\uff08Windows 11\uff09\u7b49\u5de5\u5177\u53ef\u4ee5\u5e6b\u52a9\u4f60\u5be6\u6642\u76e3\u63a7 VRAM \u4f7f\u7528\u60c5\u6cc1\u3002<\/p>\n

      \n
    1. \u4f7f\u7528 llama.cpp<\/strong>\uff1a\u7d30\u7dfb\u63a7\u5236<\/strong><\/li>\n<\/ol>\n

      llama.cpp \u7684\u4e3b\u8981\u4e8c\u9032\u4f4d\u6a94\u900f\u904e -ngl \u65d7\u6a19\u63d0\u4f9b\u7cbe\u78ba\u63a7\u5236\uff0c\u4e26\u4e14\u975e\u5e38\u9069\u5408\u505a\u57fa\u6e96\u6e2c\u8a66\u3002<\/p>\n\n\n\n
      # Benchmark a model with different offload strategies<\/p>\n

      .\/main -m mixtral-8x7b.Q5_K_M.gguf -n 512 -ngl 0 –prompt “Hello” # CPU only<\/p>\n

      .\/main -m mixtral-8x7b.Q5_K_M.gguf -n 512 -ngl 20 –prompt “Hello” # 20 layers on GPU<\/p>\n

      .\/main -m mixtral-8x7b.Q5_K_M.gguf -n 512 -ngl 40 –prompt “Hello” # 40 layers on GPU<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u00a0<\/strong><\/p>\n

        \n
      1. \u9032\u968e\u8abf\u6821\uff1a\u5c0b\u627e\u6700\u4f73\u9ede<\/strong><\/li>\n<\/ol>\n

        \u76ee\u6a19\u662f\u627e\u5230\u66f2\u7dda\u4e2d\u7684\u6027\u80fd\u300c\u819d\u9ede\u300d\u2013\u5373\u6dfb\u52a0\u66f4\u591a GPU \u5c64\u6703\u7522\u751f\u905e\u6e1b\u6548\u76ca\u3002<\/p>\n

          \n
        1. \u7cfb\u7d71\u6027\u5206\u6790\uff1a<\/strong>\u6e2c\u91cf\u4e0d\u540c ngl \u503c\uff08\u4f8b\u5982 0\u300110\u300120\u300130\u300140\uff09\u4e0b\u7684\u6bcf\u79d2\u4ee3\u5e63\u6578\uff08t\/s\uff09\u3002\u4f7f\u7528\u4e00\u81f4\u7684\u63d0\u793a\u9577\u5ea6\u3002<\/li>\n
        2. \u76e3\u63a7\u8cc7\u6e90\uff1a<\/strong>\u89c0\u5bdf\u4f60\u7684 VRAM \u4f7f\u7528\u91cf\u63a5\u8fd1\u5176\u6700\u5927\u503c\uff08\u4f8b\u5982\uff0c23.8 \/ 24 GB\uff09\u3002\u9019\u662f\u4f60\u7684\u5be6\u969b\u4e0a\u9650\u3002<\/li>\n
        3. \u8003\u616e\u6a21\u578b\u67b6\u69cb\uff1a<\/strong>\u4e26\u975e\u6240\u6709\u5c64\u90fd\u662f\u76f8\u540c\u7684\u3002\u65e9\u671f\u5c64\u901a\u5e38\u66f4\u9069\u5408\u4f7f\u7528 GPU \u5378\u8f09\u3002\u6700\u5f8c\u7684 ngl \u5c64\u6703\u88ab\u9001\u5230 GPU\u3002<\/li>\n<\/ol>\n

          \u7d93\u9a57\u6cd5\u5247\uff1a<\/strong>\u4e00\u500b\u597d\u7684\u8d77\u9ede\u662f\u5378\u8f09\u8db3\u5920\u7684\u5c64\uff0c\u4ee5\u586b\u6eff\u4f60\u53ef\u7528 VRAM \u7684 80-90%\uff0c\u4e26\u7559\u4e0b\u4e00\u500b\u5c0f\u7684\u7de9\u885d\u4ee5\u61c9\u5c0d\u984d\u5916\u958b\u92b7\u3002<\/p>\n

          \u6548\u80fd\u671f\u671b\u8207\u6b0a\u8861<\/strong><\/h2>\n

          \u4e86\u89e3\u6027\u80fd\u66f2\u7dda\u5c0d\u65bc\u8a2d\u5b9a\u73fe\u5be6\u7684\u671f\u671b\u81f3\u95dc\u91cd\u8981\u3002<\/p>\n

            \n
          • \u66f2\u7dda\u4e26\u975e\u7dda\u6027\uff1a<\/strong>\u6700\u521d\u5378\u8f09\u5230 GPU \u7684\u5c64\u63d0\u4f9b\u6700\u5927\u7684\u901f\u5ea6\u63d0\u5347\u3002\u6bcf\u589e\u52a0\u4e00\u5c64\uff0c\u589e\u76ca\u6703\u9010\u6f38\u6e1b\u5c0f\u3002\u5f9e 0 \u5c64\u589e\u52a0\u5230 20 \u5c64 GPU \u53ef\u80fd\u6703\u5c07\u901f\u5ea6\u63d0\u5347\u4e09\u500d\uff0c\u800c\u5f9e 20 \u5c64\u589e\u52a0\u5230 40 \u5c64\u53ef\u80fd\u50c5\u589e\u52a0 50% \u7684\u901f\u5ea6\u3002<\/li>\n
          • \u901a\u8a0a\u6210\u672c\uff1a<\/strong>\u8cc7\u6599\u5fc5\u9808\u901a\u904e PCIe \u7dda\u8def\u5728 CPU \u7684 RAM \u8207 GPU \u7684 VRAM \u4e4b\u9593\u50b3\u8f38\u3002\u9019\u500b\u958b\u92b7\u5c31\u662f\u6df7\u5408\u63a8\u7406\u6bd4\u5168 GPU \u63a8\u7406\u6162\u7684\u539f\u56e0\u3002\u8207\u820a\u578b\u6216\u8f03\u6162\u7684\u7e3d\u7dda\u76f8\u6bd4\uff0cPCIe 4.0 x16 \u4ecb\u9762\u5728\u9019\u65b9\u9762\u660e\u986f\u66f4\u597d\u3002<\/li>\n
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