LLM Deployment using vLLM

Dynamo vLLM integrates vLLM engines into Dynamo’s distributed runtime, enabling disaggregated serving, KV-aware routing, and request cancellation while maintaining full compatibility with vLLM’s native engine arguments. Dynamo leverages vLLM’s native KV cache events, NIXL-based transfer mechanisms, and metric reporting to enable KV-aware routing and P/D disaggregation.

Installation

Install Latest Release

We recommend using uv to install:

$
uv venv --python 3.12 --seed
$
uv pip install "ai-dynamo[vllm]"

This installs Dynamo with the compatible vLLM version.

Container

We have public images available on NGC Catalog:

$
docker pull nvcr.io/nvidia/ai-dynamo/vllm-runtime:<version>
$
./container/run.sh -it --framework VLLM --image nvcr.io/nvidia/ai-dynamo/vllm-runtime:<version>

$
python container/render.py --framework vllm --output-short-filename
$
docker build -f container/rendered.Dockerfile -t dynamo:latest-vllm .

$
./container/run.sh -it --framework VLLM [--mount-workspace]

Development Setup

For development, use the devcontainer which has all dependencies pre-installed.

Feature Support Matrix

Quick Start

Start infrastructure services for local development:

$
docker compose -f deploy/docker-compose.yml up -d

Launch an aggregated serving deployment:

$
cd $DYNAMO_HOME/examples/backends/vllm
$
bash launch/agg.sh

Next Steps

• Reference Guide: Configuration, arguments, and operational details
• Examples: All deployment patterns with launch scripts
• KV Cache Offloading: KVBM, LMCache, and FlexKV integrations
• Observability: Metrics and monitoring
• vLLM-Omni: Multimodal model serving
• Kubernetes Deployment: Kubernetes deployment guide
• vLLM Documentation: Upstream vLLM serve arguments