\u90a3\u4e48，\u5982\u4f55\u5728\u81ea\u5df1\u7684\u670d\u52a1\u5668\u4e0a\u90e8\u7f72\u8fd9\u4e2a"\u5de8\u65e0\u9738"\u6a21\u578b\u5462？\u63a5\u4e0b\u6765，\u6211\u5c06\u5206\u4eab\u6211\u7684\u5b8c\u6574\u64cd\u4f5c\u6d41\u7a0b\u3002<\/p>\n
\u4e00\u3001\u73af\u5883\u51c6\u5907<\/h3>\n1.1 \u786c\u4ef6\u914d\u7f6e<\/h4>\n
\u5728\u5f00\u59cb\u90e8\u7f72\u4e4b\u524d，\u5148\u6765\u770b\u770b\u6211\u4f7f\u7528\u7684\u786c\u4ef6\u914d\u7f6e：<\/p>\n
- \n
- \u670d\u52a1\u5668\u578b\u53f7：H20<\/li>\n
- GPU：8\u00d7NVIDIA H20 (141GB)<\/li>\n
- CPU：\u53cc\u8defIntel\u81f3\u5f3a\u5904\u7406\u5668<\/li>\n
- \u5185\u5b58：2TB<\/li>\n
- \u5b58\u50a8：\u9ad8\u901fNVMe SSD<\/li>\n<\/ul>\n
\u8fd9\u5957\u914d\u7f6e\u5bf9\u4e8e\u90e8\u7f72671B\u53c2\u6570\u7684\u6a21\u578b\u6765\u8bf4\u662f\u521a\u597d\u591f\u7528\u7684\u3002\u6839\u636e\u6211\u7684\u7ecf\u9a8c，\u81f3\u5c11\u9700\u89818\u5f20\u9ad8\u7aefGPU\u624d\u80fd\u6ee1\u8db3\u63a8\u7406\u9700\u6c42\u3002<\/p>\n
1.2 \u73af\u5883\u68c0\u67e5<\/h4>\n
\u9996\u5148，\u786e\u8ba4\u7cfb\u7edf\u8d44\u6e90\u662f\u5426\u6ee1\u8db3\u9700\u6c42：<\/p>\n
# \u68c0\u67e5CPU\u4fe1\u606f
\nlscpu<\/p>\n# \u68c0\u67e5GPU\u4fe1\u606f
\nnvidia-smi<\/p>\n# \u68c0\u67e5\u5185\u5b58\u4fe1\u606f
\ndmidecode -t memory<\/p>\n# \u68c0\u67e5\u78c1\u76d8\u7a7a\u95f4
\ndf -h<\/p>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250419111107-680384cbe684f.png\")
\u00a0\u8fd9\u6b21\u8bd5\u7528\u7684H20\u662f141G\u663e\u5b58\u7684PCIE\u7248\u672c\u30028\u5f20GPU\u4e4b\u95f4\u90fd\u662f\u901a\u8fc7NV18(18\u6761NVLink)\u4e92\u8054,\u5f62\u6210\u4e86\u5168\u4e92\u8054(fully connected)\u7684\u7f51\u7edc\u62d3\u6251,GPU0-3\u5c5e\u4e8eNUMA\u8282\u70b90 (CPU\u6838\u5fc30-55,112-167),GPU4-7\u5c5e\u4e8eNUMA\u8282\u70b91 (CPU\u6838\u5fc356-111,168-223),\u5355\u5361\u603b\u5e26\u5bbd：26.562 \u00d7 18 \u2248 478 GB\/s<\/p>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250419111108-680384cc27357.png\")
\u00a0<\/p>\n\u7279\u522b\u6ce8\u610f：\u90e8\u7f72DeepSeek R1 671B\u81f3\u5c11\u9700\u8981700GB\u7684\u78c1\u76d8\u7a7a\u95f4\u7528\u4e8e\u5b58\u50a8\u6a21\u578b\u6587\u4ef6，\u8bf7\u786e\u4fdd\u6709\u8db3\u591f\u7a7a\u95f4\u3002<\/p>\n
1.3 \u8f6f\u4ef6\u73af\u5883\u914d\u7f6e<\/h4>\n
\u6211\u9009\u62e9\u4f7f\u7528Apptainer（\u539fSingularity）\u4f5c\u4e3a\u5bb9\u5668\u8fd0\u884c\u73af\u5883，\u5b83\u6bd4Docker\u66f4\u9002\u5408HPC\u573a\u666f，\u5728\u591aGPU\u534f\u4f5c\u65b9\u9762\u8868\u73b0\u66f4\u597d\u3002<\/p>\n
