dynamo/docs/pages/getting-started/quickstart.md

title
Quickstart

This guide covers running Dynamo using the CLI on your local machine or VM.

**Looking to deploy on Kubernetes instead?** See the [Kubernetes Installation Guide](../kubernetes/installation-guide.md) and [Kubernetes Quickstart](../kubernetes/README.md) for cluster deployments.

Install Dynamo

Option A: Containers (Recommended)

Containers have all dependencies pre-installed. No setup required.

# SGLang
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.8.1

# TensorRT-LLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.8.1

# vLLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.1

To run frontend and worker in the same container, either:

• Run processes in background with & (see Run Dynamo section below), or
• Open a second terminal and use docker exec -it <container_id> bash

See Release Artifacts for available versions and backend guides for run instructions: SGLang | TensorRT-LLM | vLLM

Option B: Install from PyPI

# Install uv (recommended Python package manager)
curl -LsSf https://astral.sh/uv/install.sh | sh

# Create virtual environment
uv venv venv
source venv/bin/activate
uv pip install pip

Install system dependencies and the Dynamo wheel for your chosen backend:

SGLang

sudo apt install python3-dev
uv pip install --prerelease=allow "ai-dynamo[sglang]"

For CUDA 13 (B300/GB300), the container is recommended. See [SGLang install docs](https://docs.sglang.io/get_started/install.html) for details.

TensorRT-LLM

sudo apt install python3-dev
pip install torch==2.9.0 torchvision --index-url https://download.pytorch.org/whl/cu130
pip install --pre --extra-index-url https://pypi.nvidia.com "ai-dynamo[trtllm]"

TensorRT-LLM requires `pip` due to a transitive Git URL dependency that `uv` doesn't resolve. We recommend using the TensorRT-LLM container for broader compatibility. See the [TRT-LLM backend guide](../backends/trtllm/README.md) for details.

vLLM

sudo apt install python3-dev libxcb1
uv pip install --prerelease=allow "ai-dynamo[vllm]"

Run Dynamo

**(Optional)** Before running Dynamo, verify your system configuration: `python3 deploy/sanity_check.py`

Start the frontend, then start a worker for your chosen backend.

To run in a single terminal (useful in containers), append `> logfile.log 2>&1 &` to run processes in background. Example: `python3 -m dynamo.frontend --discovery-backend file > dynamo.frontend.log 2>&1 &`

# Start the OpenAI compatible frontend (default port is 8000)
# --discovery-backend file avoids needing etcd (frontend and workers must share a disk)
python3 -m dynamo.frontend --discovery-backend file

In another terminal (or same terminal if using background mode), start a worker:

SGLang

python3 -m dynamo.sglang --model-path Qwen/Qwen3-0.6B --discovery-backend file

TensorRT-LLM

python3 -m dynamo.trtllm --model-path Qwen/Qwen3-0.6B --discovery-backend file

vLLM

python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B --discovery-backend file \
  --kv-events-config '{"enable_kv_cache_events": false}'

For dependency-free local development, disable KV event publishing (avoids NATS):

• vLLM: Add --kv-events-config '{"enable_kv_cache_events": false}'
• SGLang: No flag needed (KV events disabled by default)
• TensorRT-LLM: No flag needed (KV events disabled by default)

TensorRT-LLM only: The warning Cannot connect to ModelExpress server/transport error. Using direct download. is expected and can be safely ignored.

**Deprecation notice:** vLLM automatically enables KV event publishing when prefix caching is active. In a future release, this will change — KV events will be disabled by default for all backends. Start using `--kv-events-config` explicitly to prepare.

Test Your Deployment

curl localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "Qwen/Qwen3-0.6B",
       "messages": [{"role": "user", "content": "Hello!"}],
       "max_tokens": 50}'