Getting Started with NVIDIA Dynamo: A Powerful Framework for Distributed LLM Inference

In the rapidly evolving landscape of generative AI, efficiently serving large language models (LLMs) at scale remains a significant challenge. Enter NVIDIA Dynamo, an open-source inference framework specifically designed to address the complexities of serving generative AI models in distributed environments. In this blog post, we’ll explore what makes Dynamo special and provide a practical guide to getting started with this powerful tool.

What is NVIDIA Dynamo?

NVIDIA Dynamo is a high-throughput, low-latency inference framework built for serving generative AI and reasoning models across multi-node distributed environments. What sets Dynamo apart is its inference engine-agnostic approach, supporting popular backends like TRT-LLM, vLLM, SGLang, and others.

Key capabilities that make Dynamo particularly valuable include:

• Disaggregated prefill & decode inference that maximizes GPU throughput by separating the computationally intensive prefill phase from the latency-sensitive decode phase
• Dynamic GPU scheduling that optimizes performance based on fluctuating demand
• LLM-aware request routing that intelligently directs requests to minimize redundant KV cache computations
• Accelerated data transfer using NIXL (NVIDIA Inference tranXfer Library)
• KV cache offloading that leverages multiple memory hierarchies for improved system throughput

Why Choose Dynamo?

Traditional inference serving frameworks often struggle with the complexities of large-scale distributed execution. This leads to challenges like GPU underutilization, expensive KV cache re-computation, memory bottlenecks, and inefficient GPU allocation.

NVIDIA Dynamo addresses these issues through its innovative architecture. Performance benchmarks show impressive gains, including:

• Up to 2X throughput improvements with disaggregated serving
• 3X improvement in time-to-first-token with KV-aware routing
• 40% TTFT improvement through system memory offloading for KV caches

Getting Started with Dynamo

Let’s walk through the steps to set up and start using NVIDIA Dynamo:

System Requirements

Dynamo works best on systems with:

• Ubuntu 24.04 (recommended) with x86_64 CPU
• NVIDIA GPUs (performance scales with more GPUs)
• Python 3.x

For a complete list of supported configurations, refer to the support matrix.

Installation

First, let’s install the necessary system packages and set up a Python environment:

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]

This installs Dynamo with all its dependencies. The [all] option ensures you get the full set of features, including all supported backends.

Running a Local LLM

The simplest way to get started is to run a model locally using the dynamo run command. This allows you to interact with an LLM through a simple interface:

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

This command downloads the DeepSeek R1 Distill Llama 8B model and serves it using the vLLM backend. You’ll be presented with an interactive console where you can chat with the model.

Setting Up Distributed Serving

For more advanced use cases, you’ll want to set up Dynamo’s distributed serving capabilities. Here’s how:

1. Start Dynamo Distributed Runtime Services

First, start the core Dynamo services using Docker Compose:

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

This initializes the distributed runtime services that enable Dynamo’s advanced capabilities.

2. Start Dynamo LLM Serving Components

Next, serve a minimal configuration with an HTTP server, basic router, and a worker:

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

This command sets up:

• An OpenAI-compatible HTTP API server
• A router to load balance traffic
• A worker that runs the LLM

3. Test the Deployment

You can now test your deployment using a simple curl command:

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

Advanced Configuration

Customizing the Worker Configuration

Dynamo allows you to customize worker configurations based on your needs. For example, if you want to allocate more workers for the prefill phase (to improve TTFT) and fewer for decode, you can modify the configuration file:

workers:
  prefill:
    count: 3
    gpu_memory_utilization: 0.9
  decode:
    count: 1
    gpu_memory_utilization: 0.8

Enabling KV Cache Management

To enable the powerful KV cache management capabilities:

kv_cache:
  enabled: true
  cache_engine: "memory"
  max_memory_gb: 64

This configuration enables KV cache offloading to system memory with a limit of 64GB.

Real-World Deployment Considerations

When deploying Dynamo in production, consider these best practices:

1. Scale Horizontally: Add more nodes to your cluster as your throughput requirements increase
2. Monitor Memory Usage: Keep an eye on both GPU and system memory usage
3. Tune Worker Allocation: Adjust the balance between prefill and decode workers based on your specific workload
4. Security: Configure appropriate authentication for the API server
5. Load Testing: Test your deployment with realistic traffic patterns before going live

Conclusion

NVIDIA Dynamo represents a significant advancement in the field of LLM inference serving. By addressing key challenges like GPU utilization, memory bottlenecks, and inefficient routing, it enables more efficient deployment of generative AI models at scale.

Whether you’re running a simple local setup or a complex distributed deployment, Dynamo provides the tools and flexibility needed to optimize performance for your specific use case. As an open-source project with a transparent development approach, it also benefits from contributions from the wider AI community.

The combination of Rust’s performance with Python’s flexibility makes Dynamo not just powerful but also highly extensible. As generative AI continues to evolve, having infrastructure that can efficiently scale with your needs becomes increasingly important—and NVIDIA Dynamo is positioned to be a key player in that ecosystem.

Ready to try it out? Visit the official GitHub repository to get started and join the Discord community to connect with other users and contributors.

Happy inferencing!