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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.
NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments. 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/support_matrix.md
- Install etcd and nats
To co-ordinate across the data center Dynamo relies on an etcd and nats cluster. To run locally these need to be available.
The Dynamo team recommend the uv Python package manager, although anyway works. Install uv:
curl -LsSf https://astral.sh/uv/install.sh | sh
- Select an engine
We publish Python wheels specialized for each of our supported engines: vllm, sglang, llama.cpp and trtllm. The examples that follow use sglang, read on for other engines.
uv venv venv
source venv/bin/activate
uv pip install pip
# Choose one
uv pip install "ai-dynamo[sglang]"
uv pip install "ai-dynamo[vllm]"
uv pip install "ai-dynamo[llama_cpp]" # CPU, see later for GPU
You can run a model and interact with it locally using commands below.
python -m dynamo.frontend --interactive
python -m dynamo.sglang.worker Qwen/Qwen3-4B
✔ User · Hello, how are you?
Okay, so I'm trying to figure out how to respond to the user's greeting. They said, "Hello, how are you?" and then followed it with "Hello! I'm just a program, but thanks for asking." Hmm, I need to come up with a suitable reply. ...
If the model is not available locally it will be downloaded from HuggingFace and cached.
You can also pass a local path: python -m dynamo.sglang.worker --model-path ~/llms/Qwen3-0.6B
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 vllm 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 deepseek-ai/DeepSeek-R1-Distill-Llama-8B
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
}' | jqRerun with curl -N and change stream in the request to true to get the responses as soon as the engine issues them.
In the introduction we installed the sglang engine. There are other options.
All of these requires nats and etcd, as well as a 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.
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.
This currently requires a container TODO ADD THE DOCS PLZ THANK YOU
To install llama.cpp for CPU inference:
uv pip install ai-dynamo[llama_cpp]
To build llama.cpp for CUDA:
pip install llama-cpp-python -C cmake.args="-DGGML_CUDA=on"
uv pip install uvloop ai-dynamo
At time of writing the uv pip version does not support that syntax, so use pip directly inside the venv.
To build llama.cpp for other accelerators see https://pypi.org/project/llama-cpp-python/ .
Download a GGUF and run the engine like this:
python -m dynamo.llama_cpp --model-path ~/llms/Qwen3-0.6B-Q8_0.gguf
If you have multiple GPUs, llama.cpp does automatic tensor parallelism. You do not need to pass any extra flags to dynamo-run to enable it.
- Install libraries
Ubuntu:
sudo apt install -y build-essential libhwloc-dev libudev-dev pkg-config libclang-dev protobuf-compiler python3-dev cmake
macOS:
# if brew is not installed on your system, install it
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
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.
- Install Rust
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
source $HOME/.cargo/env
- Create a Python virtual env:
uv venv dynamo
source dynamo/bin/activate
- Install build tools
uv pip install pip maturin
Maturin is the Rust<->Python bindings build tool.
- Build the Rust bindings
cd lib/bindings/python
maturin develop --uv
- Install the wheel
cd $PROJECT_ROOT
uv pip install .
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.
Follow the Quickstart Guide to deploy to Kubernetes.