# \u5b89\u88c5Apptainer
\nsudo add-apt-repository -y ppa:apptainer\/ppa
\nsudo apt update
\nsudo apt install -y apptainer<\/p>\n# \u68c0\u67e5\u5b89\u88c5\u7248\u672c
\napptainer –version<\/p>\n\u4e8c\u3001\u6a21\u578b\u83b7\u53d6\u4e0e\u5b58\u50a8<\/h3>\n
2.1 \u6a21\u578b\u4e0b\u8f7d<\/h4>\n
DeepSeek R1 671B\u6a21\u578b\u53ef\u4ee5\u4ece\u5b98\u65b9\u6e20\u9053\u4e0b\u8f7d，\u4f46\u6587\u4ef6\u975e\u5e38\u5927\u3002\u5728\u6211\u7684\u6848\u4f8b\u4e2d，\u6a21\u578b\u5df2\u9884\u5148\u4e0b\u8f7d\u5e76\u5b58\u50a8\u5728 \/data0\/DeepSeek-R1\/ \u76ee\u5f55\u4e0b\u3002<\/p>\n
2.2 \u6a21\u578b\u5b8c\u6574\u6027\u9a8c\u8bc1<\/h4>\n
\u4e0b\u8f7d\u5b8c\u6210\u540e，\u52a1\u5fc5\u9a8c\u8bc1\u6a21\u578b\u6587\u4ef6\u7684\u5b8c\u6574\u6027：<\/p>\n
cd \/data0\/DeepSeek-R1
\n# \u9a8c\u8bc1\u6a21\u578b\u6587\u4ef6\u7684MD5\u503c
\nmd5sum model-00001-of-00163.safetensors<\/p>\n\u26a0\ufe0f \u6ce8\u610f：\u6a21\u578b\u6587\u4ef6\u53ef\u80fd\u5206\u4e3a\u591a\u4e2a\u90e8\u5206，\u4e00\u5b9a\u8981\u9a8c\u8bc1\u6240\u6709\u6587\u4ef6\u7684\u5b8c\u6574\u6027，\u907f\u514d\u56e0\u6587\u4ef6\u635f\u574f\u5bfc\u81f4\u7684\u542f\u52a8\u5931\u8d25\u3002<\/p>\n
\u4e09\u3001\u670d\u52a1\u90e8\u7f72<\/h3>\n
\u5bf9\u4e8e\u8d85\u5927\u89c4\u6a21\u6a21\u578b，\u6211\u6d4b\u8bd5\u4e86\u4e24\u79cd\u4e3b\u6d41\u7684\u90e8\u7f72\u65b9\u5f0f：\u57fa\u4e8evLLM\u548c\u57fa\u4e8eSGLang\u7684\u90e8\u7f72\u3002<\/p>\n
3.1 \u57fa\u4e8evLLM\u7684\u90e8\u7f72<\/h4>\n
vLLM\u662f\u4e00\u4e2a\u9ad8\u6027\u80fd\u7684\u5927\u8bed\u8a00\u6a21\u578b\u63a8\u7406\u5f15\u64ce，\u4e13\u4e3aLLM\u4f18\u5316，\u652f\u6301PagedAttention\u7b49\u6280\u672f，\u5185\u5b58\u4f7f\u7528\u6548\u7387\u9ad8\u3002<\/p>\n
3.1.1 \u83b7\u53d6vLLM\u5bb9\u5668\u955c\u50cf<\/h5>\n
mkdir -p \/data0\/ctyun\/vllm
\ncd \/data0\/ctyun\/vllm
\nwget https:\/\/jiangsu-10.zos.ctyun.cn\/galaxy\/apptainer\/vllm\/vllm-openai_v0.7.3.sif<\/p>\n3.1.2 \u521b\u5efa\u542f\u52a8\u811a\u672c<\/h5>\n
vi run.sh<\/p>\n
\u5728\u811a\u672c\u4e2d\u6dfb\u52a0\u4ee5\u4e0b\u5185\u5bb9：<\/p>\n
#!\/bin\/bash
\napptainer run –nv vllm-openai_v0.7.3.sif \\\\
\n python3 -m vllm.entrypoints.openai.api_server \\\\
\n –model \/data0\/DeepSeek-R1 \\\\
\n –tensor-parallel-size 8 \\\\
\n –host 0.0.0.0 \\\\
