Unleash AI Power with NVIDIA Dynamo: The Ultimate Guide to High-Throughput LLM Inference

Ready to revolutionize your AI inferencing? NVIDIA Dynamo is a game-changing framework designed to supercharge generative AI and reasoning models, even in complex, multi-node environments. This article will walk you through everything you need to know, from installation to advanced features, and show you how Dynamo can dramatically improve your LLM serving capabilities.

What is NVIDIA Dynamo and Why Should You Care?

NVIDIA Dynamo is a high-performance inference framework architected to deliver low-latency results at high throughput. Built for modern demands, it’s engineered to handle the unique needs of Large Language Models (LLMs), and its open-source nature fosters innovation and collaboration. NVIDIA Dynamo stands out because it is inference engine agnostic, so it is able to support TRT-LLM, vLLM, SGLang, and others.

Key Benefits of Using NVIDIA Dynamo: Accelerate Your AI

• Blazing-Fast Inference: Achieve unparalleled speed and responsiveness for your AI applications.
• Optimized Throughput: Maximize GPU utilization through innovative features like disaggregated prefill & decode.
• LLM-Centric Design: Dynamo understands the nuances of LLMs, ensuring peak performance through its unique framework.

Here's a breakdown of some of its key advantages:

• Disaggregated Prefill & Decode: Optimize GPU throughput and balance speed and latency.
• Dynamic GPU Scheduling: Adapt to demand fluctuations, maintaining performance during spikes.
• LLM-Aware Request Routing: Avoid redundant KV cache re-computation, saving valuable resources.
• Accelerated Data Transfer: Minimize response times using NIXL.
• KV Cache Offloading: Take advantage of multiple memory hierarchies for increased system throughput.

Installation: Get Dynamo Up and Running

It’s recommended to use Ubuntu 24.04 with an x86_64 CPU. Getting started with NVIDIA Dynamo is straightforward. First, update your system and install essential packages with the following commands:

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]

Building the Dynamo Base Image: Essential for Kubernetes Deployment

While not required for local work, building a Dynamo base image to your container registry will be necessary when deploying your pipelines to Kubernetes.

Here is 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

You can point to your image by setting the DYNAMO_IMAGE environment variable:

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

Running and Interacting with an LLM Locally: See Dynamo in Action

Dynamo offers a simple way to run and interact with LLMs locally. To quickly get started, use the dynamo run command. This command supports several backends, including: mistralrs, sglang, vllm, and tensorrtllm.

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

LLM Serving with NVIDIA Dynamo: A Minimal Configuration

Dynamo simplifies the process of setting up inference components, making it easier than ever to serve LLMs.

Follow these steps to run a minimal configuration:

1. Start Dynamo Distributed Runtime Services:

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

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2. Start Dynamo LLM Serving Components:

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

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3. 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
   

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Local Development: Container and Conda Options

Dynamo offers flexibility for local development. You can work within a container or use a Conda environment.

Container Development:

• Build the container: ./container/build.sh
• Run the container: ./container/run.sh -it --mount-workspace

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 source
cd lib/bindings/python
pip install .
cd../../../
pip install .[all]
# To test
docker compose -f deploy/docker-compose.yml up -d
cd examples/llm
dynamo serve graphs.agg:Frontend -f configs/agg.yaml

Supercharge Your LLM Inference Today

NVIDIA Dynamo is more than just a framework, it's a gateway to unlocking the full potential of your AI models. By leveraging its advanced features and open-source flexibility, you can achieve unprecedented levels of performance and efficiency. Start exploring the possibilities today and transform your AI inferencing!