Quickstart

Quickstart#

This guide will help you quickly get started with vLLM to perform:

Prerequisites#

  • OS: Linux

  • Python: 3.9 – 3.12

Installation#

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, to create and manage Python environments. Please follow the documentation to install uv. After installing uv, you can create a new Python environment and install vLLM using the following commands:

uv venv myenv --python 3.12 --seed
source myenv/bin/activate
uv pip install vllm

You can also use conda to create and manage Python environments.

conda create -n myenv python=3.12 -y
conda activate myenv
pip install vllm

Note

For non-CUDA platforms, please refer here for specific instructions on how to install vLLM.

Offline Batched Inference#

With vLLM installed, you can start generating texts for list of input prompts (i.e. offline batch inferencing). See the example script: examples/offline_inference/basic.py

The first line of this example imports the classes LLM and SamplingParams:

  • LLM is the main class for running offline inference with vLLM engine.

  • SamplingParams specifies the parameters for the sampling process.

from vllm import LLM, SamplingParams

The next section defines a list of input prompts and sampling parameters for text generation. The sampling temperature is set to 0.8 and the nucleus sampling probability is set to 0.95. You can find more information about the sampling parameters here.

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)

The LLM class initializes vLLM’s engine and the OPT-125M model for offline inference. The list of supported models can be found here.

llm = LLM(model="facebook/opt-125m")

Note

By default, vLLM downloads models from HuggingFace. If you would like to use models from ModelScope, set the environment variable VLLM_USE_MODELSCOPE before initializing the engine.

Now, the fun part! The outputs are generated using llm.generate. It adds the input prompts to the vLLM engine’s waiting queue and executes the vLLM engine to generate the outputs with high throughput. The outputs are returned as a list of RequestOutput objects, which include all of the output tokens.

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}")

OpenAI-Compatible Server#

vLLM can be deployed as a server that implements the OpenAI API protocol. This allows vLLM to be used as a drop-in replacement for applications using OpenAI API. By default, it starts the server at http://localhost:8000. You can specify the address with --host and --port arguments. The server currently hosts one model at a time and implements endpoints such as list models, create chat completion, and create completion endpoints.

Run the following command to start the vLLM server with the Qwen2.5-1.5B-Instruct model:

vllm serve Qwen/Qwen2.5-1.5B-Instruct

Note

By default, the server uses a predefined chat template stored in the tokenizer. You can learn about overriding it here.

This server can be queried in the same format as OpenAI API. For example, to list the models:

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

You can pass in the argument --api-key or environment variable VLLM_API_KEY to enable the server to check for API key in the header.

OpenAI Completions API with vLLM#

Once your server is started, you can query the model with input prompts:

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
    }'

Since this server is compatible with OpenAI API, you can use it as a drop-in replacement for any applications using OpenAI API. For example, another way to query the server is via the openai Python package:

from openai import OpenAI

# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
completion = client.completions.create(model="Qwen/Qwen2.5-1.5B-Instruct",
                                      prompt="San Francisco is a")
print("Completion result:", completion)

A more detailed client example can be found here: examples/online_serving/openai_completion_client.py

OpenAI Chat Completions API with vLLM#

vLLM is designed to also support the OpenAI Chat Completions API. The chat interface is a more dynamic, interactive way to communicate with the model, allowing back-and-forth exchanges that can be stored in the chat history. This is useful for tasks that require context or more detailed explanations.

You can use the create chat completion endpoint to interact with the model:

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?"}
        ]
    }'

Alternatively, you can use the openai Python package:

from openai import OpenAI
# Set OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"

client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)

chat_response = client.chat.completions.create(
    model="Qwen/Qwen2.5-1.5B-Instruct",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Tell me a joke."},
    ]
)
print("Chat response:", chat_response)