\n –port 8000<\/p>\n\u8fd9\u91cc\u7684\u5173\u952e\u53c2\u6570\u662f–tensor-parallel-size 8，\u8868\u793a\u4f7f\u75288\u5361\u5f20\u91cf\u5e76\u884c，\u8fd9\u5bf9\u4e8e671B\u89c4\u6a21\u7684\u6a21\u578b\u662f\u5fc5\u987b\u7684\u3002<\/p>\n
3.1.3 \u542f\u52a8\u670d\u52a1<\/h5>\n
sh run.sh<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250419111108-680384cc4b867.png\")
<\/p>\n![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250419111110-680384ce582f5.png\")
<\/p>\nvllm\u670d\u52a1\u542f\u52a8\u6210\u529f\u540e，\u6bcf\u5757\u663e\u5361\u7684\u663e\u5b58\u5df2\u7ecf\u5360\u7528\u4e86122G\u3002\u00a0<\/p>\n
\u6210\u529f\u542f\u52a8\u540e，vLLM\u4f1a\u63d0\u4f9b\u4e00\u4e2a\u517c\u5bb9OpenAI API\u683c\u5f0f\u7684\u63a5\u53e3，\u9ed8\u8ba4\u7aef\u53e3\u4e3a8000\u3002<\/p>\n
3.2 \u57fa\u4e8eSGLang\u7684\u90e8\u7f72<\/h4>\n
SGLang\u662f\u53e6\u4e00\u4e2a\u4f18\u79c0\u7684LLM\u63a8\u7406\u6846\u67b6，\u7279\u522b\u5728\u6279\u5904\u7406\u65b9\u9762\u6709\u4e00\u4e9b\u72ec\u7279\u4f18\u52bf\u3002<\/p>\n
3.2.1 \u4e0b\u8f7dSGLang\u5bb9\u5668\u955c\u50cf<\/h5>\n
mkdir -p \/data0\/ctyun\/sglang
\ncd \/data0\/ctyun\/sglang
\nwget https:\/\/jiangsu-10.zos.ctyun.cn\/galaxy\/apptainer\/sglang\/sglang_v0.4.3-cu125.sif<\/p>\n3.2.2 \u521b\u5efa\u542f\u52a8\u811a\u672c\u5e76\u8fd0\u884c<\/h5>\n
vi run.sh
\n# \u914d\u7f6eSGLang\u542f\u52a8\u53c2\u6570
\n#!\/bin\/bash<\/p>\n# SGLang Server Startup Script
\n# Environment configuration
\nexport OMP_NUM_THREADS=14
\nexport NCCL_IB_DISABLE=1
\nexport CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7"<\/p>\n# Model configuration
\nCONTAINER_PATH="\/data0\/ctyun\/sglang\/sglang_v0.4.3-cu125.sif"
\nWORKSPACE_DIR="\/data0\/ctyun\/sglang\/workspace"
\nMODELS_DIR="\/data0\/DeepSeek-R1"
\nMODEL_NAME="DeepSeek-R1"<\/p>\n# Create workspace directory if it doesn't exist
\nmkdir -p "$WORKSPACE_DIR"<\/p>\n# Server Configuration
\nSGLANG_HOST="0.0.0.0"
\nSGLANG_PORT=8000<\/p>\n# Performance Configuration
\nTENSOR_PARALLEL_SIZE=8
\nTOKENIZER_MODE="auto"
\nLOG_LEVEL="info"<\/p>\necho "Starting SGLang server with model: $MODEL_NAME"
\necho "Using GPUs: $CUDA_VISIBLE_DEVICES with TP size: $TENSOR_PARALLEL_SIZE"<\/p>\n# Run the SGLang container with Apptainer\/Singularity
\n# Use the LOCAL_PYTORCH_MODEL format to specify a local model
\napptainer run –nv \\\\
\n –bind "$WORKSPACE_DIR:\/workspace" \\\\
\n –bind "$MODELS_DIR:\/model" \\\\
\n "$CONTAINER_PATH" \\\\
\n python3 -m sglang.launch_server \\\\
