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ai-dynamo/dynamo: A Datacenter Scale Distributed Inference Serving Framework

| Roadmap | Documentation | Examples | Design Proposals |

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

• [08/05] Deploy openai/gpt-oss-120b with disaggregated serving on NVIDIA Blackwell GPUs using Dynamo ➡️ link

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

To learn more about each framework and their capabilities, check out each framework's README!

• vLLM
• SGLang
• TensorRT-LLM

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/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

To coordinate across a data center, Dynamo relies on etcd and NATS. To run Dynamo locally, these need to be available.

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

To quickly setup etcd & NATS, you can also run:

# At the root of the repository:
docker compose -f deploy/docker-compose.yml up -d

We publish Python wheels specialized for each of our supported engines: vllm, sglang, trtllm, and llama.cpp. 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.

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, a pre-processor (prompt templating and tokenization) and a router:
python -m dynamo.frontend --http-port 8080

# 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.
python -m dynamo.sglang.worker --model deepseek-ai/DeepSeek-R1-Distill-Llama-8B --skip-tokenizer-init

curl localhost:8080/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 is designed to be inference engine agnostic. To use any engine with Dynamo, NATS and etcd need to be installed, along with a Dynamo frontend (python -m dynamo.frontend [--interactive]).

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.worker --help

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

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

Note

Ensure that you select a PyTorch container image version that matches the version of TensorRT-LLM you are using. For example, if you are using tensorrt-llm==1.0.0rc4, use the PyTorch container image version 25.05. To find the correct PyTorch container version for your desired tensorrt-llm release, visit the TensorRT-LLM Dockerfile.multi on GitHub. Switch to the branch that matches your tensorrt-llm version, and look for the BASE_TAG line to identify the recommended PyTorch container tag.

Important

Launch container with the following additional settings --shm-size=1g --ulimit memlock=-1

# Optional step: Only required for Blackwell and Grace Hopper
pip3 install torch==2.7.1 torchvision torchaudio --index-url https://download.pytorch.org/whl/cu128

sudo apt-get -y install libopenmpi-dev

Tip

You can learn more about these prequisites and known issues with TensorRT-LLM pip based installation here.

uv pip install 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

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 $PROJECT_ROOT
uv pip install .
# For development, use
export PYTHONPATH="${PYTHONPATH}:$(pwd)/components/frontend/src:$(pwd)/components/planner/src:$(pwd)/components/backends/vllm/src:$(pwd)/components/backends/sglang/src:$(pwd)/components/backends/trtllm/src:$(pwd)/components/backends/llama_cpp/src:$(pwd)/components/backends/mocker/src"

Note

Editable (-e) does not work because the dynamo package is split over multiple directories, one per backend.

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

Remember that nats and etcd must be running (see earlier).

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.

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