{"id":1117,"date":"2026-10-28T00:00:15","date_gmt":"2026-10-27T16:00:15","guid":{"rendered":"https:\/\/localarch.ai\/?p=1117"},"modified":"2026-10-26T10:15:27","modified_gmt":"2026-10-26T02:15:27","slug":"scaling-your-home-ai-lab-from-single-gpu-to-multi-node-distributed-inference","status":"publish","type":"post","link":"https:\/\/localarch.ai\/zh-hant\/scaling-your-home-ai-lab-from-single-gpu-to-multi-node-distributed-inference\/","title":{"rendered":"\u64f4\u5c55\u4f60\u7684\u5bb6\u5ead AI \u5be6\u9a57\u5ba4\uff1a\u5f9e\u55ae GPU \u5230\u591a\u7bc0\u9ede\u5206\u4f48\u5f0f\u63a8\u7406"},"content":{"rendered":"

\u64f4\u5c55\u4f60\u7684\u5bb6\u5ead AI <\/strong>\u5be6\u9a57\u5ba4\uff1a\u5f9e\u55ae GPU <\/strong>\u5230\u591a\u7bc0\u9ede\u5206\u6563\u5f0f\u63a8\u7406<\/strong><\/h2>\n

\u5f9e\u5de5\u4f5c\u7ad9\u5230\u79c1\u6709 AI <\/strong>\u53e2\u96c6\u7684\u6f14\u8b8a<\/strong><\/h2>\n

\u6574\u6574\u4e00\u5e74\u4f86\uff0c\u6211\u5011\u5f15\u5c0e\u4f60\u5f9e\u5efa\u7acb\u4e00\u500b\u5e73\u8861\u7684\u55aeGPU\u7cfb\u7d71\uff0c\u5230\u5c07\u5b83\u865b\u64ec\u5316\u4e26\u5efa\u7acb\u7ba1\u7406\u6a5f\u5236\u3002\u73fe\u5728\uff0c\u96a8\u8457\u6211\u5011\u9032\u51652026\u5e74\u7684\u5f8c\u534a\u6bb5\uff0c\u8a31\u591a\u8cc7\u6df1\u7528\u6236\u9762\u81e8\u4e00\u500b\u65b0\u7684\u6311\u6230\uff1a\u55ae\u4e00\u4f3a\u670d\u5668\uff0c\u4e0d\u7ba1\u591a\u5f37\u5927\uff0c\u90fd\u5df2\u5230\u9054\u6975\u9650\u3002\u4e0d\u7ba1\u4f60\u662f\u56e0\u70ba\u5927\u578b\u6a21\u578b\u800c\u9047\u5230GPU\u8a18\u61b6\u9ad4\u7684\u74f6\u9838\uff0c\u9700\u8981\u540c\u6642\u670d\u52d9\u591a\u500b\u4f7f\u7528\u8005\uff0c\u6216\u8005\u53ea\u662f\u60f3\u5728\u79c1\u4eba\u5be6\u9a57\u5ba4\u4e2d\u7a81\u7834\u53ef\u80fd\u7684\u754c\u9650\uff0c\u4e0b\u4e00\u500b\u5408\u7406\u7684\u6b65\u9a5f\u5c31\u662f\u5206\u6563\u5f0f\u63a8\u7406<\/strong>\u3002\u9019\u672c\u9032\u968e\u6307\u5357\u8d85\u8d8a\u4e86\u55ae\u6a5f\u65b9\u6848\uff0c\u63a2\u7d22\u5982\u4f55\u900f\u904e\u9023\u63a5\u591a\u500b\u7bc0\u9ede\u4f86\u6c34\u5e73\u64f4\u5c55\u4f60\u7684\u5bb6\u5eadAI\u5be6\u9a57\u5ba4\u2013\u5c07\u4f60\u7684\u786c\u9ad4\u96c6\u5408\u8f49\u8b8a\u6210\u4e00\u500b\u5b8c\u6574\u5f37\u5927\u7684\u79c1\u4eba\u53e2\u96c6\uff0c\u80fd\u5920\u8655\u7406\u90a3\u4e9b\u904e\u53bb\u53ea\u6709\u96f2\u7aef\u4f9b\u61c9\u5546\u624d\u80fd\u61c9\u5c0d\u7684\u5de5\u4f5c\u8ca0\u8f09\u3002<\/p>\n