\n –model-path "\/model" \\\\
\n –tokenizer-path "\/model" \\\\
\n –host "$SGLANG_HOST" \\\\
\n –port "$SGLANG_PORT" \\\\
\n –tensor-parallel-size "$TENSOR_PARALLEL_SIZE" \\\\
\n –context-length 32768 \\\\
\n –mem-fraction-static 0.9 \\\\
\n –tokenizer-mode "$TOKENIZER_MODE" \\\\
\n –trust-remote-code \\\\
\n –log-level "$LOG_LEVEL"<\/p>\n# \u542f\u52a8\u670d\u52a1
\nsh run.sh<\/p>\n🔔 \u5c0f\u8d34\u58eb：\u6211\u53d1\u73b0vLLM\u5728\u901a\u7528\u573a\u666f\u4e0b\u8868\u73b0\u66f4\u7a33\u5b9a，\u800cSGLang\u5728\u6279\u5904\u7406\u573a\u666f\u4e0b\u541e\u5410\u91cf\u7565\u9ad8\u3002<\/p>\n
![\"\"](\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2025\/04\/20250419111110-680384ce84bea.png\")
<\/p>\nSGLang\u660e\u663e\u5360\u7528\u663e\u5b58\u4e00\u4e9b，\u6a21\u578b\u52a0\u8f7d\u5b8c\u6210\u663e\u5b58\u5df2\u7ecf\u5403\u5f97\u5dee\u4e0d\u591a\u4e86\u3002\u00a0<\/p>\n
\u56db\u3001\u538b\u529b\u6d4b\u8bd5\u5de5\u5177\u51c6\u5907<\/h3>\n
\u4e3a\u4e86\u5168\u9762\u8bc4\u4f30DeepSeek R1 671B\u7684\u6027\u80fd，\u6211\u4f7f\u7528\u4e86\u4e09\u79cd\u4e0d\u540c\u7684\u6d4b\u8bd5\u5de5\u5177：LLMPerf\u3001EvalScope\u548cSGLang\u5185\u7f6e\u7684benchmark\u5de5\u5177\u3002<\/p>\n
4.1 LLMPerf\u6d4b\u8bd5\u5de5\u5177\u5b89\u88c5<\/h4>\n
LLMPerf\u662f\u4e00\u4e2a\u4e13\u95e8\u9488\u5bf9\u5927\u6a21\u578b\u8bbe\u8ba1\u7684\u6027\u80fd\u6d4b\u8bd5\u5de5\u5177：<\/p>\n
mkdir -p \/data0\/ctyun\/yangxian
\ncd \/data0\/ctyun\/yangxian
\ngit clone https:\/\/gitee.com\/yangxianpku\/llmperf.git<\/p>\n# \u8bbe\u7f6e\u73af\u5883\u53d8\u91cf
\nexport HF_ENDPOINT=https:\/\/hf-mirror.com
\nexport OPENAI_API_KEY=secret_abcdefg
\nexport OPENAI_API_BASE="http:\/\/localhost:8000\/v1\/"<\/p>\n4.2 EvalScope\u6d4b\u8bd5\u5de5\u5177\u5b89\u88c5<\/h4>\n
EvalScope\u662f\u53e6\u4e00\u4e2a\u529f\u80fd\u5f3a\u5927\u7684\u8bc4\u4f30\u5de5\u5177，\u5c24\u5176\u9002\u5408\u6a21\u62df\u771f\u5b9e\u7528\u6237\u8bf7\u6c42：<\/p>\n
# \u521b\u5efa\u865a\u62df\u73af\u5883
\npython3 -m venv evalscope
\ncd evalscope\/
\nsource bin\/activate<\/p>\n# \u5b89\u88c5evalscope
\npip install evalscope
\npip install evalscope[perf]<\/p>\n4.3 SGLang\u6d4b\u8bd5\u5de5\u5177\u5b89\u88c5<\/h4>\n
SGLang\u81ea\u5e26\u4e86\u6027\u80fd\u57fa\u51c6\u6d4b\u8bd5\u5de5\u5177，\u53ef\u4ee5\u7cbe\u786e\u6d4b\u91cf\u6279\u5904\u7406\u6027\u80fd：<\/p>\n
python3 -m venv sglang
\ncd sglang\/
\nsource bin\/activate
