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High-throughput, low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.

Full Feature Matrix → — Detailed compatibility including LoRA, Request Migration, Speculative Decoding, and feature interactions.

• [12/05] Moonshot AI's Kimi K2 achieves 10x inference speedup with Dynamo on GB200
• [12/02] Mistral AI runs Mistral Large 3 with 10x faster inference using Dynamo
• [12/01] InfoQ: NVIDIA Dynamo simplifies Kubernetes deployment for LLM inference
• [11/20] Dell integrates PowerScale with Dynamo's NIXL for 19x faster TTFT
• [11/20] WEKA partners with NVIDIA on KV cache storage for Dynamo
• [11/13] Dynamo Office Hours Playlist
• [10/16] How Baseten achieved 2x faster inference with NVIDIA Dynamo

Large language models are quickly outgrowing the memory and compute budget of any single GPU. Tensor-parallelism solves the capacity problem by spreading each layer across many GPUs—and sometimes many servers—but it creates a new one: how do you coordinate those shards, route requests, and share KV cache fast enough to feel like one accelerator? This orchestration gap is exactly what NVIDIA Dynamo is built to close.

Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLang or others) and captures LLM-specific capabilities such as:

• Disaggregated prefill & decode inference – Maximizes GPU throughput and facilitates trade off between throughput and latency.
• Dynamic GPU scheduling – Optimizes performance based on fluctuating demand
• LLM-aware request routing – Eliminates unnecessary KV cache re-computation
• Accelerated data transfer – Reduces inference response time using NIXL.
• KV cache offloading – Leverages multiple memory hierarchies for higher system throughput

Built in Rust for performance and in Python for extensibility, Dynamo is fully open-source and driven by a transparent, OSS (Open Source Software) first development approach.

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

The Dynamo team recommends the uv Python package manager, although any way works. Install uv:

curl -LsSf https://astral.sh/uv/install.sh | sh

Backend engines require Python development headers for JIT compilation. Install them with:

sudo apt install python3-dev

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.

uv venv venv
source venv/bin/activate
uv pip install pip

# Choose one
uv pip install "ai-dynamo[sglang]"  #replace with [vllm], [trtllm], etc.

Before trying out Dynamo, you can verify your system configuration and dependencies:

./deploy/sanity_check.py

This is a quick check for system resources, development tools, LLM frameworks, and Dynamo components.

Dynamo provides a simple way to spin up a local set of inference components including:

• OpenAI Compatible Frontend – High performance OpenAI compatible http api server written in Rust.
• Basic and Kv Aware Router – Route and load balance traffic to a set of workers.
• Workers – Set of pre-configured LLM serving engines.

# Start an OpenAI compatible HTTP server with prompt templating, tokenization, and routing.
# Pass the TLS certificate and key paths to use HTTPS instead of HTTP.
# Pass --store-kv to use the filesystem instead of etcd. The workers and frontend must share a disk.
python -m dynamo.frontend --http-port 8000 [--tls-cert-path cert.pem] [--tls-key-path key.pem] [--store-kv file]

# Start the SGLang engine, connecting to NATS and etcd to receive requests. You can run several of these,
# both for the same model and for multiple models. The frontend node will discover them.
# Pass --store-kv to use the filesystem instead of etcd. The workers and frontend must share a disk.
python -m dynamo.sglang --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B [--store-kv file]

curl localhost:8000/v1/chat/completions   -H "Content-Type: application/json"   -d '{
    "model": "deepseek-ai/DeepSeek-R1-Distill-Llama-8B",
    "messages": [
    {
        "role": "user",
        "content": "Hello, how are you?"
    }
    ],
    "stream":false,
    "max_tokens": 300
  }' | jq

Rerun with curl -N and change stream in the request to true to get the responses as soon as the engine issues them.

• Follow the Quickstart Guide to deploy on Kubernetes.
• Check out Backends to deploy various workflow configurations (e.g. SGLang with router, vLLM with disaggregated serving, etc.)
• Run some Examples to learn about building components in Dynamo and exploring various integrations.