\u70ba\u4ec0\u9ebc\u8981\u6c34\u5e73\u64f4\u5c55\uff1f\u591a\u7bc0\u9ede\u63a8\u7406\u7684\u7406\u7531<\/strong><\/h2>\n

\u5728\u6df1\u5165\u4e86\u89e3\u300c\u5982\u4f55\u505a\u300d\u4e4b\u524d\uff0c\u5148\u7406\u89e3\u300c\u70ba\u4ec0\u9ebc\u300d\u662f\u5f88\u91cd\u8981\u7684\u3002\u5c0d\u9032\u968e\u4f7f\u7528\u8005\u4f86\u8aaa\uff0c\u6c34\u5e73\u64f4\u5c55\uff08\u589e\u52a0\u66f4\u591a\u6a5f\u5668\uff09\u8207\u5782\u76f4\u64f4\u5c55\uff08\u589e\u52a0\u66f4\u5f37\u5927\u7684\u786c\u9ad4\uff09\u5404\u6709\u5176\u7368\u7279\u7684\u597d\u8655\uff1a<\/p>\n

    \n
  • \u514b\u670d\u8a18\u61b6\u9ad4\u9650\u5236\uff1a<\/strong>\u9019\u662f\u6700\u76f4\u63a5\u7684\u539f\u56e0\u3002\u50cf\u6a21\u578b\u4e26\u884c<\/strong>\u9019\u985e\u6280\u8853\u53ef\u4ee5\u628a\u55ae\u4e00\u7684 70B \u6216 120B+ \u53c3\u6578\u6a21\u578b\u62c6\u5206\u5230\u591a\u5f35 GPU \u7684 VRAM \u4e0a\uff0c\u5373\u4f7f\u5b83\u5011\u5728\u4e0d\u540c\u7684\u6a5f\u5668\u4e0a\uff0c\u4e5f\u80fd\u8b93\u4ee5\u524d\u300c\u7121\u6cd5\u904b\u884c\u300d\u7684\u6a21\u578b\u8b8a\u5f97\u53ef\u7528\u3002<\/li>\n
  • \u6700\u5927\u5316\u670d\u52d9\u7684\u541e\u5410\u91cf\uff1a<\/strong>\u7576\u4f60\u9700\u8981\u8655\u7406\u5927\u91cf\u8acb\u6c42\u6642\uff08\u4f8b\u5982\uff0c\u5927\u578b\u5718\u968a\u4f7f\u7528\u7684\u5167\u90e8\u804a\u5929\u6a5f\u5668\u4eba\uff09\uff0c\u4f60\u53ef\u4ee5\u4f7f\u7528\u6d41\u6c34\u7dda\u4e26\u884c<\/strong>\u8655\u7406\u4e0d\u540c\u7bc0\u9ede\u4e0a\u7684\u4e0d\u540c\u8acb\u6c42\uff0c\u6216\u8005\u4f7f\u7528\u591a\u500b\u6a21\u578b\u526f\u672c\u4f86\u9032\u884c\u8ca0\u8f09\u5747\u8861<\/strong>\u3002<\/li>\n
  • \u7570\u69cb\u786c\u9ad4\u7684\u6210\u672c\u6548\u76ca\u4f7f\u7528\uff1a<\/strong>\u96c6\u7fa4\u53ef\u4ee5\u8b93\u4f60\u6574\u5408\u4e0d\u540c\u96fb\u8166\u7684\u904b\u7b97\u80fd\u529b \u2013\u4e00\u53f0\u5f37\u5927\u7684\u4e3b\u4f3a\u670d\u5668\u642d\u914d RTX 6000 Ada\uff0c\u4e00\u53f0\u820a\u6b3e\u904a\u6232 PC \u642d\u914d 3090\uff0c\u751a\u81f3\u4e00\u53f0 Intel NUC \u642d\u914d ARC GPU\u3002\u6bcf\u53f0\u96fb\u8166\u90fd\u53ef\u4ee5\u8655\u7406\u9069\u5408\u81ea\u5df1\u80fd\u529b\u7684\u5de5\u4f5c\u8ca0\u8f09\u3002<\/li>\n
  • \u9ad8\u53ef\u7528\u6027\u8207\u97cc\u6027\uff1a<\/strong>\u900f\u904e\u591a\u7bc0\u9ede\u8a2d\u7f6e\uff0c\u4f60\u53ef\u4ee5\u8a2d\u8a08\u7cfb\u7d71\u8b93\u67d0\u500b\u7bc0\u9ede\u51fa\u554f\u984c\u6642\uff0c\u5176\u4ed6\u7bc0\u9ede\u53ef\u4ee5\u63a5\u624b\uff0c\u78ba\u4fdd\u95dc\u9375\u7684 AI \u670d\u52d9\u4ecd\u7136\u53ef\u7528\u3002<\/li>\n<\/ul>\n

