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docs: refactor Dynamo readme.md and quick_start_local.rst (#5649) 


Signed-off-by: default avatarDan Gil <dagil@nvidia.com>
Co-authored-by: default avatarCursor <cursoragent@cursor.com>
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README.md
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......@@ -44,34 +44,35 @@ Dynamo is inference engine agnostic (supports TRT-LLM, vLLM, SGLang) and provide
- **Accelerated Data Transfer** – Reduces inference response time using NIXL
- **KV Cache Offloading** – Leverages multiple memory hierarchies for higher throughput
<p align="center">
<img src="./docs/images/frontpage-architecture.png" alt="Dynamo architecture" width="600" />
</p>
Built in Rust for performance and Python for extensibility, Dynamo is fully open-source with an OSS-first development approach.
## Framework Support Matrix
## Backend Feature Support
| Feature | [vLLM](docs/backends/vllm/README.md) | [SGLang](docs/backends/sglang/README.md) | [TensorRT-LLM](docs/backends/trtllm/README.md) |
| -------------------------------------------------------------------- | :--: | :----: | :----------: |
| | [SGLang](docs/backends/sglang/README.md) | [TensorRT-LLM](docs/backends/trtllm/README.md) | [vLLM](docs/backends/vllm/README.md) |
|---|:----:|:----------:|:--:|
| **Best For** | High-throughput serving | Maximum performance | Broadest feature coverage |
| [**Disaggregated Serving**](docs/design_docs/disagg_serving.md) | ✅ | ✅ | ✅ |
| [**KV-Aware Routing**](docs/router/kv_cache_routing.md) | ✅ | ✅ | ✅ |
| [**SLA-Based Planner**](docs/planner/sla_planner.md) | ✅ | ✅ | ✅ |
| [**KVBM**](docs/kvbm/kvbm_architecture.md) | ✅ | 🚧 | ✅ |
| [**KVBM**](docs/kvbm/kvbm_architecture.md) | 🚧 | ✅ | ✅ |
| [**Multimodal**](docs/multimodal/index.md) | ✅ | ✅ | ✅ |
| [**Tool Calling**](docs/agents/tool-calling.md) | ✅ | ✅ | ✅ |
> **[Full Feature Matrix →](docs/reference/feature-matrix.md)** — Detailed compatibility including LoRA, Request Migration, Speculative Decoding, and feature interactions.
## Dynamo Architecture
<p align="center">
<img src="./docs/images/frontpage-architecture.png" alt="Dynamo architecture" width="600" />
</p>
> **[Architecture Deep Dive →](docs/design_docs/architecture.md)**
## Latest News
- [12/05] [Moonshot AI's Kimi K2 achieves 10x inference speedup with Dynamo on GB200](https://quantumzeitgeist.com/kimi-k2-nvidia-ai-ai-breakthrough/)
- [12/02] [Mistral AI runs Mistral Large 3 with 10x faster inference using Dynamo](https://www.marktechpost.com/2025/12/02/nvidia-and-mistral-ai-bring-10x-faster-inference-for-the-mistral-3-family-on-gb200-nvl72-gpu-systems/)
- [12/01] [InfoQ: NVIDIA Dynamo simplifies Kubernetes deployment for LLM inference](https://www.infoq.com/news/2025/12/nvidia-dynamo-kubernetes/)
- [11/20] [Dell integrates PowerScale with Dynamo's NIXL for 19x faster TTFT](https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releases~usa~2025~11~dell-technologies-and-nvidia-advance-enterprise-ai-innovation.htm)
- [11/20] [WEKA partners with NVIDIA on KV cache storage for Dynamo](https://siliconangle.com/2025/11/20/nvidia-weka-kv-cache-solution-ai-inferencing-sc25/)
- [11/13] [Dynamo Office Hours Playlist](https://www.youtube.com/playlist?list=PL5B692fm6--tgryKu94h2Zb7jTFM3Go4X)
- [10/16] [How Baseten achieved 2x faster inference with NVIDIA Dynamo](https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/)
## Get Started
......@@ -79,62 +80,81 @@ Built in Rust for performance and Python for extensibility, Dynamo is fully open
|------|----------|------|--------------|
| [**Local Quick Start**](#local-quick-start) | Test on a single machine | ~5 min | 1 GPU, Ubuntu 24.04 |
| [**Kubernetes Deployment**](#kubernetes-deployment) | Production multi-node clusters | ~30 min | K8s cluster with GPUs |
| [**Building from Source**](#building-from-source) | Contributors and development | ~15 min | Ubuntu, Rust, Python |
## Contributing
Want to help shape the future of distributed LLM inference? We welcome contributors at all levels—from doc fixes to new features.
