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# LLM Deployment using TensorRT-LLM

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

## 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](#single-node-examples)
- [Advanced Examples](#advanced-examples)
- [KV Cache Transfer](#kv-cache-transfer-in-disaggregated-serving)
- [Client](#client)
- [Benchmarking](#benchmarking)
- [Multimodal Support](#multimodal-support)
- [Logits Processing](#logits-processing)
- [Performance Sweep](#performance-sweep)

## Feature Support Matrix

### Core Dynamo Features

| Feature | TensorRT-LLM | Notes |
|---------|--------------|-------|
| [**Disaggregated Serving**](../../../docs/design_docs/disagg_serving.md) | ✅ |  |
| [**Conditional Disaggregation**](../../../docs/design_docs/disagg_serving.md#conditional-disaggregation) | 🚧 | Not supported yet |
| [**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) | 🚧 | Planned |
| [**KVBM**](../../../docs/kvbm/kvbm_architecture.md) | ✅ | |

### Large Scale P/D and WideEP Features

| Feature            | TensorRT-LLM | Notes                                                           |
|--------------------|--------------|-----------------------------------------------------------------|
| **WideEP**         | ✅           |                                                                 |
| **DP Rank Routing**| ✅           |                                                                 |
| **GB200 Support**  | ✅           |                                                                 |

## TensorRT-LLM 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
```

### Build container

```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 trtllm

# On an ARM machine:
./container/build.sh --framework trtllm --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 trtllm --tensorrtllm-git-url https://github.com/NVIDIA/TensorRT-LLM.git --tensorrtllm-commit main
```

### Run container

```bash
./container/run.sh --framework trtllm -it
```

## Single Node Examples

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

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. In disaggregated serving, Worker1 acts as the decode worker and Worker2 as the prefill worker, with the unified frontend coordinating request routing between them.

### Aggregated
```bash
cd $DYNAMO_HOME/examples/backends/trtllm
./launch/agg.sh
```

### Aggregated with KV Routing
```bash
cd $DYNAMO_HOME/examples/backends/trtllm
./launch/agg_router.sh
```

### Disaggregated

```bash
cd $DYNAMO_HOME/examples/backends/trtllm
./launch/disagg.sh
```

### Disaggregated with KV Routing

> [!IMPORTANT]
> In disaggregated workflow, requests are routed to the prefill worker to maximize KV cache reuse.

```bash
cd $DYNAMO_HOME/examples/backends/trtllm
./launch/disagg_router.sh
```

### Aggregated with Multi-Token Prediction (MTP) and DeepSeek R1
```bash
cd $DYNAMO_HOME/examples/backends/trtllm

export AGG_ENGINE_ARGS=./engine_configs/deepseek-r1/agg/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:
- 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.

## Advanced Examples

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

### 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.

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

### Kubernetes Deployment

For complete Kubernetes deployment instructions, configurations, and troubleshooting, see [TensorRT-LLM Kubernetes Deployment Guide](../../../examples/backends/trtllm/deploy/README.md).

### Client

See [client](../../../docs/backends/sglang/README.md#testing-the-deployment) 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 `python3 -m dynamo.frontend <args>`.

### Benchmarking

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


## 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
# For decode and aggregated workers
python3 -m dynamo.trtllm ... --migration-limit=3
```

> [!IMPORTANT]
> **Prefill workers do not support request migration** and must use `--migration-limit=0` (the default). Prefill workers only process prompts and return KV cache state - they don't maintain long-running generation requests that would benefit from migration.

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](../../fault_tolerance/request_cancellation.md) documentation.

## Client

See [client](../../../docs/backends/sglang/README.md#testing-the-deployment) 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 `python3 -m dynamo.frontend <args>`.

## Benchmarking

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

## Multimodal support

Dynamo with the TensorRT-LLM backend supports multimodal models, enabling you to process both text and images (or pre-computed embeddings) in a single request. For detailed setup instructions, example requests, and best practices, see the [Multimodal Support Guide](./multimodal_support.md).

## Logits Processing

Logits processors let you modify the next-token logits at every decoding step (e.g., to apply custom constraints or sampling transforms). Dynamo provides a backend-agnostic interface and an adapter for TensorRT-LLM so you can plug in custom processors.

### How it works
- **Interface**: Implement `dynamo.logits_processing.BaseLogitsProcessor` which defines `__call__(input_ids, logits)` and modifies `logits` in-place.
- **TRT-LLM adapter**: Use `dynamo.trtllm.logits_processing.adapter.create_trtllm_adapters(...)` to convert Dynamo processors into TRT-LLM-compatible processors and assign them to `SamplingParams.logits_processor`.
- **Examples**: See example processors in `lib/bindings/python/src/dynamo/logits_processing/examples/` ([temperature](../../../lib/bindings/python/src/dynamo/logits_processing/examples/temperature.py), [hello_world](../../../lib/bindings/python/src/dynamo/logits_processing/examples/hello_world.py)).

### Quick test: HelloWorld processor
You can enable a test-only processor that forces the model to respond with "Hello world!". This is useful to verify the wiring without modifying your model or engine code.

```bash
cd $DYNAMO_HOME/examples/backends/trtllm
export DYNAMO_ENABLE_TEST_LOGITS_PROCESSOR=1
./launch/agg.sh
```

Notes:
- When enabled, Dynamo initializes the tokenizer so the HelloWorld processor can map text to token IDs.
- Expected chat response contains "Hello world".

### Bring your own processor
Implement a processor by conforming to `BaseLogitsProcessor` and modify logits in-place. For example, temperature scaling:

```python
from typing import Sequence
import torch
from dynamo.logits_processing import BaseLogitsProcessor

class TemperatureProcessor(BaseLogitsProcessor):
    def __init__(self, temperature: float = 1.0):
        if temperature <= 0:
            raise ValueError("Temperature must be positive")
        self.temperature = temperature

    def __call__(self, input_ids: Sequence[int], logits: torch.Tensor):
        if self.temperature == 1.0:
            return
        logits.div_(self.temperature)
```

Wire it into TRT-LLM by adapting and attaching to `SamplingParams`:

```python
from dynamo.trtllm.logits_processing.adapter import create_trtllm_adapters
from dynamo.logits_processing.examples import TemperatureProcessor

processors = [TemperatureProcessor(temperature=0.7)]
sampling_params.logits_processor = create_trtllm_adapters(processors)
```

### Current limitations
- Per-request processing only (batch size must be 1); beam width > 1 is not supported.
- Processors must modify logits in-place and not return a new tensor.
- If your processor needs tokenization, ensure the tokenizer is initialized (do not skip tokenizer init).

## Performance Sweep

For detailed instructions on running comprehensive performance sweeps across both aggregated and disaggregated serving configurations, see the [TensorRT-LLM Benchmark Scripts for DeepSeek R1 model](../../../examples/backends/trtllm/performance_sweeps/README.md). This guide covers recommended benchmarking setups, usage of provided scripts, and best practices for evaluating system performance.

## Dynamo KV Block Manager Integration

Dynamo with TensorRT-LLM currently supports integration with the Dynamo KV Block Manager. This integration can significantly reduce time-to-first-token (TTFT) latency, particularly in usage patterns such as multi-turn conversations and repeated long-context requests.

Here is the instruction: [Running KVBM in TensorRT-LLM](./../../../docs/kvbm/trtllm-setup.md) .