    \u5efa\u7bc9\u85cd\u5716\uff1a\u901a\u5f80\u5206\u6563\u5be6\u9a57\u5ba4\u7684\u9053\u8def<\/strong><\/h2>\n

    \u5efa\u7acb\u53e2\u96c6\u6c92\u6709\u55ae\u4e00\u7684\u300c\u6b63\u78ba\u300d\u65b9\u5f0f\u3002\u4f60\u7684\u65b9\u6cd5\u53d6\u6c7a\u65bc\u4f60\u7684\u4e3b\u8981\u76ee\u6a19\u3002\u4e0b\u5716\u8aaa\u660e\u4e86\u5f9e\u55ae\u4e00 GPU \u5230\u5206\u6563\u5f0f\u63a8\u8ad6\u53e2\u96c6\u7684\u4e3b\u8981\u67b6\u69cb\u8def\u5f91\uff1a<\/p>\n

     <\/p>\n

     <\/p>\n

    \"\"<\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

     <\/p>\n

    <\/h2>\n

     <\/p>\n

    \u9014\u5f911<\/strong>\uff1a\u5de8\u578b\u6a21\u578b\u7684\u6a21\u578b\u4e26\u884c<\/strong><\/h2>\n

    \u9019\u662f\u7d66\u9700\u8981\u904b\u884c\u6bd4\u4efb\u4f55\u55ae\u4e00 GPU \u8a18\u61b6\u9ad4\u9084\u5927\u7684\u6a21\u578b\u7684\u4f7f\u7528\u8005\u3002\u50cf vLLM \u88e1\u7684 Tensor Parallelism<\/strong> \u6216 DeepSpeed<\/strong> \u9019\u985e\u6846\u67b6\u6703\u628a\u6a21\u578b\u7684\u5c64\u3001\u6ce8\u610f\u529b\u982d\u6216\u6b0a\u91cd\u5206\u6563\u5230\u4e0d\u540c\u7684\u88dd\u7f6e\u4e0a\u3002GPU \u4e4b\u9593\u7684\u901a\u8a0a\uff08\u900f\u904e NVLink \u6216 PCIe\uff09\u5c0d\u6548\u80fd\u975e\u5e38\u91cd\u8981\u3002<\/p>\n

    \u5de5\u5177\u4eae\u9ede\uff1a\u5177\u5099\u5f35\u91cf\u4e26\u884c\u7684 vLLM<\/strong><\/p>\n

    vLLM \u5df2\u7d93\u6210\u70ba\u9ad8\u541e\u5410\u91cf\u670d\u52d9\u7684\u9ec3\u91d1\u6a19\u6e96\uff0c\u73fe\u5728\u5728\u5206\u6563\u5f0f\u63a8\u7406\u65b9\u9762\u4e5f\u8868\u73fe\u51fa\u8272\u3002<\/p>\n\n\n\n
    # Example: Serving a model across two GPUs on the same machine<\/p>\n

    python -m vllm.entrypoints.api_server \\<\/p>\n

    –model meta-llama\/Llama-3.1-70B-Instruct \\<\/p>\n

    –tensor-parallel-size 2 \\<\/p>\n

    –gpu-memory-utilization 0.9 \\<\/p>\n

    –port 8000<\/p>\n

     <\/p>\n

    # To extend across nodes, you’d use a –distributed-worker flag<\/p>\n

    # and configure a Ray cluster (vLLM’s distributed backend)<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

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

    \u9014\u5f91 2<\/strong>\uff1a\u591a\u7bc0\u9ede\u63a8\u7406\u7528\u65bc\u670d\u52d9\u8207\u8ca0\u8f09\u5e73\u8861<\/strong><\/h2>\n

