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# LLM Deployment using vLLM

This directory contains reference implementations for deploying Large Language Models (LLMs) in various configurations using vLLM. For Dynamo integration, we leverage vLLM's native KV cache events, NIXL based transfer mechanisms, and metric reporting to enable KV-aware routing and P/D disaggregation.

## Use the Latest Release

We recommend using the latest stable release of Dynamo to avoid breaking changes:

[![GitHub Release](https://img.shields.io/github/v/release/ai-dynamo/dynamo)](https://github.com/ai-dynamo/dynamo/releases/latest)

You can find the latest release [here](https://github.com/ai-dynamo/dynamo/releases/latest) and check out the corresponding branch with:

```bash
git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
```

---

## Table of Contents
- [Feature Support Matrix](#feature-support-matrix)
- [Quick Start](#quick-start)
- [Single Node Examples](#run-single-node-examples)
- [Advanced Examples](#advanced-examples)
- [Deploy on Kubernetes](#kubernetes-deployment)
- [Configuration](#configuration)

## Feature Support Matrix

### Core Dynamo Features

| Feature | vLLM | Notes |
|---------|------|-------|
| [**Disaggregated Serving**](../../../docs/design_docs/disagg_serving.md) | ✅ |  |
| [**Conditional Disaggregation**](../../../docs/design_docs/disagg_serving.md#conditional-disaggregation) | 🚧 | WIP |
| [**KV-Aware Routing**](../../../docs/router/kv_cache_routing.md) | ✅ |  |
| [**SLA-Based Planner**](../../../docs/planner/sla_planner.md) | ✅ |  |
| [**Load Based Planner**](../../../docs/planner/load_planner.md) | 🚧 | WIP |
| [**KVBM**](../../../docs/kvbm/kvbm_architecture.md) | ✅ |  |
| [**LMCache**](./LMCache_Integration.md) | ✅ |  |

### Large Scale P/D and WideEP Features

| Feature            | vLLM | Notes                                                                 |
|--------------------|------|-----------------------------------------------------------------------|
| **WideEP**         | ✅   | Support for PPLX / DeepEP not verified                                           |
| **DP Rank Routing**| ✅   | Supported via external control of DP ranks |
| **GB200 Support**  | 🚧   | Container functional on main |

## vLLM Quick Start

Below we provide a guide that lets you run all of our the common deployment patterns on a single node.

### Start NATS and ETCD in the background

Start using [Docker Compose](../../../deploy/docker-compose.yml)

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

### Pull or build container

We have public images available on [NGC Catalog](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/ai-dynamo/collections/ai-dynamo/artifacts). If you'd like to build your own container from source:

```bash
./container/build.sh --framework VLLM
```

### Run container

```bash
./container/run.sh -it --framework VLLM [--mount-workspace]
```

This includes the specific commit [vllm-project/vllm#19790](https://github.com/vllm-project/vllm/pull/19790) which enables support for external control of the DP ranks.

## Run Single Node Examples

> [!IMPORTANT]
> Below we provide simple shell scripts that run the components for each configuration. Each shell script runs `python3 -m dynamo.frontend` to start the ingress and uses `python3 -m dynamo.vllm` to start the vLLM workers. You can also run each command in separate terminals for better log visibility.

This figure shows an overview of the major components to deploy:

```mermaid
%%{init: {'theme':'base', 'themeVariables': { 'fontSize':'10px', 'primaryColor':'#2e8b57', 'primaryTextColor':'#fff', 'primaryBorderColor':'#333', 'lineColor':'#81b1db', 'secondaryColor':'#b35900', 'tertiaryColor':'#808080', 'edgeLabelBackground':'transparent'}}}%%
graph TD
    %% Node Definitions with custom shapes
    HTTP[HTTP]
    ROUTER[Router]
    PREFILL[vLLM Prefill Worker]
    DECODE[vLLM Decode Worker]

    %% Class Definitions for color
    classDef worker_style fill:#2e8b57,stroke:#333,stroke-width:2px,color:#fff;
    classDef router_style fill:#b35900,stroke:#333,stroke-width:2px,color:#fff;

    %% Applying classes to nodes
    class PREFILL,DECODE worker_style
    class ROUTER router_style

    %% Request/Response flow
    HTTP <--> |"request/response"| ROUTER
    ROUTER --> |"1. send to prefill"| PREFILL
    PREFILL --> |"2. return NIXL metadata"| ROUTER
    ROUTER --> |"3. send with metadata"| DECODE
    DECODE --> |"4. stream response"| ROUTER

    %% KV Events publishing
    PREFILL -.-> |"publish kv events"| ROUTER
```

Note: The above architecture illustrates all the components. The final components that get spawned depend upon the chosen deployment pattern.

