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95,106 stars · Python

How to use vLLM: what you can do with it

vLLM is a fast, easy-to-use library for LLM inference and serving, with an OpenAI-compatible API server.

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What do you want to do?

4 use cases

  • Serve an open LLM behind an OpenAI-compatible API

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

    View prompt
    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.

  • Get structured JSON output from a self-hosted model

    Constrain model replies to a schema so downstream code can parse them reliably.

    View prompt
    Using the vllm-project/vllm repo, set up a served model <model-id> with structured output enabled. Send the sample input <sample-text> with a JSON schema and return the parsed object. Verify that the output parses as JSON and contains every required field.

  • Serve several LoRA fine-tunes from one base model

    Run multiple adapters on a single loaded model instead of loading a full copy for each.

    View prompt
    Using the vllm-project/vllm repo, serve the base model <base-model-id> with the LoRA adapters at <adapter-paths>. Send the same prompt to each adapter and the base model, then report the differences. Verify that each adapter name returns a reply.

  • Create text embeddings from a served retrieval model

    Use an embedding model served by vLLM to turn documents and queries into vectors.

    View prompt
    Using the vllm-project/vllm repo, serve the embedding model <embedding-model-id> and create vectors for the documents in <documents-file>. Run the query <query-text> and return the top three documents by similarity. Verify that the top result is relevant to the query.

Quick start with vLLM

  1. Check you have what it needs: 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.
  2. Open vllm-project/vllm on GitHub and read the README and license.
  3. Install vLLM with uv pip install vllm.
  4. Run vllm download-kernels to fetch precompiled kernels for faster startup.
  5. 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)
  6. Send one chat request to the server from your app using an OpenAI-style client.

These steps come from our top use case, Serve an open LLM behind an OpenAI-compatible API, which also has a copy-paste agent prompt.

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vLLM alternatives · Best AI repos for Software and DevOps

About this repo

  • Who it is forBuilt for engineers and ML teams who need to run open-weight language models on their own GPUs or CPUs at high throughput.
  • Why it is hot+1,327 stars this week, 95k total, picked up by one AI source we track
  • LanguagePython
  • Sourcegithub.com/vllm-project/vllm · Back to Hot repos

Requirements

  • 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

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Questions about vLLM

What is vLLM?

vLLM is a fast, easy-to-use library for LLM inference and serving, with an OpenAI-compatible API server. It is an open-source project on GitHub: https://github.com/vllm-project/vllm.

Who is vLLM for?

Built for engineers and ML teams who need to run open-weight language models on their own GPUs or CPUs at high throughput.

What can I do with vLLM?

4 use cases so far, for example: Serve an open LLM behind an OpenAI-compatible API; Get structured JSON output from a self-hosted model; Serve several LoRA fine-tunes from one base model. Each has steps and a copy-paste agent prompt.

What do I need to run vLLM?

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.

What are alternatives to vLLM?

Other hot AI repos in Software and DevOps: Omarchy Local AI, Hermes Plan Mode, Agent 2, Agnes Harness, Skills.

Is vLLM safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.