    \u9019\u5c0d\u6709\u5f88\u591a\u7528\u6236\u7684\u61c9\u7528\u7a0b\u5f0f\u4f86\u8aaa\u975e\u5e38\u7406\u60f3\u3002\u4f60\u53ef\u4ee5\u5728\u4e0d\u540c\u7684\u6a5f\u5668\u4e0a\u904b\u884c\u591a\u500b\u6a21\u578b\u526f\u672c\uff0c\u7136\u5f8c\u5728\u5b83\u5011\u524d\u9762\u653e\u7f6e\u4e00\u500b\u8ca0\u8f09\u5e73\u8861\u5668<\/strong>\u3002<\/p>\n

    \u6838\u5fc3\u5806\u758a\uff1a\u7de8\u6392 + <\/strong>\u670d\u52d9 + <\/strong>\u8def\u7531<\/strong><\/p>\n

      \n
    1. \u7de8\u6392\uff1aKubernetes<\/strong>\uff08K3s\/K8s<\/strong>\uff09<\/strong>\u3002\u8f15\u91cf\u7d1a\u7684 K3s \u975e\u5e38\u9069\u5408\u5bb6\u7528\u5be6\u9a57\u5ba4\u3002\u5b83\u53ef\u4ee5\u7ba1\u7406\u4f60\u7684 AI \u5bb9\u5668\u90e8\u7f72\u3001\u64f4\u7e2e\u526f\u672c\u6578\u91cf\uff0c\u4ee5\u53ca\u8655\u7406\u6545\u969c\u3002<\/li>\n
    2. \u63d0\u4f9b\u670d\u52d9\uff1aOllama <\/strong>\u6216 vLLM<\/strong> \u4ee5\u5bb9\u5668\u5316\u5f62\u5f0f\u904b\u884c\u3002\u6bcf\u500b\u90fd\u5728\u81ea\u5df1\u7684 Pod \u4e2d\u904b\u884c\u3002<\/li>\n
    3. \u8def\u7531\/API <\/strong>\u9580\u6236\uff1aTraefik <\/strong>\u6216 Nginx<\/strong>\u3002<\/strong>\u5b83\u4f4d\u65bc\u4f60\u7684\u53e2\u96c6\u524d\u65b9\uff0c\u63a5\u6536\u6240\u6709\u4f7f\u7528\u8005\u8acb\u6c42\uff0c\u4e26\u5c07\u5b83\u5011\u5747\u52fb\u5206\u914d\u7d66\u5065\u5eb7\u7684\u6a21\u578b\u526f\u672c\u3002<\/li>\n<\/ol>\n

      \u5be6\u4f5c\u6307\u5357\uff1a\u5efa\u7acb\u4e00\u500b\u96d9\u7bc0\u9ede K3s <\/strong>\u53e2\u96c6<\/strong><\/p>\n

      \u9019\u88e1\u6709\u4e00\u4efd\u7c21\u5316\u7248\u7684\u6307\u5357\uff0c\u6559\u4f60\u5982\u4f55\u5efa\u7acb\u4e00\u500b\u57fa\u672c\u4e14\u53ef\u904b\u4f5c\u7684\u53e2\u96c6\u3002\u6211\u5011\u5047\u8a2d\u4f60\u6709\u5169\u53f0 Linux \u6a5f\u5668\uff08node-01 \u7576\u4f5c\u4f3a\u670d\u5668\uff0cnode-02 \u7576\u4f5c\u4ee3\u7406\uff09\u5728\u540c\u4e00\u500b\u7db2\u8def\u4e0a\u3002<\/p>\n

      \u6b65\u9a5f 1<\/strong>\uff1a\u57fa\u790e – <\/strong>\u5b89\u88dd K3s<\/strong><\/p>\n

      K3s \u662f\u4e00\u500b\u8f15\u91cf\u7d1a\u3001\u7d93\u8a8d\u8b49\u7684 Kubernetes \u767c\u884c\u7248\u672c\uff0c\u975e\u5e38\u9069\u5408\u8cc7\u6e90\u6709\u9650\u7684\u74b0\u5883\u3002<\/p>\n

      \u5728\u4e3b\u7bc0\u9ede (node-01) <\/strong>\u4e0a\uff1a<\/strong><\/p>\n\n\n\n
      curl -sfL https:\/\/get.k3s.io | INSTALL_K3S_EXEC=”–disable traefik –node-taint CriticalAddonsOnly=true:NoExecute” sh –<\/p>\n