- **[Contributing Guide](CONTRIBUTING.md)** – How to get started
- **[Report a Bug](https://github.com/ai-dynamo/dynamo/issues/new?template=bug_report.yml)** – Found an issue?
- **[Feature Request](https://github.com/ai-dynamo/dynamo/issues/new?template=feature_request.yml)** – Have an idea?
Want to help shape the future of distributed LLM inference? See the **[Contributing Guide](CONTRIBUTING.md)**.
# Local Quick Start
The following examples require a few system level packages.
Recommended to use Ubuntu 24.04 with a x86_64 CPU. See [docs/reference/support-matrix.md](docs/reference/support-matrix.md)
## 1. Initial Setup
## Install Dynamo
The Dynamo team recommends the `uv` Python package manager, although any way works. Install uv:
### Option A: Containers (Recommended)
```
curl -LsSf https://astral.sh/uv/install.sh | sh
```
Containers have all dependencies pre-installed. No setup required.
### Install Python Development Headers
```bash
# SGLang
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/sglang-runtime:0.8.1
Backend engines require Python development headers for JIT compilation. Install them with:
# TensorRT-LLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/tensorrtllm-runtime:0.8.1
```bash
sudo apt install python3-dev
# vLLM
docker run --gpus all --network host --rm -it nvcr.io/nvidia/ai-dynamo/vllm-runtime:0.8.1
```
## 2. Select an Engine
> **Tip:** To run frontend and worker in the same container, either run processes in background with `&` (see below), or open a second terminal and use `docker exec -it <container_id> bash`.
We publish Python wheels specialized for each of our supported engines: vllm, sglang, and trtllm. The examples that follow use SGLang; continue reading for other engines.
See [Release Artifacts](docs/reference/release-artifacts.md#container-images) for available versions.
```
### Option B: Install from PyPI
The Dynamo team recommends the `uv` Python package manager, although any way works.
```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
```
# Choose one
uv pip install "ai-dynamo[sglang]" #replace with [vllm], [trtllm], etc.
Install system dependencies and the Dynamo wheel for your chosen backend:
**SGLang**
```bash
sudo apt install python3-dev
uv pip install "ai-dynamo[sglang]"
```
## 3. Run Dynamo
> **Note:** For CUDA 13 (B300/GB300), the container is recommended. See [SGLang install docs](https://docs.sglang.ai/start/install.html) for details.
### Sanity Check (Optional)
**TensorRT-LLM**
Before trying out Dynamo, you can verify your system configuration and dependencies:
```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](docs/reference/release-artifacts.md#container-images) for broader compatibility.
**vLLM**
```bash
python3 deploy/sanity_check.py
sudo apt install python3-dev libxcb1
uv pip install "ai-dynamo[vllm]"
```
This is a quick check for system resources, development tools, LLM frameworks, and Dynamo components.
## Run Dynamo
### Running an LLM API Server
> **Tip (Optional):** Before running Dynamo, verify your system configuration with `python3 deploy/sanity_check.py`
Dynamo provides a simple way to spin up a local set of inference components including:
......@@ -142,17 +162,38 @@ Dynamo provides a simple way to spin up a local set of inference components incl
- **Basic and Kv Aware Router** – Route and load balance traffic to a set of workers.
- **Workers** – Set of pre-configured LLM serving engines.