\npip install "sglang[all]>=0.4.3" –find-links https:\/\/flashinfer.ai\/whl\/cu124\/torch2.5\/flashinfer-python<\/p>\n\u4e94\u3001\u538b\u529b\u6d4b\u8bd5\u65b9\u6848\u4e0e\u7ed3\u679c<\/h3>\n
\u63a5\u4e0b\u6765\u662f\u6700\u6fc0\u52a8\u4eba\u5fc3\u7684\u90e8\u5206 – \u538b\u529b\u6d4b\u8bd5！\u6211\u8bbe\u8ba1\u4e86\u4e00\u7cfb\u5217\u6d4b\u8bd5\u573a\u666f，\u4ece\u5355\u5e76\u53d1\u5230\u9ad8\u5e76\u53d1，\u4ece\u77ed\u6587\u672c\u5230\u957f\u6587\u672c\u751f\u6210，\u5168\u65b9\u4f4d\u8bc4\u4f30\u6a21\u578b\u6027\u80fd\u3002<\/p>\n
5.1 \u4f7f\u7528LLMPerf\u8fdb\u884c\u541e\u5410\u91cf\u6d4b\u8bd5<\/h4>\n
\u9996\u5148，\u6d4b\u8bd5\u4e0d\u540c\u8f93\u5165\u957f\u5ea6\u4e0b\u7684\u5355\u5e76\u53d1\u6027\u80fd：<\/p>\n
# \u8f93\u51658K tokens，\u8f93\u51fa1K tokens
\npython3 token_benchmark_ray.py –model "DeepSeek-R1" \\\\
\n –mean-input-tokens 8192 –stddev-input-tokens 0 \\\\
\n –mean-output-tokens 1024 –stddev-output-tokens 0 \\\\
\n –max-num-completed-requests 6 –timeout 600 \\\\
\n –num-concurrent-requests 1 –results-dir "result_outputs" \\\\
\n –llm-api openai –additional-sampling-params '{}'<\/p>\n\u7136\u540e，\u6d4b\u8bd5\u4e0d\u540c\u5e76\u53d1\u6570\u4e0b\u7684\u6027\u80fd\u8868\u73b0：<\/p>\n
# 64\u5e76\u53d1，\u8f93\u51654K tokens，\u8f93\u51fa1K tokens
\npython3 token_benchmark_ray.py –model "DeepSeek-R1" \\\\
\n –mean-input-tokens 4096 –stddev-input-tokens 0 \\\\
\n –mean-output-tokens 1024 –stddev-output-tokens 0 \\\\
\n –max-num-completed-requests 192 –timeout 600 \\\\
\n –num-concurrent-requests 64 –results-dir "result_outputs" \\\\
\n –llm-api openai –additional-sampling-params '{}'<\/p>\n\u6d4b\u8bd5\u7ed3\u679c\u5206\u6790：<\/p>\n
- \n
- \u5355\u5e76\u53d1\u4e0b，8K\u8f93\u5165+1K\u8f93\u51fa\u7684\u573a\u666f，\u5e73\u5747\u541e\u5410\u91cf\u7ea6\u4e3a750 tokens\/s<\/li>\n
- \u5e76\u53d1\u6570\u589e\u52a0\u523064\u65f6，\u603b\u541e\u5410\u91cf\u53ef\u8fbe2\u4e07 tokens\/s\u5de6\u53f3<\/li>\n
- \u8d85\u8fc7128\u5e76\u53d1\u540e，\u6027\u80fd\u63d0\u5347\u4e0d\u660e\u663e，\u751a\u81f3\u53ef\u80fd\u56e0\u8d44\u6e90\u7ade\u4e89\u800c\u4e0b\u964d<\/li>\n<\/ul>\n
5.2 \u4f7f\u7528EvalScope\u6a21\u62df\u771f\u5b9e\u7528\u6237\u8bf7\u6c42<\/h4>\n
EvalScope\u80fd\u6a21\u62df\u66f4\u63a5\u8fd1\u771f\u5b9e\u573a\u666f\u7684\u6d4b\u8bd5，\u6211\u4ece\u4f4e\u5e76\u53d1\u9010\u6b65\u63d0\u9ad8\u5230\u9ad8\u5e76\u53d1：<\/p>\n
