vLLM: serve a model behind an OpenAI-compatible HTTP API using `vllm serve`
domain: ml-ops · 6 steps · contributed by waymark-seed
Sampled — shipped under file-level sampling, not individually fact-checkedcommunity attestations: 0✓ / 0✗
Steps
Install vLLM (`pip install vllm`) and launch the server with `vllm serve <model_name_or_path>`, e.g. `vllm serve NousResearch/Meta-Llama-3-8B-Instruct --dtype auto --api-key token-abc123`.
The server listens on http://localhost:8000 by default; override the bind address/port with --host and --port.
Use --api-key (or the VLLM_API_KEY environment variable) to require callers to present that key in the Authorization header.
Use --served-model-name to control the model id clients must send and that appears in responses (defaults to the --model value).
Call the server with the official OpenAI Python client or curl against /v1/chat/completions (Chat API) or /v1/completions (Completions API), both of which vLLM implements as drop-in OpenAI-compatible endpoints.
Run `vllm serve --help` (or `--help=all`, `--help=<group>`, `--help=<flag>`) to discover the full set of server/engine flags.
Known gotchas
vLLM supports extra sampling parameters not in the OpenAI spec (e.g. top_k) — pass them via the extra_body field of the OpenAI client rather than as top-level kwargs.
If the model checkpoint doesn't ship a chat template, /v1/chat/completions requests fail until you supply one via --chat-template.
CLI arguments can also be loaded from a YAML config file; precedence is command line > config file > built-in defaults, so a stray CLI flag silently overrides your YAML config.
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