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How to serve an open LLM behind an OpenAI-compatible API with vLLM

Turn a Hugging Face model into a local chat endpoint that existing OpenAI client code can call.

Who it is forA developer or platform engineer who wants apps to call a self-hosted open-weight model instead of a hosted API.

The problemExisting app code is written against an OpenAI-style API, but the team wants the model to run on its own hardware.

The outcomeA running server at a local URL that answers chat requests from standard OpenAI-style clients.

What you need first

  • Python with uv or pip installed
  • A supported NVIDIA, AMD or Intel GPU, or an x86, ARM or PowerPC CPU
  • A Hugging Face model you are allowed to download and use

Steps

  1. Install vLLM with uv pip install vllm.
  2. Run vllm download-kernels to fetch precompiled kernels for faster startup.
  3. Start the server with vllm serve <model-id>; the README mentions an OpenAI-compatible API server but does not show this exact command, so check the Quickstart. (inferred, check the README)
  4. Send one chat request to the server from your app using an OpenAI-style client.
  5. Confirm the reply is returned and note the server's throughput and latency for your model.

Prompt for your agent

Using the vllm-project/vllm repo, install vLLM, start an OpenAI-compatible server for <model-id>, and send one chat request. Report the server URL and the model's reply. Verify that the model name appears in the server's models list before you finish.

Caveat. The README does not show the serve command, so the exact invocation must be confirmed in the official Quickstart before relying on it.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for vllm-project/vllm and "Serve an open LLM behind an OpenAI-compatible API"

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claude mcp add --transport http deduped https://dedupedai.com/mcp

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