# \u5355\u5e76\u53d1\u6d4b\u8bd5
\nevalscope perf –parallel 1 –url http:\/\/127.0.0.1:8000\/v1\/chat\/completions \\\\
\n –model DeepSeek-R1 –log-every-n-query 5 –connect-timeout 6000 \\\\
\n –read-timeout 6000 –max-tokens 2048 –min-tokens 2048 \\\\
\n –api openai –dataset openqa –number 1 –stream<\/p>\n# \u9010\u6b65\u63d0\u9ad8\u5e76\u53d1
\nevalscope perf –parallel 192 –url http:\/\/127.0.0.1:8000\/v1\/chat\/completions \\\\
\n –model DeepSeek-R1 –log-every-n-query 5 –connect-timeout 6000 \\\\
\n –read-timeout 6000 –max-tokens 2048 –min-tokens 2048 \\\\
\n –api openai –dataset openqa –number 192 –stream<\/p>\n\u6d4b\u8bd5\u53d1\u73b0：<\/p>\n
- \u5bf9\u8bdd\u6a21\u5f0f\u4e0b，\u6d41\u5f0f\u8f93\u51fa(stream)\u7684\u7528\u6237\u4f53\u9a8c\u66f4\u597d<\/li>\n
- \u5e76\u53d1\u63d0\u5347\u5230192\u65f6，\u5ef6\u8fdf\u5f00\u59cb\u660e\u663e\u589e\u52a0<\/li>\n
- \u8f93\u51fatoken\u957f\u5ea6\u5bf9\u541e\u5410\u91cf\u5f71\u54cd\u663e\u8457：\n
- \n
- 2048 tokens\u8f93\u51fa：\u7ea610K tokens\/s\u603b\u541e\u5410\u91cf<\/li>\n
- 200 tokens\u8f93\u51fa：\u7ea625K tokens\/s\u603b\u541e\u5410\u91cf<\/li>\n
- 50 tokens\u8f93\u51fa：\u7ea635K tokens\/s\u603b\u541e\u5410\u91cf<\/li>\n<\/ul>\n<\/li>\n
5.3 \u4f7f\u7528SGLang\u6d4b\u8bd5\u6279\u5904\u7406\u6027\u80fd<\/h4>\n
SGLang\u7279\u522b\u9002\u5408\u6d4b\u8bd5\u6279\u5904\u7406\u80fd\u529b：<\/p>\n
# \u6d4b\u8bd5\u4e0d\u540c\u6279\u5904\u7406\u5927\u5c0f
\npython3 -m sglang.bench_one_batch_server –model DeepSeek-R1 \\\\
\n –base-url http:\/\/127.0.0.1:30000 –batch-size 1 \\\\
\n –input-len 128 –output-len 128<\/p>\npython3 -m sglang.bench_one_batch_server –model DeepSeek-R1 \\\\
\n –base-url http:\/\/127.0.0.1:30000 –batch-size 192 \\\\
\n –input-len 128 –output-len 128<\/p>\n\u6279\u5904\u7406\u6d4b\u8bd5\u7ed3\u679c：<\/p>\n
- \n
- \u6279\u5904\u7406\u5927\u5c0f=1：\u7ea6800 tokens\/s<\/li>\n
- \u6279\u5904\u7406\u5927\u5c0f=32：\u7ea612K tokens\/s<\/li>\n
- \u6279\u5904\u7406\u5927\u5c0f=192：\u7ea628K tokens\/s<\/li>\n
- \u6279\u5904\u7406\u5927\u5c0f=512：\u7ea632K tokens\/s（\u4f46\u5ef6\u8fdf\u589e\u52a0\u663e\u8457）<\/li>\n<\/ul>\n
\u516d\u3001\u6027\u80fd\u76d1\u63a7\u4e0e\u8c03\u4f18<\/h3>\n
\u5728\u6d4b\u8bd5\u8fc7\u7a0b\u4e2d，\u6301\u7eed\u76d1\u63a7\u7cfb\u7edf\u8d44\u6e90\u4f7f\u7528\u60c5\u51b5\u975e\u5e38\u91cd\u8981：<\/p>\n
# GPU\u76d1\u63a7
\nnvidia-smi<\/p>\n# \u7cfb\u7edf\u8d44\u6e90\u76d1\u63a7
\nhtop