      # Get the node token for joining agents<\/p>\n

      sudo cat \/var\/lib\/rancher\/k3s\/server\/node-token<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u5728\u5de5\u4f5c\u7bc0\u9ede\uff08node-02<\/strong>\uff09\uff1a<\/strong><\/p>\n\n\n\n
      curl -sfL https:\/\/get.k3s.io | K3S_URL=https:\/\/<node-01-ip>:6443 K3S_TOKEN=<token-from-above> sh –<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u6b65\u9a5f 2<\/strong>\uff1a\u5c07\u4f60\u7684\u63a8\u7406\u5f15\u64ce\u5bb9\u5668\u5316<\/strong><\/p>\n

      \u70ba\u4f60\u7684\u6a21\u578b\u670d\u52d9\u5275\u5efa\u4e00\u500b Dockerfile\u3002\u9019\u88e1\u6709\u4e00\u500b Ollama \u7684\u7bc4\u4f8b\uff1a<\/p>\n\n\n\n\n
      DockerFile<\/td>\n<\/tr>\n
      # Dockerfile.ollama-serve<\/p>\n

      FROM ollama\/ollama:latest<\/p>\n

      RUN ollama pull llama3.1:8b-instruct-q5_k_m<\/p>\n

      EXPOSE 11434<\/p>\n

      CMD [“ollama”, “serve”]<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u5728\u5169\u500b\u7bc0\u9ede\u4e0a\u5efa\u7acb\u4e26\u8f09\u5165\u6620\u50cf\uff0c\u6216\u8a2d\u7f6e\u4e00\u500b\u79c1\u4eba\u8a3b\u518a\u8868\u3002<\/p>\n

      \u6b65\u9a5f 3<\/strong>\uff1a\u7528 Kubernetes <\/strong>\u6e05\u55ae\u90e8\u7f72<\/strong><\/p>\n

      \u5efa\u7acb\u4e00\u500b\u6a94\u6848 llama-deployment.yaml\u3002\u9019\u500b\u6a94\u6848\u544a\u8a34 K3s \u904b\u884c\u5169\u500b Ollama pod \u7684\u526f\u672c\u4e26\u5c07\u5b83\u5011\u516c\u958b\u3002<\/p>\n\n\n\n\n
      yaml<\/td>\n<\/tr>\n
      apiVersion: apps\/v1<\/p>\n

      kind: Deployment<\/p>\n

      metadata:<\/p>\n

      name: ollama-llama<\/p>\n

      spec:<\/p>\n

      replicas: 2 # Launches two identical pods<\/p>\n

      selector:<\/p>\n

      matchLabels:<\/p>\n

      app: llama-inference<\/p>\n

      template:<\/p>\n

      metadata:<\/p>\n

      labels:<\/p>\n

      app: llama-inference<\/p>\n

      spec:<\/p>\n

      hostname: ollama-pod<\/p>\n

      containers:<\/p>\n

      – name: ollama<\/p>\n

      image: local\/ollama-llama:latest<\/p>\n

      ports:<\/p>\n

      – containerPort: 11434<\/p>\n

      resources:<\/p>\n

      limits:<\/p>\n

      nvidia.com\/gpu: 1 # Critical: Requests 1 GPU per pod<\/p>\n

      —<\/p>\n

      apiVersion: v1<\/p>\n

      kind: Service<\/p>\n

      metadata:<\/p>\n

      name: ollama-service<\/p>\n

      spec:<\/p>\n

      selector:<\/p>\n

      app: llama-inference<\/p>\n

      ports:<\/p>\n

      – protocol: TCP<\/p>\n

      port: 11434<\/p>\n

      targetPort: 11434<\/p>\n

      —<\/p>\n

      apiVersion: networking.k8s.io\/v1<\/p>\n

      kind: Ingress<\/p>\n

      metadata:<\/p>\n

      name: ollama-ingress<\/p>\n

      spec:<\/p>\n

      ingressClassName: nginx<\/p>\n

      rules:<\/p>\n

      – http:<\/p>\n

      paths:<\/p>\n

      – path: \/<\/p>\n

      pathType: Prefix<\/p>\n

      backend:<\/p>\n

      service:<\/p>\n

      name: ollama-service<\/p>\n

      port:<\/p>\n

      number: 11434<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

      \u90e8\u7f72\u5b83\uff1akubectl apply -f llama-deployment.yaml<\/p>\n

      \u91cd\u8981\u8003\u91cf\u8207\u6545\u969c\u6392\u9664<\/strong><\/p>\n

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       <\/p>\n","protected":false},"excerpt":{"rendered":"

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