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How to serve a 7-8B model behind an OpenAI-compatible endpoint with Time to First Token

Deploy vLLM on one rented 24GB GPU and call it with OpenAI-style requests.

Who it is forBackend engineer who needs a self-hosted LLM endpoint and has no prior serving experience.

The problemRunning an LLM in production starts with GPU setup, serving, and API compatibility, and those steps block any real work.

The outcomeA working endpoint serving Llama-3.1-8B-Instruct or Qwen2.5-7B that existing OpenAI-style client code can call.

What you need first

  • Python and command-line comfort
  • One rented GPU with about 24GB of memory for a 7-8B model, billed by the half hour
  • Docker for the Prometheus and Grafana stack
  • An H100 for the 1000-concurrent load test in week 5

Steps

  1. Rent one 24GB GPU by the half hour, for example on RunPod or Modal.
  2. pip install vllm
  3. Start the server with vllm serve meta-llama/Llama-3.1-8B-Instruct, using vLLM's OpenAI-compatible server as the README describes. (inferred, check the README)
  4. Send one chat request to the endpoint from an OpenAI-compatible client and confirm a response comes back.
  5. Save the launch command, since the roadmap reuses it for the following ten weeks.

Prompt for your agent

Using the patchy631/time-to-first-token repo as the guide, set up vLLM on <GPU host> serving Qwen2.5-7B behind an OpenAI-compatible API. Expected output: a chat completion response from the endpoint. Verify that the response contains a non-empty assistant message.

Caveat. The README does not give the exact launch command, so confirm the vLLM flags against the version you install.

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