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The Era of Multi-Node, Multi-GPU

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.

Introducing NVIDIA Dynamo

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.

Installation

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

apt-get update
DEBIAN_FRONTEND=noninteractive apt-get install -yq python3-dev python3-pip python3-venv libucx0
python3 -m venv venv
source venv/bin/activate
pip install "ai-dynamo[all]"

Note

To ensure compatibility, please refer to the examples in the release branch or tag that matches the version you installed.

Building the Dynamo Base Image

Although not needed for local development, deploying your Dynamo pipelines to Kubernetes will require you to build and push a Dynamo base image to your container registry. You can use any container registry of your choice, such as:

• Docker Hub (docker.io)
• NVIDIA NGC Container Registry (nvcr.io)
• Any private registry

Here's how to build it:

./container/build.sh
docker tag dynamo:latest-vllm <your-registry>/dynamo-base:latest-vllm
docker login <your-registry>
docker push <your-registry>/dynamo-base:latest-vllm

Notes about builds for specific frameworks:

• For specific details on the --framework vllm build, see here.
• For specific details on the --framework tensorrtllm build, see here.

Note about AWS environments:

• If deploying Dynamo in AWS, make sure to build the container with EFA support using the --make-efa flag.

After building, you can use this image by setting the DYNAMO_IMAGE environment variable to point to your built image:

export DYNAMO_IMAGE=<your-registry>/dynamo-base:latest-vllm

Note

We are working on leaner base images that can be built using the targets in the top-level Earthfile.

Running and Interacting with an LLM Locally

To run a model and interact with it locally you can call dynamo run with a hugging face model. dynamo run supports several backends including: mistralrs, sglang, vllm, and tensorrtllm.

Example Command

dynamo run out=vllm deepseek-ai/DeepSeek-R1-Distill-Llama-8B

? User › Hello, how are you?
✔ 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. ...

LLM Serving

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.

To run a minimal configuration you can use a pre-configured example.

Start Dynamo Distributed Runtime Services

First start the Dynamo Distributed Runtime services:

docker compose -f deploy/metrics/docker-compose.yml up -d

Start Dynamo LLM Serving Components

Next serve a minimal configuration with an http server, basic round-robin router, and a single worker.

cd examples/llm
dynamo serve graphs.agg:Frontend -f configs/agg.yaml

Send a Request

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

Local Development

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

Otherwise, to develop locally, we recommend working inside of the container

./container/build.sh
./container/run.sh -it --mount-workspace
cargo build --release
mkdir -p /workspace/deploy/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/http /workspace/deploy/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/llmctl /workspace/deploy/sdk/src/dynamo/sdk/cli/bin
cp /workspace/target/release/dynamo-run /workspace/deploy/sdk/src/dynamo/sdk/cli/bin
uv pip install -e .export PYTHONPATH=$PYTHONPATH:/workspace/deploy/sdk/src:/workspace/components/planner/src

Conda Environment

Alternately, you can use a conda environment

conda activate <ENV_NAME>
pip install nixl # Or install https://github.com/ai-dynamo/nixl from source
cargo build --release
# To install ai-dynamo-runtime from sourcecd lib/bindings/python
pip install .cd ../../../
pip install ".[all]"# To test
docker compose -f deploy/metrics/docker-compose.yml up -d
cd examples/llm
dynamo serve graphs.agg:Frontend -f configs/agg.yaml