SGLang | NVIDIA Dynamo Documentation

Use the Latest Release

We recommend using the latest stable release of Dynamo to avoid breaking changes.

Dynamo SGLang integrates SGLang engines into Dynamo’s distributed runtime, enabling disaggregated serving, KV-aware routing, and request cancellation while maintaining full compatibility with SGLang’s native engine arguments. It supports LLM inference, embedding models, multimodal vision models, and diffusion-based generation (LLM, image, video).

Prerequisites

CUDA toolkit headers for bare-metal builds (e.g. nvcc, cuda_runtime.h). See CUDA Requirements. Not required when running the pre-built sglang-runtime container.

HF_TOKEN for gated models. Export it on every node that pulls the model weights, and accept the model license on the Hugging Face model page before launch:

$ export HF_TOKEN=hf_...

Installation

Install Latest Release

We recommend using uv to install:

$ uv venv --python 3.12 --seed
$ uv pip install --prerelease=allow "ai-dynamo[sglang]"

This installs the latest stable release of Dynamo with the compatible SGLang version.

Install for Development

Development installation in a virtual environment (recommended)

Requires Rust and the CUDA toolkit (nvcc).

$ # install dynamo
$ uv venv --python 3.12 --seed
$ uv pip install maturin nixl
$ cd $DYNAMO_HOME/lib/bindings/python
$ maturin develop --uv
$ cd $DYNAMO_HOME
$ uv pip install -e .
$ # install sglang
$ git clone https://github.com/sgl-project/sglang.git
$ # you can optionally checkout any sglang branch
$ cd sglang && uv pip install -e "python"

This is the ideal way for agents to develop. You can provide the path to both repos and the virtual environment and have it rerun these commands as it makes changes

Docker

Two paths are supported. Pick the one that matches how you plan to develop.

Pre-built Dynamo SGLang container (recommended)

Pull and launch the published sglang-runtime image from NGC. See release artifacts for the current tag and CUDA variants.

$ docker run --gpus all -it --rm \
>     --network host --shm-size=10G \
>     --ulimit memlock=-1 --ulimit stack=67108864 \
>     --ulimit nofile=65536:65536 \
>     --cap-add CAP_SYS_PTRACE --ipc host \
>     -v $HOME/.cache/huggingface:/home/dynamo/.cache/huggingface \
>     nvcr.io/nvidia/ai-dynamo/sglang-runtime:1.1.1

Mount the host Hugging Face cache (-v $HOME/.cache/huggingface:/home/dynamo/.cache/huggingface) so each container restart doesn’t re-download model weights. The container runs as user dynamo (UID 1000), which is why the in-container path is /home/dynamo/.cache/huggingface.

Build from source inside upstream SGLang container

Pull and launch the upstream SGLang image, then build Dynamo from source inside it:

$ docker run --gpus all -it --rm \
>     --network host --shm-size=10G \
>     --ulimit memlock=-1 --ulimit stack=67108864 \
>     --ulimit nofile=65536:65536 \
>     --ipc host \
>     lmsysorg/sglang:v{sglang_version}

Install build dependencies and Rust inside the container:

$ apt-get update -qq && apt-get install -y -qq \
>     build-essential libclang-dev curl git > /dev/null 2>&1
$ 
$ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh -s -- -y
$ source "$HOME/.cargo/env"
$ 
$ pip install maturin[patchelf]

Clone and build Dynamo:

$ cd /sgl-workspace/
$ git clone https://github.com/ai-dynamo/dynamo.git
$ cd dynamo
$ 
$ cd lib/bindings/python/
$ maturin build -o /tmp
$ pip install /tmp/ai_dynamo_runtime*.whl
$ 
$ cd /sgl-workspace/dynamo/
$ pip install -e .

Feature Support Matrix

Feature	Status	Notes
Disaggregated Serving	✅	Prefill/decode separation with NIXL KV transfer
KV-Aware Routing	✅
SLA-Based Planner	✅
Multimodal Support	✅	Image via EPD, E/PD, E/P/D patterns
Diffusion Models	✅	LLM diffusion, image, and video generation
Request Cancellation	✅	Aggregated full; disaggregated decode-only
Graceful Shutdown	✅	Discovery unregister + grace period
Observability	✅	Metrics, tracing, and Grafana dashboards
KVBM	❌	Planned

Quick Start

Python / CLI Deployment

Start infrastructure services for local development:

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

Launch an aggregated serving deployment:

$ cd $DYNAMO_HOME/examples/backends/sglang
$ ./launch/agg.sh

Verify the deployment:

$ curl localhost:8000/v1/chat/completions \
>   -H "Content-Type: application/json" \
>   -d '{
>     "model": "Qwen/Qwen3-0.6B",
>     "messages": [{"role": "user", "content": "Explain why Roger Federer is considered one of the greatest tennis players of all time"}],
>     "stream": true,
>     "max_tokens": 30
>   }'

Disaggregated Serving

Launch a disaggregated Qwen3-0.6B deployment (smallest model, useful for plumbing validation):

$ cd $DYNAMO_HOME/examples/backends/sglang
$ ./launch/disagg.sh

Performance caveat: Qwen3-0.6B is small enough that the disaggregated pathway is dominated by transport overhead and will often look slower than aggregated. Use it for plumbing validation, not benchmarks. Switch to Qwen3-32B-FP8 or larger for realistic disagg numbers.

Multi-Node TP

SGLang supports multi-node tensor parallelism via the native --dist-init-addr, --nnodes, and --node-rank flags. See SGLang server arguments for the canonical reference; the same flags work with python -m dynamo.sglang. For a Kubernetes deployment example, see disagg-multinode.yaml.

Kubernetes Deployment

You can deploy SGLang with Dynamo on Kubernetes using a DynamoGraphDeployment. For more details, see the SGLang Kubernetes Deployment Guide.

Next Steps

Reference Guide: Worker types, architecture, and configuration
Examples: All deployment patterns with launch scripts
Disaggregation: P/D architecture and KV transfer details
Diffusion: LLM, image, and video diffusion models
Observability: Metrics, tracing, and Grafana dashboards
Deploying SGLang with Dynamo on Kubernetes: Kubernetes deployment guide