### Aggregated Serving

```bash
# requires one gpu
cd examples/backends/vllm
bash launch/agg.sh
```

### Aggregated Serving with KV Routing

```bash
# requires two gpus
cd examples/backends/vllm
bash launch/agg_router.sh
```

### Disaggregated Serving

```bash
# requires two gpus
cd examples/backends/vllm
bash launch/disagg.sh
```

### Disaggregated Serving with KV Routing

```bash
# requires three gpus
cd examples/backends/vllm
bash launch/disagg_router.sh
```

### Single Node Data Parallel Attention / Expert Parallelism

This example is not meant to be performant but showcases Dynamo routing to data parallel workers

```bash
# requires four gpus
cd examples/backends/vllm
bash launch/dep.sh
```

> [!TIP]
> Run a disaggregated example and try adding another prefill worker once the setup is running! The system will automatically discover and utilize the new worker.

## Advanced Examples

Below we provide a selected list of advanced deployments. Please open up an issue if you'd like to see a specific example!

### Speculative Decoding with Aggregated Serving (Meta-Llama-3.1-8B-Instruct + Eagle3)

Run **Meta-Llama-3.1-8B-Instruct** with **Eagle3** as a draft model using **aggregated speculative decoding** on a single node.
This setup demonstrates how to use Dynamo to create an instance using Eagle-based speculative decoding under the **VLLM aggregated serving framework** for faster inference while maintaining accuracy.

**Guide:** [Speculative Decoding Quickstart](./speculative_decoding.md)

### Kubernetes Deployment

For complete Kubernetes deployment instructions, configurations, and troubleshooting, see [vLLM Kubernetes Deployment Guide](../../../examples/backends/vllm/deploy/README.md)

## Configuration

vLLM workers are configured through command-line arguments. Key parameters include:

- `--model`: Model to serve (e.g., `Qwen/Qwen3-0.6B`)
- `--is-prefill-worker`: Enable prefill-only mode for disaggregated serving
- `--metrics-endpoint-port`: Port for publishing KV metrics to Dynamo
- `--connector`: Specify which kv_transfer_config you want vllm to use `[nixl, lmcache, kvbm, none]`. This is a helper flag which overwrites the engines KVTransferConfig.

See `args.py` for the full list of configuration options and their defaults.

The [documentation](https://docs.vllm.ai/en/v0.9.2/configuration/serve_args.html?h=serve+arg) for the vLLM CLI args points to running 'vllm serve --help' to see what CLI args can be added. We use the same argument parser as vLLM.

### Hashing Consistency for KV Events

When using KV-aware routing, ensure deterministic hashing across processes to avoid radix tree mismatches. Choose one of the following:

- Set `PYTHONHASHSEED=0` for all vLLM processes when relying on Python's builtin hashing for prefix caching.
- If your vLLM version supports it, configure a deterministic prefix caching algorithm, for example:

```bash
vllm serve ... --enable-prefix-caching --prefix-caching-algo sha256
```
See the high-level notes in [KV Cache Routing](../../../docs/router/kv_cache_routing.md) on deterministic event IDs.

## Request Migration

You can enable [request migration](../../../docs/fault_tolerance/request_migration.md) to handle worker failures gracefully. Use the `--migration-limit` flag to specify how many times a request can be migrated to another worker:

```bash
python3 -m dynamo.vllm ... --migration-limit=3
```

This allows a request to be migrated up to 3 times before failing. See the [Request Migration Architecture](../../../docs/fault_tolerance/request_migration.md) documentation for details on how this works.

## Request Cancellation

When a user cancels a request (e.g., by disconnecting from the frontend), the request is automatically cancelled across all workers, freeing compute resources for other requests.

### Cancellation Support Matrix

| | Prefill | Decode |
|-|---------|--------|
| **Aggregated** | ✅ | ✅ |
| **Disaggregated** | ✅ | ✅ |

For more details, see the [Request Cancellation Architecture](../../../docs/fault_tolerance/request_cancellation.md) documentation.
