dynamo/docs/_includes/quick_start_local.rst

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SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES.
All rights reserved.
SPDX-License-Identifier: Apache-2.0
This guide covers running Dynamo **using the CLI on your local machine or VM**.
.. important::
**Looking to deploy on Kubernetes instead?**
See the `Kubernetes Installation Guide <../kubernetes/installation_guide.html>`_
and `Kubernetes Quickstart <../kubernetes/README.html>`_ for cluster deployments.
**Install Dynamo**
**Option A: Containers (Recommended)**
Containers have all dependencies pre-installed. No setup required.
.. code-block:: bash
# 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
.. tip::
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 <../reference/release-artifacts.html#container-images>`_ for available
versions and backend guides for run instructions: `SGLang <../backends/sglang/README.html>`_ |
`TensorRT-LLM <../backends/trtllm/README.html>`_ | `vLLM <../backends/vllm/README.html>`_
**Option B: Install from PyPI**
.. code-block:: bash
# 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**
.. code-block:: bash
sudo apt install python3-dev
uv pip install --prerelease=allow "ai-dynamo[sglang]"
.. note::
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**
.. code-block:: bash
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]"
.. note::
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.html>`_
for details.
**vLLM**
.. code-block:: bash
sudo apt install python3-dev libxcb1
uv pip install --prerelease=allow "ai-dynamo[vllm]"
**Run Dynamo**
.. tip::
**(Optional)** Before running Dynamo, verify your system configuration:
``python3 deploy/sanity_check.py``
Start the frontend, then start a worker for your chosen backend.
.. tip::
To run in a single terminal (useful in containers), append ``> logfile.log 2>&1 &``
to run processes in background. Example: ``python3 -m dynamo.frontend --store-kv file > dynamo.frontend.log 2>&1 &``
.. code-block:: bash
# Start the OpenAI compatible frontend (default port is 8000)
# --store-kv file avoids needing etcd (frontend and workers must share a disk)
python3 -m dynamo.frontend --store-kv file
In another terminal (or same terminal if using background mode), start a worker:
**SGLang**
.. code-block:: bash
python3 -m dynamo.sglang --model-path Qwen/Qwen3-0.6B --store-kv file
**TensorRT-LLM**
.. code-block:: bash
python3 -m dynamo.trtllm --model-path Qwen/Qwen3-0.6B --store-kv file
**vLLM**
.. code-block:: bash
python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B --store-kv file \
--kv-events-config '{"enable_kv_cache_events": false}'
.. note::
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.
**Test Your Deployment**
.. code-block:: bash
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}'