Start the frontend:
> **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 &`
```bash
# Start an OpenAI compatible HTTP server with prompt templating, tokenization, and routing.
# For local dev: --store-kv file avoids etcd (workers and frontend must share a disk)
python3 -m dynamo.frontend --http-port 8000 --store-kv file
```
In another terminal (or same terminal if using background mode), start a worker for your chosen backend:
# Start the SGLang engine. You can run several of these for the same or different models.
# The frontend will discover them automatically.
python3 -m dynamo.sglang --model-path deepseek-ai/DeepSeek-R1-Distill-Llama-8B --store-kv file
```bash
# SGLang
python3 -m dynamo.sglang --model-path Qwen/Qwen3-0.6B --store-kv file
# TensorRT-LLM
python3 -m dynamo.trtllm --model-path Qwen/Qwen3-0.6B --store-kv file
# vLLM (note: uses --model, not --model-path)
python3 -m dynamo.vllm --model Qwen/Qwen3-0.6B --store-kv file \
--kv-events-config '{"enable_kv_cache_events": false}'
```
> **Note:** vLLM workers publish KV cache events by default, which requires NATS. For dependency-free local development with vLLM, add `--kv-events-config '{"enable_kv_cache_events": false}'`. This keeps local prefix caching enabled while disabling event publishing. See [Service Discovery and Messaging](#service-discovery-and-messaging) for details.
> **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.
>
> See [Service Discovery and Messaging](#service-discovery-and-messaging) for details.
#### Send a Request
......@@ -172,13 +213,6 @@ curl localhost:8000/v1/chat/completions -H "Content-Type: application/json"
Rerun with `curl -N` and change `stream` in the request to `true` to get the responses as soon as the engine issues them.
### What's Next?
- **Scale up**: Deploy on Kubernetes with [Recipes](recipes/)
- **Add features**: Enable [KV-aware routing](docs/router/kv_cache_routing.md), [disaggregated serving](docs/design_docs/disagg_serving.md)
- **Benchmark**: Use [AIPerf](docs/benchmarks/benchmarking.md) to measure performance
- **Try other engines**: [vLLM](docs/backends/vllm/), [SGLang](docs/backends/sglang/), [TensorRT-LLM](docs/backends/trtllm/)
# Kubernetes Deployment
For production deployments on Kubernetes clusters with multiple GPUs.
......@@ -206,60 +240,6 @@ See [recipes/README.md](recipes/README.md) for the full list and deployment inst
- [Amazon EKS](examples/deployments/EKS/)
- [Google GKE](examples/deployments/GKE/)
# Concepts
## Engines
Dynamo is inference engine agnostic. Install the wheel for your chosen engine and run with `python3 -m dynamo.<engine> --help`.
| Engine | Install | Docs | Best For |
|--------|---------|------|----------|
| vLLM | `uv pip install ai-dynamo[vllm]` | [Guide](docs/backends/vllm/) | Broadest feature coverage |
| SGLang | `uv pip install ai-dynamo[sglang]` | [Guide](docs/backends/sglang/) | High-throughput serving |
| TensorRT-LLM | `pip install --pre --extra-index-url https://pypi.nvidia.com ai-dynamo[trtllm]` | [Guide](docs/backends/trtllm/) | Maximum performance |
> **Note:** TensorRT-LLM requires `pip` (not `uv`) due to URL-based dependencies. See the [TRT-LLM guide](docs/backends/trtllm/) for container setup and prerequisites.
Use `CUDA_VISIBLE_DEVICES` to specify which GPUs to use. Engine-specific options (context length, multi-GPU, etc.) are documented in each backend guide.
## Service Discovery and Messaging
Dynamo uses TCP for inter-component communication. External services are optional for most deployments:
| Deployment | etcd | NATS | Notes |
|------------|------|------|-------|
| **Kubernetes** | ❌ Not required | ❌ Not required | K8s-native discovery; TCP request plane |
| **Local Development** | ❌ Not required | ❌ Not required | Pass `--store-kv file`; vLLM also needs `--kv-events-config '{"enable_kv_cache_events": false}'` |
| **KV-Aware Routing** | — | ✅ Required | Prefix caching enabled by default requires NATS |
For local development without external dependencies, pass `--store-kv file` (avoids etcd) to both the frontend and workers. vLLM users should also pass `--kv-events-config '{"enable_kv_cache_events": false}'` to disable KV event publishing (avoids NATS) while keeping local prefix caching enabled; SGLang and TRT-LLM don't require this flag.
For distributed non-Kubernetes deployments or KV-aware routing:
- [etcd](https://etcd.io/) can be run directly as `./etcd`.
- [nats](https://nats.io/) needs JetStream enabled: `nats-server -js`.
