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# LLM Deployment Examples using TensorRT-LLM

This directory contains examples and reference implementations for deploying Large Language Models (LLMs) in various configurations using TensorRT-LLM.

# User Documentation

- [Deployment Architectures](#deployment-architectures)
- [Getting Started](#getting-started)
  - [Prerequisites](#prerequisites)
  - [Build docker](#build-docker)
  - [Run container](#run-container)
  - [Run deployment](#run-deployment)
    - [Single Node deployment](#single-node-deployments)
    - [Multinode deployment](#multinode-deployment)
  - [Client](#client)
  - [Benchmarking](#benchmarking)
- [Disaggregation Strategy](#disaggregation-strategy)
- [KV Cache Transfer](#kv-cache-transfer-in-disaggregated-serving)
- [More Example Architectures](#more-example-architectures)
  - [Llama 4 Maverick Instruct + Eagle Speculative Decoding](./llama4_plus_eagle.md)

# Quick Start

## 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))
```

## Deployment Architectures

See [deployment architectures](../llm/README.md#deployment-architectures) to learn about the general idea of the architecture.

Note: TensorRT-LLM disaggregation does not support conditional disaggregation yet. You can configure the deployment to always use either aggregate or disaggregated serving.

## Getting Started

1. Choose a deployment architecture based on your requirements
2. Configure the components as needed
3. Deploy using the provided scripts

### Prerequisites

Start required services (etcd and NATS) using [Docker Compose](../../deploy/metrics/docker-compose.yml)
```bash
docker compose -f deploy/metrics/docker-compose.yml up -d
```

### Build docker

```bash
# TensorRT-LLM uses git-lfs, which needs to be installed in advance.
apt-get update && apt-get -y install git git-lfs

# On an x86 machine:
./container/build.sh --framework tensorrtllm

# On an ARM machine:
./container/build.sh --framework tensorrtllm --platform linux/arm64

# Build the container with the default experimental TensorRT-LLM commit
# WARNING: This is for experimental feature testing only.
# The container should not be used in a production environment.
./container/build.sh --framework tensorrtllm --use-default-experimental-tensorrtllm-commit
```

### Run container

```
./container/run.sh --framework tensorrtllm -it
```
## Run Deployment

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



```

+------+      +-----------+      +------------------+             +---------------+
| HTTP |----->| processor |----->|      Worker1     |------------>|    Worker2    |
|      |<-----|           |<-----|                  |<------------|               |
+------+      +-----------+      +------------------+             +---------------+
                  |    ^                  |
       query best |    | return           | publish kv events
           worker |    | worker_id        v
                  |    |         +------------------+
                  |    +---------|     kv-router    |
                  +------------->|                  |
                                 +------------------+

```

**Note:** The diagram above shows all possible components in a deployment. Depending on the chosen disaggregation strategy, you can configure whether Worker1 handles prefill and Worker2 handles decode, or vice versa. For more information on how to select and configure these strategies, see the [Disaggregation Strategy](#disaggregation-strategy) section below.

### Single-Node Deployments

> [!IMPORTANT]
> Below we provide some simple shell scripts that run the components for each configuration. Each shell script is simply running the `dynamo-run` to start up the ingress and using `python3` to start up the workers. You can easily take each command and run them in separate terminals.

#### Aggregated
```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm
./launch/agg.sh
```

#### Aggregated with KV Routing
```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm
./launch/agg_router.sh
```

#### Disaggregated

> [!IMPORTANT]
> Disaggregated serving supports two strategies for request flow: `"prefill_first"` and `"decode_first"`. By default, the script below uses the `"decode_first"` strategy, which can reduce response latency by minimizing extra hops in the return path. You can switch strategies by setting the `DISAGGREGATION_STRATEGY` environment variable.

```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm
./launch/disagg.sh
```

#### Disaggregated with KV Routing

> [!IMPORTANT]
> Disaggregated serving with KV routing uses a "prefill first" workflow by default. Currently, Dynamo supports KV routing to only one endpoint per model. In disaggregated workflow, it is generally more effective to route requests to the prefill worker. If you wish to use a "decode first" workflow instead, you can simply set the `DISAGGREGATION_STRATEGY` environment variable accordingly.

```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm
./launch/disagg_router.sh
```

#### Aggregated with Multi-Token Prediction (MTP) and DeepSeek R1
```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm

export AGG_ENGINE_ARGS=./engine_configs/deepseek_r1/mtp/mtp_agg.yaml
export SERVED_MODEL_NAME="nvidia/DeepSeek-R1-FP4"
# nvidia/DeepSeek-R1-FP4 is a large model
export MODEL_PATH="nvidia/DeepSeek-R1-FP4"
./launch/agg.sh
```

Notes:
- MTP is only available within the container built with the experimental TensorRT-LLM commit. Please add --use-default-experimental-tensorrtllm-commit to the arguments of the build.sh script.

  Example: `./container/build.sh --framework tensorrtllm --use-default-experimental-tensorrtllm-commit`

- There is a noticeable latency for the first two inference requests. Please send warm-up requests before starting the benchmark.
- MTP performance may vary depending on the acceptance rate of predicted tokens, which is dependent on the dataset or queries used while benchmarking. Additionally, `ignore_eos` should generally be omitted or set to `false` when using MTP to avoid speculating garbage outputs and getting unrealistic acceptance rates.

### Multinode Deployment

For comprehensive instructions on multinode serving, see the [multinode-examples.md](./multinode/multinode-examples.md) guide. It provides step-by-step deployment examples and configuration tips for running Dynamo with TensorRT-LLM across multiple nodes. While the walkthrough uses DeepSeek-R1 as the model, you can easily adapt the process for any supported model by updating the relevant configuration files. You can see [Llama4+eagle](./llama4_plus_eagle.md) guide to learn how to use these scripts when a single worker fits on the single node.

### Client

See [client](../llm/README.md#client) section to learn how to send request to the deployment.

NOTE: To send a request to a multi-node deployment, target the node which is running `dynamo-run in=http`.

### Benchmarking

To benchmark your deployment with GenAI-Perf, see this utility script, configuring the
`model` name and `host` based on your deployment: [perf.sh](../../benchmarks/llm/perf.sh)


## Disaggregation Strategy

The disaggregation strategy controls how requests are distributed between the prefill and decode workers in a disaggregated deployment.

By default, Dynamo uses a `decode first` strategy: incoming requests are initially routed to the decode worker, which then forwards them to the prefill worker in round-robin fashion. The prefill worker processes the request and returns results to the decode worker for any remaining decode operations.

When using KV routing, however, Dynamo switches to a `prefill first` strategy. In this mode, requests are routed directly to the prefill worker, which can help maximize KV cache reuse and improve overall efficiency for certain workloads. Choosing the appropriate strategy can have a significant impact on performance, depending on your use case.

The disaggregation strategy can be set using the `DISAGGREGATION_STRATEGY` environment variable. You can set the strategy before launching your deployment, for example:
```bash
DISAGGREGATION_STRATEGY="prefill_first" ./launch/disagg.sh
```

## KV Cache Transfer in Disaggregated Serving

Dynamo with TensorRT-LLM supports two methods for transferring KV cache in disaggregated serving: UCX (default) and NIXL (experimental). For detailed information and configuration instructions for each method, see the [KV cache transfer guide](./kv-cache-tranfer.md).

## More Example Architectures

- [Llama 4 Maverick Instruct + Eagle Speculative Decoding](./llama4_plus_eagle.md)
