vLLM is open-source software for running inference with open-source models and serving them to applications. Its quickstart covers two workflows: offline batched inference and an online server that accepts requests using the OpenAI API protocol. To try the online workflow, install the version that matches your operating system and hardware, start a model with vllm serve, then send a request to the server running on your machine.
What vLLM does—and what it does not include
vLLM runs a model to generate responses, either for batches of prompts processed offline or through a server that accepts requests from clients. The server supports documented OpenAI API protocol-compatible endpoints, so an application designed to send requests in that format can be configured to use your vLLM server instead.
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This compatibility is a local serving option; it does not include or connect to the hosted OpenAI service. You need a model and a supported environment of your own. The quickstart examples demonstrate a workflow, not a guarantee that a particular model will fit every device.
Check your system before installing
The standard quickstart lists Linux and Python 3.10–3.13 as prerequisites. Its NVIDIA example uses Python 3.12. Installation depends on the operating system, accelerator, runtime, drivers, and available package builds, so use the current installation guide for your specific platform rather than treating one command as universal.
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- NVIDIA: The GPU guide lists compute capability 7.5 or higher and gives examples including T4, RTX20xx, A100, L4, H100, and B200. These examples are not a promise that every listed card can run every model or workload.
- AMD and Intel GPUs: The documentation provides separate ROCm and Intel XPU installation paths.
- Other accelerators: Separate paths are documented for Google TPU and Ascend NPU.
- Apple Silicon: The quickstart describes acceleration through vLLM-Metal, which uses MLX and models optimized for that ecosystem.
- CPU: The CPU guide covers basic inference and serving on x86 and Arm, as well as experimental native macOS CPU support.
These options are not interchangeable. Check the live vLLM documentation for the hardware, software, and package combination that applies to your machine; supported requirements can change.
Install vLLM for the NVIDIA example
The quickstart recommends uv for environment management. For its Linux/NVIDIA path, the documented example creates a Python 3.12 virtual environment and installs vLLM with automatic PyTorch backend selection:
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uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install vllm --torch-backend=autoUse this only when the NVIDIA setup matches your system. For AMD, Intel, CPU, Apple Silicon, or another accelerator, follow that platform’s current instructions in the installation guide. The quickstart also documents uv run --with vllm for invoking the CLI without first creating a permanent environment.
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With vLLM installed and a model suitable for your hardware selected, start the quickstart’s example model:
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vllm serve Qwen/Qwen2.5-1.5B-InstructThe server defaults to http://localhost:8000. Its model-list endpoint provides a simple check that the server is responding. In another terminal, run:
curl http://localhost:8000/v1/modelsTo choose a different listening address or port, use the documented --host and --port options with the server command. The quickstart covers model listing, completions, and chat completions; the request below exercises the chat-completions endpoint:
curl http://localhost:8000/v1/chat/completions
-H "Content-Type: application/json"
-d '{
"model": "Qwen/Qwen2.5-1.5B-Instruct",
"messages": [{"role": "user", "content": "Explain what vLLM does in one sentence."}]
}'For an application using the OpenAI Python package, the quickstart shows pointing its base URL at http://localhost:8000/v1. The client then sends requests to the local vLLM server using the compatible protocol.
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Know the server’s defaults and controls
- One model at a time: The server hosts one model at a time. To serve a different model, use the appropriate model-serving setup for that model.
- Generation configuration: If the model repository includes
generation_config.json, vLLM applies it by default. Use--generation-config vllmto disable that behavior. - API-key checks: The quickstart documents configuring a key with
--api-keyor theVLLM_API_KEYenvironment variable.
Choose the workflow that fits your task
| Workflow | How it works | Useful when |
|---|---|---|
| Offline batched inference | Processes prompts in batches without exposing an online request server. | You have a batch of inputs to run as a job. |
| Online serving | Runs a server that accepts client requests through documented API-compatible endpoints. | An application needs to send requests to a running model service. |
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