\nnvtop<\/p>\n# \u8fdb\u7a0b\u76d1\u63a7
\ntop<\/p>\n\u57fa\u4e8e\u76d1\u63a7\u7ed3\u679c，\u6211\u53d1\u73b0\u4e86\u4e00\u4e9b\u6027\u80fd\u4f18\u5316\u7684\u5173\u952e\u70b9：<\/p>\n
- GPU\u5229\u7528\u7387：\u5728\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b，GPU\u5229\u7528\u7387\u7a33\u5b9a\u572885%-95%\u4e4b\u95f4\u6700\u4f73<\/li>\n
- CPU\u8d44\u6e90：\u9884\u5904\u7406\u548c\u540e\u5904\u7406\u9636\u6bb5\u4f1a\u6d88\u8017\u5927\u91cfCPU\u8d44\u6e90，\u5efa\u8bae\u4f7f\u7528\u9ad8\u9891CPU<\/li>\n
- \u5185\u5b58\u4f7f\u7528：671B\u6a21\u578b\u57288\u5361\u914d\u7f6e\u4e0b，\u6bcf\u5361\u5927\u7ea6\u9700\u898164-70GB\u663e\u5b58<\/li>\n
- \u7f51\u7edc\u5e26\u5bbd：\u9ad8\u5e76\u53d1\u573a\u666f\u4e0b\u7f51\u7edc\u53ef\u80fd\u6210\u4e3a\u74f6\u9888，\u5efa\u8bae\u4f7f\u7528\u9ad8\u901f\u7f51\u7edc\u63a5\u53e3<\/li>\n
\u4e03\u3001\u5e38\u89c1\u95ee\u9898\u4e0e\u89e3\u51b3\u65b9\u6848<\/h3>\n
\u5728\u90e8\u7f72\u8fc7\u7a0b\u4e2d，\u6211\u9047\u5230\u4e86\u4e00\u4e9b\u5e38\u89c1\u95ee\u9898，\u5206\u4eab\u89e3\u51b3\u65b9\u6848：<\/p>\n
7.1 \u8d44\u6e90\u51b2\u7a81\u95ee\u9898<\/h4>\n
\u5982\u679c\u7cfb\u7edf\u4e2d\u8fd0\u884c\u7740\u5176\u4ed6Docker\u5bb9\u5668\u6216\u8fdb\u7a0b，\u53ef\u80fd\u4f1a\u4e0e\u6a21\u578b\u90e8\u7f72\u51b2\u7a81：<\/p>\n
# \u505c\u6b62Docker\u670d\u52a1
\nsystemctl stop docker.service
\nsystemctl stop docker.socket<\/p>\n# \u7ec8\u6b62\u5360\u7528\u8d44\u6e90\u7684Python\u8fdb\u7a0b
\npkill python3
\nkill -9 [PID]<\/p>\n7.2 GPU\u4e0d\u53ef\u89c1\u95ee\u9898<\/h4>\n
\u6709\u65f6\u5bb9\u5668\u5185\u65e0\u6cd5\u6b63\u786e\u8bc6\u522bGPU：<\/p>\n
# \u68c0\u67e5NVIDIA\u9a71\u52a8\u4e0eCUDA\u7248\u672c\u517c\u5bb9\u6027
\nnvidia-smi<\/p>\n# \u786e\u4fdd\u4f7f\u7528–nv\u53c2\u6570\u542f\u52a8Apptainer
\napptainer run –nv …<\/p>\n7.3 \u6a21\u578b\u52a0\u8f7d\u7f13\u6162<\/h4>\n
DeepSeek R1 671B\u6a21\u578b\u975e\u5e38\u5927，\u9996\u6b21\u52a0\u8f7d\u53ef\u80fd\u9700\u89813-5\u5206\u949f，\u8bf7\u8010\u5fc3\u7b49\u5f85\u3002<\/p>\n
7.4 \u5185\u5b58\u6ea2\u51fa\u9519\u8bef<\/h4>\n
\u5982\u679c\u51fa\u73b0OOM\u9519\u8bef，\u53ef\u4ee5\u5c1d\u8bd5：<\/p>\n
- \n
- \u51cf\u5c0fbatch size<\/li>\n
- \u51cf\u5c0ftensor_parallel_size（\u4f46\u53ef\u80fd\u9700\u8981\u66f4\u591a\u663e\u5b58）<\/li>\n
- \u4f7f\u7528\u6a21\u578b\u91cf\u5316\u7248\u672c（\u5982FP8\u6216INT8）<\/li>\n<\/ul>\n
\u516b\u3001\u603b\u7ed3\u4e0e\u5efa\u8bae<\/h3>\n