To quickly setup both: `docker compose -f deploy/docker-compose.yml up -d`
# Advanced Topics
## Benchmarking
Dynamo provides comprehensive benchmarking tools:
- **[Benchmarking Guide](docs/benchmarks/benchmarking.md)** – Compare deployment topologies using AIPerf
- **[SLA-Driven Deployments](docs/planner/sla_planner_quickstart.md)** – Optimize deployments to meet SLA requirements
## Frontend OpenAPI Specification
The OpenAI-compatible frontend exposes an OpenAPI 3 spec at `/openapi.json`. To generate without running the server:
```bash
cargo run -p dynamo-llm --bin generate-frontend-openapi
```
This writes to `docs/frontends/openapi.json`.
# Building from Source
For contributors who want to build Dynamo from source rather than installing from PyPI.
......@@ -347,13 +327,64 @@ cd $PROJECT_ROOT
uv pip install -e .
```
You should now be able to run `python3 -m dynamo.frontend`.
## 8. Run the Frontend
```bash
python3 -m dynamo.frontend
```
## 9. Configure for Local Development
For local development, pass `--store-kv file` to avoid external dependencies (see Service Discovery and Messaging section).
- Pass `--store-kv file` to avoid external dependencies (see [Service Discovery and Messaging](#service-discovery-and-messaging))
- Set `DYN_LOG` to adjust the logging level (e.g., `export DYN_LOG=debug`). Uses the same syntax as `RUST_LOG`
Set the environment variable `DYN_LOG` to adjust the logging level; for example, `export DYN_LOG=debug`. It has the same syntax as `RUST_LOG`.
> **Note:** VSCode and Cursor users can use the `.devcontainer` folder for a pre-configured dev environment. See the [devcontainer README](.devcontainer/README.md) for details.
If you use vscode or cursor, we have a .devcontainer folder built on [Microsofts Extension](https://code.visualstudio.com/docs/devcontainers/containers). For instructions see the [ReadMe](.devcontainer/README.md) for more details.
# Advanced Topics
## Benchmarking
Dynamo provides comprehensive benchmarking tools:
- **[Benchmarking Guide](docs/benchmarks/benchmarking.md)** – Compare deployment topologies using AIPerf
- **[SLA-Driven Deployments](docs/planner/sla_planner_quickstart.md)** – Optimize deployments to meet SLA requirements
## Frontend OpenAPI Specification
The OpenAI-compatible frontend exposes an OpenAPI 3 spec at `/openapi.json`. To generate without running the server:
```bash
cargo run -p dynamo-llm --bin generate-frontend-openapi
```
This writes to `docs/frontends/openapi.json`.
## Service Discovery and Messaging
Dynamo uses TCP for inter-component communication. On Kubernetes, native resources ([CRDs + EndpointSlices](docs/kubernetes/service_discovery.md)) handle service discovery. External services are optional for most deployments:
| Deployment | etcd | NATS | Notes |
|------------|------|------|-------|
| **Local Development** | ❌ Not required | ❌ Not required | Pass `--store-kv file`; vLLM also needs `--kv-events-config '{"enable_kv_cache_events": false}'` |
| **Kubernetes** | ❌ Not required | ❌ Not required | K8s-native discovery; TCP request plane |
> **Note:** KV-Aware Routing requires NATS for prefix caching coordination.
For Slurm or other distributed deployments (and KV-aware routing):
- [etcd](https://etcd.io/) can be run directly as `./etcd`.
- [nats](https://nats.io/) needs JetStream enabled: `nats-server -js`.
To quickly setup both: `docker compose -f deploy/docker-compose.yml up -d`
See [SGLang on Slurm](examples/backends/sglang/slurm_jobs/README.md) and [TRT-LLM on Slurm](examples/basics/multinode/trtllm/README.md) for deployment examples.
