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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.
apt-get update
DEBIAN_FRONTEND=noninteractive apt-get install -yq python3-dev libucx0
pip install ai-dynamo[all]
Note
TensorRT-LLM Support is currently available on a branch
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.
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. ...
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.
First start the Dynamo Distributed Runtime services:
docker compose -f deploy/docker-compose.yml up -dNext 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.yamlcurl 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