\u7ecf\u8fc7\u4e00\u7cfb\u5217\u6d4b\u8bd5，\u6211\u5bf9DeepSeek R1 671B\u6a21\u578b\u6709\u4e86\u66f4\u6df1\u5165\u7684\u4e86\u89e3：<\/p>\n
- \u786c\u4ef6\u9700\u6c42：8\u5f20\u9ad8\u7aefGPU(\u5982H20-141G)\u662f\u57fa\u672c\u914d\u7f6e，\u5185\u5b58\u5efa\u8bae1TB\u4ee5\u4e0a<\/li>\n
- \u90e8\u7f72\u65b9\u5f0f：vLLM\u5728\u901a\u7528\u573a\u666f\u66f4\u7a33\u5b9a，SGLang\u5728\u6279\u5904\u7406\u573a\u666f\u4f18\u52bf\u660e\u663e<\/li>\n
- \u5e76\u53d1\u80fd\u529b：\u6700\u4f73\u5e76\u53d1\u6570\u5728128-192\u4e4b\u95f4，\u8d85\u8fc7\u8fd9\u4e2a\u8303\u56f4\u6027\u80fd\u63d0\u5347\u4e0d\u660e\u663e<\/li>\n
- \u54cd\u5e94\u5ef6\u8fdf：\u9996token\u5ef6\u8fdf\u7ea61-2\u79d2，\u751f\u6210\u901f\u5ea6\u5728\u5355\u8bf7\u6c42\u4e0b750-800 tokens\/s<\/li>\n
- \u541e\u5410\u91cf：\u5728\u6700\u4f73\u914d\u7f6e\u4e0b，\u6574\u4f53\u541e\u5410\u91cf\u53ef\u8fbe30K tokens\/s\u5de6\u53f3<\/li>\n
\u5982\u679c\u4f60\u8ba1\u5212\u5728\u751f\u4ea7\u73af\u5883\u90e8\u7f72DeepSeek R1 671B，\u6211\u7684\u5efa\u8bae\u662f：<\/p>\n
- \n
- \u4f7f\u7528\u5f20\u91cf\u5e76\u884c（TP）\u800c\u975e\u6d41\u6c34\u7ebf\u5e76\u884c（PP）<\/li>\n
- \u9488\u5bf9\u771f\u5b9e\u4e1a\u52a1\u573a\u666f\u8fdb\u884c\u9488\u5bf9\u6027\u6d4b\u8bd5\u548c\u4f18\u5316<\/li>\n
- \u8003\u8651\u4f7f\u7528\u6a21\u578b\u91cf\u5316\u6280\u672f\u964d\u4f4e\u8d44\u6e90\u9700\u6c42<\/li>\n
- \u5b9e\u73b0\u52a8\u6001\u6279\u5904\u7406\u4ee5\u63d0\u9ad8\u6574\u4f53\u541e\u5410\u91cf<\/li>\n<\/ul>\n
\u5199\u5728\u6700\u540e<\/h3>\n
\u901a\u8fc7\u8fd9\u6b21DeepSeek R1 671B\u7684\u90e8\u7f72\u4e4b\u65c5，\u6211\u6df1\u523b\u4f53\u4f1a\u5230\u5927\u6a21\u578b\u670d\u52a1\u5316\u7684\u6311\u6218\u548c\u4e50\u8da3\u3002\u5e0c\u671b\u672c\u6587\u80fd\u5e2e\u52a9\u66f4\u591a\u5f00\u53d1\u8005\u4e86\u89e3\u5982\u4f55\u90e8\u7f72\u548c\u6d4b\u8bd5\u8d85\u5927\u89c4\u6a21\u8bed\u8a00\u6a21\u578b，\u4e5f\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u5206\u4eab\u4f60\u7684\u7ecf\u9a8c\u548c\u95ee\u9898\u3002<\/p>\n
\u4f60\u662f\u5426\u6709\u90e8\u7f72\u8d85\u5927\u6a21\u578b\u7684\u7ecf\u5386？\u9047\u5230\u4e86\u54ea\u4e9b\u6311\u6218？\u6b22\u8fce\u5728\u8bc4\u8bba\u533a\u8ba8\u8bba！<\/p>\n
\n\u5173\u952e\u8bcd： DeepSeek R1, 671B, \u5927\u6a21\u578b\u90e8\u7f72, vLLM, SGLang, \u538b\u529b\u6d4b\u8bd5, GPU, \u5f20\u91cf\u5e76\u884c<\/p>\n","protected":false},"excerpt":{"rendered":"
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