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Online Serving

vLLM provides an HTTP server that is compatible with many interfaces!

OpenAI-Compatible Server

We currently support the following OpenAI APIs:

Anthropic APIs

  • Anthropic messages API (/v1/messages)

Cohere APIs

SageMaker APIs

  • /invocations - SageMaker-compatible endpoint (routes to the same inference functions as /v1 endpoints)

Pooling APIs

For further details on pooling models, please refer to this page.

Speech to Text APIs

For further details on speech to text, please refer to this page.

Disaggregated APIs

Renderer APIs

For further details on renderer APIs, please refer to this page.

Custom APIs

Utility APIs

  • /tokenize - Tokenize text
  • /detokenize - Detokenize tokens
  • /health - Health check
  • /ping - SageMaker health check
  • /version - Version information
  • /load - Server load metrics

Sleep Mode APIs

For further details on sleep mode, please refer to this page.

  • /sleep - Put engine to sleep (causes denial of service)
  • /wake_up - Wake engine from sleep
  • /is_sleeping - Check if engine is sleeping
  • /collective_rpc - Execute arbitrary RPC methods on the engine (extremely dangerous)

Chat Template

In order for the language model to support chat protocol, vLLM requires the model to include a chat template in its tokenizer configuration. The chat template is a Jinja2 template that specifies how roles, messages, and other chat-specific tokens are encoded in the input.

An example chat template for NousResearch/Meta-Llama-3-8B-Instruct can be found here

Some models do not provide a chat template even though they are instruction/chat fine-tuned. For those models, you can manually specify their chat template in the --chat-template parameter with the file path to the chat template, or the template in string form. Without a chat template, the server will not be able to process chat and all chat requests will error.

vllm serve <model> --chat-template ./path-to-chat-template.jinja

vLLM community provides a set of chat templates for popular models. You can find them under the examples directory.

With the inclusion of multi-modal chat APIs, the OpenAI spec now accepts chat messages in a new format which specifies both a type and a text field. An example is provided below:

completion = client.chat.completions.create(
    model="NousResearch/Meta-Llama-3-8B-Instruct",
    messages=[
        {
            "role": "user",
            "content": [
                {"type": "text", "text": "Classify this sentiment: vLLM is wonderful!"},
            ],
        },
    ],
)

Most chat templates for LLMs expect the content field to be a string, but there are some newer models like meta-llama/Llama-Guard-3-1B that expect the content to be formatted according to the OpenAI schema in the request. vLLM provides best-effort support to detect this automatically, which is logged as a string like "Detected the chat template content format to be...", and internally converts incoming requests to match the detected format, which can be one of:

  • "string": A string.
    • Example: "Hello world"
  • "openai": A list of dictionaries, similar to OpenAI schema.
    • Example: [{"type": "text", "text": "Hello world!"}]

If the result is not what you expect, you can set the --chat-template-content-format CLI argument to override which format to use.

Offline API Documentation

The FastAPI /docs endpoint requires an internet connection by default. To enable offline access in air-gapped environments, use the --enable-offline-docs flag:

vllm serve NousResearch/Meta-Llama-3-8B-Instruct --enable-offline-docs

Ray Serve LLM

Ray Serve LLM enables scalable, production-grade serving of the vLLM engine. It integrates tightly with vLLM and extends it with features such as auto-scaling, load balancing, and back-pressure.

Key capabilities:

  • Exposes an OpenAI-compatible HTTP API as well as a Pythonic API.
  • Scales from a single GPU to a multi-node cluster without code changes.
  • Provides observability and autoscaling policies through Ray dashboards and metrics.

The following example shows how to deploy a large model like DeepSeek R1 with Ray Serve LLM: examples/ray_serving/ray_serve_deepseek.py.

Learn more about Ray Serve LLM with the official Ray Serve LLM documentation.