tensor_parallel_size=4, max_num_seqs=2048 ) llm.generate(prompts=prompts) 用了4个GPU推理,将模型分割到4个GPU上,用的是张量并行,即模型的参数在每个GPU上独立计算,然后再进行聚合。张量并行需要在GPU之间进行传输,增加了通信量。VLLM对于多卡推理,使用Ray做GPU调度管理。
This requires the whole model to be able to fit on to one GPU (as per data parallel's usual implementation) and will doubtless have a higher RAM overhead (I haven't checked, but it shouldn't be massive depending on your text size), but it does run seem to run at roughly N times...
如何下载DeepSeek 671B 模型文件请参考努力犯错玩AI:生产环境H200部署DeepSeek 671B 满血版全流程实战(一):系统初始化。 vllm serve /data/DeepSeek-R1 --tensor-parallel-size 8 --max-model-len 16384 --port 8102 --trust-remote-code --served-model-name deepseek-r1 --enable-chunked-prefill --max...
Vllm的参数很多,上面这串命令使用的参数就是我们常用的,这几个参数还是挺影响模型运行的性能的, --tensor-parallel-size是用于分布式推理的参数,设置为一就是单卡推理,也就是8卡推理(ollama的在文末),单节点多卡推理是说一台机子上有多个GPU推理,多节点多卡推理是说多个机子多GPU推理。 下面参数影响篇幅有限,具...
分布式推理实验,要运行多 GPU 服务,请在启动服务器时传入 --tensor-parallel-size 参数。 例如,要在 2 个 GPU 上运行 API 服务器: python -m vllm.entrypoints.openai.api_server --model /root/autodl-tmp/Yi-6B-Chat --dtype auto --api-key token-agiclass --trust-remote-code --port 6006 --ten...
] # 输入prompts sampling_params = SamplingParams(temperature=0.8, top_k=50) # 采样策略 llm = LLM(model="facebook/opt-125m", tensor_parallel_size=2) # 初始化 LLM outputs = llm.generate(prompts, sampling_params) # 完成推理 for output in outputs: prompt = output.prompt generated_text =...
llm = LLM(model="facebook/opt-125m", tensor_parallel_size=2) # 初始化 LLM outputs = llm.generate(prompts, sampling_params) # 完成推理 for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"Prompt: {prompt!r}, Generated text: {generated_text!r...
tensor_parallel_size=2, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, quantization_param_path=None, device_config=cuda, decoding_config=DecodingConfig(guided_decoding_backend='outlines'), seed=0, served_model_name=/scratch/pbs.5401450.kman.restech....
设置总的 tensor-parallel-sizecd /root/.cache/huggingface/Qwen# 确认模型挂载的目录vllm serve "Qwen2.5-1.5B-Instruct"--tensor-parallel-size 2--max-model-len 128--gpu_memory_utilization=0.5root@user:~/.cache/huggingface/Qwen# vllm serve "Qwen2.5-1.5B-Instruct" --tensor-parallel-size ...
vllm serve /home/models/DeepSeek-R1-Distill-Qwen-7B\--served_model_nameDeepSeek-R1-Distill-Qwen-7B\--tensor-parallel-size2\--dtypefloat16\--gpu-memory-utilization0.95\--max-model-len65536\--trust-remote-code 1. 2. 3. 4. 5.