Dynamo uses TCP for inter-component communication. External services are optional for most deployments:

For local development, pass --store-kv file to both the frontend and workers. For distributed non-Kubernetes deployments or KV-aware routing:

• etcd can be run directly as ./etcd.
• nats needs JetStream enabled: nats-server -js.

To quickly setup both: docker compose -f deploy/docker-compose.yml up -d

Dynamo provides comprehensive benchmarking tools to evaluate and optimize your deployments:

• Benchmarking Guide – Compare deployment topologies (aggregated vs. disaggregated vs. vanilla vLLM) using AIPerf
• SLA-Driven Dynamo Deployments – Optimize your deployment to meet SLA requirements

The OpenAI-compatible HTTP frontend exposes an OpenAPI 3 specification at /openapi.json. To generate and persist the same specification without running the server (for example for CI, documentation, or NIM integration), run:

cargo run -p dynamo-llm --bin generate-frontend-openapi

This writes the current frontend spec to docs/frontends/openapi.json at the repository root.

Dynamo is designed to be inference engine agnostic. To use any engine with Dynamo, start a Dynamo frontend (python -m dynamo.frontend). For local development, pass --store-kv file to avoid etcd dependency. NATS is optional and only required for KV-aware routing.

uv pip install ai-dynamo[vllm]

Run the backend/worker like this:

python -m dynamo.vllm --help

vLLM attempts to allocate enough KV cache for the full context length at startup. If that does not fit in your available memory pass --context-length <value>.

To specify which GPUs to use set environment variable CUDA_VISIBLE_DEVICES.

# Install libnuma
apt install -y libnuma-dev

uv pip install ai-dynamo[sglang]

Run the backend/worker like this:

python -m dynamo.sglang --help

You can pass any sglang flags directly to this worker, see https://docs.sglang.ai/advanced_features/server_arguments.html . See there to use multiple GPUs.

It is recommended to use NGC PyTorch Container for running the TensorRT-LLM engine.

# Optional step: Only required for non-container installations. The PyTorch 25.10 container already includes PyTorch 2.9.0 with CUDA 13.0.
uv pip install torch==2.9.0 torchvision --index-url https://download.pytorch.org/whl/cu130

sudo apt-get -y install libopenmpi-dev

# Optional step: Only required for disaggregated serving
sudo apt-get -y install libzmq3-dev

pip install --pre --extra-index-url https://pypi.nvidia.com ai-dynamo[trtllm]

Run the backend/worker like this:

python -m dynamo.trtllm --help

To specify which GPUs to use set environment variable CUDA_VISIBLE_DEVICES.

Ubuntu:

sudo apt install -y build-essential libhwloc-dev libudev-dev pkg-config libclang-dev protobuf-compiler python3-dev cmake

macOS:

• Homebrew

# if brew is not installed on your system, install it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"

• Xcode

brew install cmake protobuf

## Check that Metal is accessible
xcrun -sdk macosx metal

If Metal is accessible, you should see an error like metal: error: no input files, which confirms it is installed correctly.

curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env

Follow the instructions in uv installation guide to install uv if you don't have uv installed. Once uv is installed, create a virtual environment and activate it.

• Install uv

curl -LsSf https://astral.sh/uv/install.sh | sh

• Create a virtual environment

uv venv dynamo
source dynamo/bin/activate

uv pip install pip maturin

Maturin is the Rust<->Python bindings build tool.

cd lib/bindings/python
maturin develop --uv

cd $PROJECT_ROOT
uv pip install -e .

You should now be able to run python -m dynamo.frontend.

For local development, pass --store-kv file to avoid external dependencies (see Service Discovery and Messaging section).

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.

If you use vscode or cursor, we have a .devcontainer folder built on Microsofts Extension. For instructions see the ReadMe for more details.