## More News
- [11/20] [Dell integrates PowerScale with Dynamo's NIXL for 19x faster TTFT](https://www.dell.com/en-us/dt/corporate/newsroom/announcements/detailpage.press-releases~usa~2025~11~dell-technologies-and-nvidia-advance-enterprise-ai-innovation.htm)
- [11/20] [WEKA partners with NVIDIA on KV cache storage for Dynamo](https://siliconangle.com/2025/11/20/nvidia-weka-kv-cache-solution-ai-inferencing-sc25/)
- [11/13] [Dynamo Office Hours Playlist](https://www.youtube.com/playlist?list=PL5B692fm6--tgryKu94h2Zb7jTFM3Go4X)
- [10/16] [How Baseten achieved 2x faster inference with NVIDIA Dynamo](https://www.baseten.co/blog/how-baseten-achieved-2x-faster-inference-with-nvidia-dynamo/)
<!-- Reference links for Feature Compatibility Matrix -->
[disagg]: docs/design_docs/disagg_serving.md
......
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Pip (PyPI)
----------
Install a pre-built wheel from PyPI.
.. code-block:: bash
# Create a virtual environment and activate it
uv venv venv
source venv/bin/activate
# Install Dynamo from PyPI (choose one backend extra)
uv pip install "ai-dynamo[sglang]==my-tag" # or [vllm], [trtllm]
Pip from source
---------------
Install directly from a local checkout for development.
.. code-block:: bash
# Clone the repository
git clone https://github.com/ai-dynamo/dynamo.git
cd dynamo
# Create a virtual environment and activate it
uv venv venv
source venv/bin/activate
uv pip install ".[sglang]" # or [vllm], [trtllm]
Docker
------
Pull and run prebuilt images from NVIDIA NGC (`nvcr.io`).
.. code-block:: bash
# Run a container (mount your workspace if needed)
docker run --rm -it \
--gpus all \
--network host \
nvcr.io/nvidia/ai-dynamo/sglang-runtime:my-tag # or vllm, tensorrtllm
docs/_includes/quick_start_local.rst
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Get started with Dynamo locally in just a few commands:
..
SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES.
All rights reserved.
SPDX-License-Identifier: Apache-2.0
**1. Install Dynamo**
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 and install Dynamo
# Create virtual environment
uv venv venv
source venv/bin/activate
# Use prerelease flag to install RC versions of flashinfer and/or other dependencies
uv pip install --prerelease=allow "ai-dynamo[sglang]" # or [vllm], [trtllm]
uv pip install pip
**2. Start etcd/NATS**
Install system dependencies and the Dynamo wheel for your chosen backend:
**SGLang**
.. code-block:: bash
# Fetch and start etcd and NATS using Docker Compose
VERSION=$(uv pip show ai-dynamo | grep Version | cut -d' ' -f2)
curl -fsSL -o docker-compose.yml https://raw.githubusercontent.com/ai-dynamo/dynamo/refs/tags/v${VERSION}/deploy/docker-compose.yml
docker compose -f docker-compose.yml up -d
sudo apt install python3-dev
uv pip install --prerelease=allow "ai-dynamo[sglang]"
.. note::
**3. Run Dynamo**
For CUDA 13 (B300/GB300), the container is recommended. See
`SGLang install docs <https://docs.sglang.ai/start/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)
python -m dynamo.frontend
# --store-kv file avoids needing etcd (frontend and workers must share a disk)
python3 -m dynamo.frontend --store-kv file
# In another terminal, start an SGLang worker
python -m dynamo.sglang --model-path Qwen/Qwen3-0.6B
In another terminal (or same terminal if using background mode), start a worker:
**4. Test your deployment**
**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
......@@ -41,5 +148,3 @@ Get started with Dynamo locally in just a few commands:
-d '{"model": "Qwen/Qwen3-0.6B",
"messages": [{"role": "user", "content": "Hello!"}],
"max_tokens": 50}'
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..
Installation Page (left sidebar target)
..
Installation
============
.. include:: ../_includes/install.rst
docs/index.rst
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......@@ -41,7 +41,6 @@ Quickstart
:caption: Getting Started
Quickstart <self>
Installation <_sections/installation>
Support Matrix <reference/support-matrix.md>
Feature Matrix <reference/feature-matrix.md>
Release Artifacts <reference/release-artifacts.md>
......
docs/reference/feature-matrix.md
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<!--
SPDX-FileCopyrightText: Copyright (c) 2025-2026 NVIDIA CORPORATION & AFFILIATES.
All rights reserved.
SPDX-License-Identifier: Apache-2.0
-->
# Dynamo Feature Compatibility Matrices
This document provides a comprehensive compatibility matrix for key Dynamo features across the supported backends.
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