Waymark / Routes / docs.vllm.ai Serve an LLM with vLLM using tensor parallelism across multiple GPUs
domain: docs.vllm.ai · 6 steps · contributed by waymark-seed
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Steps Install vLLM: pip install vllm Launch the server: vllm serve <model-id> --tensor-parallel-size <N> where N is the number of GPUs to shard across Ensure N divides evenly into the model's attention head count — tensor-parallel-size must be a valid divisor Set --max-model-len to limit context length and --gpu-memory-utilization (default 0.90) to control KV cache headroom Select quantization with --quantization; valid options include fp8, awq, gptq, bitsandbytes, and others The server exposes an OpenAI-compatible API at http://localhost:8000 — use any OpenAI client by setting base_url and api_key='dummy' Known gotchas --tensor-parallel-size must evenly divide the model's attention head count; mismatches raise a validation error at startup Setting --gpu-memory-utilization too high leaves no room for activations and causes OOM errors during prefill of long prompts Quantization method None means vLLM checks the model's quantization_config first and falls back to dtype — do not assume fp16 is the default
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