Dynamo Examples

This directory contains practical examples demonstrating how to deploy and use Dynamo for distributed LLM inference. Each example includes setup instructions, configuration files, and explanations to help you understand different deployment patterns and use cases.

Want to see a specific example? Open a GitHub issue to request an example you'd like to see, or open a pull request if you'd like to contribute your own!

Basics & Tutorials

Learn fundamental Dynamo concepts through these introductory examples:

• Quickstart - Simple aggregated serving example with vLLM backend
• Disaggregated Serving - Prefill/decode separation for enhanced performance and scalability
• Multi-node - Distributed inference across multiple nodes and GPUs

Framework Support

These examples show how Dynamo broadly works using major inference engines.

If you want to see advanced, framework-specific deployment patterns and best practices, check out the Examples Backends directory:

• vLLM – vLLM-specific deployment and configuration
• SGLang – SGLang integration examples and workflows
• TensorRT-LLM – TensorRT-LLM workflows and optimizations

Deployment Examples

Platform-specific deployment guides for production environments:

• Amazon EKS - Deploy Dynamo on Amazon Elastic Kubernetes Service
• Azure AKS - Deploy Dynamo on Azure Kubernetes Service
• Amazon ECS - Deploy Dynamo on Amazon Elastic Container Service
• Google GKE - Coming soon

Runtime Examples

Low-level runtime examples for developers using Python<>Rust bindings:

• Hello World - Minimal Dynamo runtime service demonstrating basic concepts

Getting Started

1. Choose your deployment pattern: Start with the Quickstart for a simple local deployment, or explore Disaggregated Serving for advanced architectures.

2. Set up prerequisites: Most examples require etcd and NATS services. You can start them using:

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

3. Follow the example: Each directory contains detailed setup instructions and configuration files specific to that deployment pattern.

Prerequisites

Before running any examples, ensure you have:

• Docker & Docker Compose - For containerized services
• CUDA-compatible GPU - For LLM inference (except hello_world, which is non-GPU aware)
• Python 3.9+ - For client scripts and utilities

For Kubernetes Deployments

If you're running Kubernetes/cloud deployment examples (EKS, AKS, GKE), you'll also need:

Tool	Minimum Version	Installation
kubectl	v1.24+	Install kubectl
Helm	v3.0+	Install Helm

See the Kubernetes Installation Guide for detailed setup instructions and pre-deployment checks.