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This guide will help you quickly get started with vLLM to perform offline batched inference and deploy an OpenAI-compatible API server.

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Prerequisites

Operating system

Linux (including WSL on Windows)

Python version

Python 3.10, 3.11, 3.12, or 3.13

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Installation

Choose your hardware platform for installation instructions:
If you are using NVIDIA GPUs, you can install vLLM using pip directly.It’s recommended to use uv, a very fast Python environment manager. Install uv and create a new environment:
uv venv --python 3.12 --seed
source .venv/bin/activate
uv pip install vllm --torch-backend=auto
uv can automatically select the appropriate PyTorch index by inspecting your CUDA driver version with --torch-backend=auto. To select a specific backend (e.g., cu126), use --torch-backend=cu126.
You can also run vLLM commands without creating a permanent environment:
uv run --with vllm vllm --help
If you prefer conda, you can also use it to manage environments:
conda create -n myenv python=3.12 -y
conda activate myenv
pip install --upgrade uv
uv pip install vllm --torch-backend=auto
For more installation options and other platforms, see the Installation Guide.

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Offline batched inference

With vLLM installed, you can start generating text for a list of input prompts (offline batch inference).

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Basic usage

The core classes you’ll use are:
  • LLM - Main class for running offline inference with the vLLM engine
  • SamplingParams - Parameters for the sampling process
from vllm import LLM, SamplingParams

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Define prompts and sampling parameters

Create a list of input prompts and configure sampling parameters: 
prompts = [
    "Hello, my name is",
    "The president of the United States is",
    "The capital of France is",
    "The future of AI is",
]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
By default, vLLM uses sampling parameters from the model’s generation_config.json on HuggingFace if it exists. This provides optimal results recommended by the model creator.To use vLLM’s default sampling parameters instead, set generation_config="vllm" when creating the LLM instance.

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Initialize the LLM engine

Create an LLM instance with your chosen model: 
llm = LLM(model="facebook/opt-125m")
By default, vLLM downloads models from HuggingFace. To use models from ModelScope, set the environment variable:
export VLLM_USE_MODELSCOPE=True

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Generate outputs

Generate text for all prompts with high throughput: 
outputs = llm.generate(prompts, sampling_params)

for output in outputs:
    prompt = output.prompt
    generated_text = output.outputs[0].text
    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
The llm.generate method does not automatically apply chat templates. For Instruct or Chat models, manually apply the template or use the llm.chat method:
from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("/path/to/chat_model")
messages_list = [
    [{"role": "user", "content": prompt}]
    for prompt in prompts
]
texts = tokenizer.apply_chat_template(
    messages_list,
    tokenize=False,
    add_generation_prompt=True,
)

outputs = llm.generate(texts, sampling_params)

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Complete example

See the full working example at: examples/offline_inference/basic/basic.py

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OpenAI-compatible server

vLLM can be deployed as a server implementing the OpenAI API protocol, allowing it to be a drop-in replacement for OpenAI API applications.

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Start the server

Launch the vLLM server with a model: 
vllm serve Qwen/Qwen2.5-1.5B-Instruct
By default, the server:
  • Starts at http://localhost:8000
  • Hosts one model at a time
  • Implements OpenAI-compatible endpoints
Use --host and --port to customize the server address:
vllm serve Qwen/Qwen2.5-1.5B-Instruct --host 0.0.0.0 --port 8080
By default, the server applies generation_config.json from the HuggingFace model repository. To disable this and use vLLM defaults, pass:
vllm serve Qwen/Qwen2.5-1.5B-Instruct --generation-config vllm

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API authentication

Enable API key checking: 
vllm serve Qwen/Qwen2.5-1.5B-Instruct --api-key sk-key1 --api-key sk-key2
Or use environment variable: 
export VLLM_API_KEY="sk-your-key"
vllm serve Qwen/Qwen2.5-1.5B-Instruct

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Query the server

List available models: 
curl http://localhost:8000/v1/models

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Completions API

Generate completions using the /v1/completions endpoint: 
curl http://localhost:8000/v1/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen2.5-1.5B-Instruct",
        "prompt": "San Francisco is a",
        "max_tokens": 7,
        "temperature": 0
    }'

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Chat completions API

The chat interface enables dynamic, back-and-forth exchanges: 
curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "Qwen/Qwen2.5-1.5B-Instruct",
        "messages": [
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Who won the world series in 2020?"}
        ]
    }'

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Attention backends

vLLM automatically selects the most performant attention backend for your system. You can manually specify a backend: 
vllm serve Qwen/Qwen2.5-1.5B-Instruct --attention-backend FLASH_ATTN

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Available backends

NVIDIA CUDA

FLASH_ATTN or FLASHINFER

AMD ROCm

TRITON_ATTN, ROCM_ATTN, ROCM_AITER_FA, ROCM_AITER_UNIFIED_ATTN, TRITON_MLA, ROCM_AITER_MLA, or ROCM_AITER_TRITON_MLA
Flash Infer is not included in pre-built wheels. Install it separately following the Flash Infer documentation.

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Next steps

Installation guide

Detailed installation for all platforms

Supported models

Browse all compatible models

API reference

Explore complete API documentation

Configuration

Learn about advanced configuration options

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