README.md

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# Dynamo Components

This directory contains the core components that make up the Dynamo inference framework. Each component serves a specific role in the distributed LLM serving architecture, enabling high-throughput, low-latency inference across multiple nodes and GPUs.

## Supported Inference Engines

Dynamo supports multiple inference engines (with a focus on SGLang, vLLM, and TensorRT-LLM), each with their own deployment configurations and capabilities:

- **[vLLM](/docs/backends/vllm/README.md)** - High-performance LLM inference with native KV cache events and NIXL-based transfer mechanisms
- **[SGLang](/docs/backends/sglang/README.md)** - Structured generation language framework with ZMQ-based communication
- **[TensorRT-LLM](/docs/backends/trtllm/README.md)** - NVIDIA's optimized LLM inference engine with TensorRT acceleration

Each engine provides launch scripts for different deployment patterns in their respective `/launch` & `/deploy` directories.

## Core Components

### [Backends](backends/)

The backends directory contains inference engine integrations and implementations, with a key focus on:

- **vLLM** - Full-featured vLLM integration with disaggregated serving, KV-aware routing, and SLA-based planning
- **SGLang** - SGLang engine integration supporting disaggregated serving and KV-aware routing
- **TensorRT-LLM** - TensorRT-LLM integration with disaggregated serving capabilities


### [Frontend](src/dynamo/frontend/)

The frontend component provides the HTTP API layer and request processing:

- **OpenAI-compatible HTTP server** - RESTful API endpoint for LLM inference requests
- **Pre-processor** - Handles request preprocessing and validation
- **Router** - Routes requests to appropriate workers based on load and KV cache state
- **Auto-discovery** - Automatically discovers and registers available workers

### [Planner](src/dynamo/planner/)

The planner component monitors system state and dynamically adjusts worker allocation:

- **Dynamic scaling** - Scales prefill/decode workers up and down based on metrics
- **SLA-based planning** - Ensures inference performance targets are met
- **Load-based planning** - Optimizes resource utilization based on demand

## Getting Started

To get started with Dynamo components:

1. **Choose an inference engine** from the supported backends
2. **Set up required services** (etcd and NATS) using Docker Compose
3. **Configure** your chosen engine using Python wheels or building an image
4. **Run deployment scripts** from the engine's launch directory
5. **Monitor performance** using the metrics component

For detailed instructions, see the README files in each component directory and the main [Dynamo documentation](../docs/).