# 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: [](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) - [Disaggregation Strategy](#disaggregation-strategy) - [KV Cache Transfer](#kv-cache-transfer-in-disaggregated-serving) - [Client](#client) - [Benchmarking](#benchmarking) ## Feature Support Matrix ### Core Dynamo Features | Feature | TensorRT-LLM | Notes | |---------|--------------|-------| | [**Disaggregated Serving**](../../../docs/architecture/disagg_serving.md) | ✅ | | | [**Conditional Disaggregation**](../../../docs/architecture/disagg_serving.md#conditional-disaggregation) | 🚧 | Not supported yet | | [**KV-Aware Routing**](../../../docs/architecture/kv_cache_routing.md) | ✅ | | | [**SLA-Based Planner**](../../../docs/architecture/sla_planner.md) | 🚧 | Planned | | [**Load Based Planner**](../../../docs/architecture/load_planner.md) | 🚧 | Planned | | [**KVBM**](../../../docs/architecture/kvbm_architecture.md) | 🚧 | Planned | ### Large Scale P/D and WideEP Features | Feature | TensorRT-LLM | Notes | |--------------------|--------------|-----------------------------------------------------------------------| | **WideEP** | ✅ | | | **DP Rank Routing**| ✅ | | | **GB200 Support** | ✅ | | ## 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 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 ```bash ./container/run.sh --framework tensorrtllm -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 ` to start up the ingress and using `python3 -m dynamo.trtllm ` 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. 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. ### Aggregated ```bash cd $DYNAMO_HOME/components/backends/trtllm ./launch/agg.sh ``` ### Aggregated with KV Routing ```bash cd $DYNAMO_HOME/components/backends/trtllm ./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_HOME/components/backends/trtllm ./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_HOME/components/backends/trtllm ./launch/disagg_router.sh ``` ### Aggregated with Multi-Token Prediction (MTP) and DeepSeek R1 ```bash cd $DYNAMO_HOME/components/backends/trtllm 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. ## 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 Kubernetes deployment, YAML manifests are provided in the `deploy/` directory. These define DynamoGraphDeployment resources for various configurations: - `agg.yaml` - Aggregated serving - `agg_router.yaml` - Aggregated serving with KV routing - `disagg.yaml` - Disaggregated serving - `disagg_router.yaml` - Disaggregated serving with KV routing #### Prerequisites - **Dynamo Cloud**: Follow the [Quickstart Guide](../../../docs/guides/dynamo_deploy/quickstart.md) to deploy Dynamo Cloud first. - **Container Images**: The deployment files currently require access to `nvcr.io/nvidian/nim-llm-dev/trtllm-runtime`. If you don't have access, build and push your own image: ```bash ./container/build.sh --framework tensorrtllm # Tag and push to your container registry # Update the image references in the YAML files ``` - **Port Forwarding**: After deployment, forward the frontend service to access the API: ```bash kubectl port-forward deployment/trtllm-v1-disagg-frontend- 8080:8000 ``` #### Deploy to Kubernetes Example with disagg: Export the NAMESPACE you used in your Dynamo Cloud Installation. ```bash cd dynamo cd components/backends/trtllm/deploy kubectl apply -f disagg.yaml -n $NAMESPACE ``` To change `DYN_LOG` level, edit the yaml file by adding ```yaml ... spec: envs: - name: DYN_LOG value: "debug" # or other log levels ... ``` ### 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). ## Request Migration You can enable [request migration](../../../docs/architecture/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.trtllm ... --migration-limit=3 ``` This allows a request to be migrated up to 3 times before failing. See the [Request Migration Architecture](../../../docs/architecture/request_migration.md) documentation for details on how this works. ## 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 `python3 -m dynamo.frontend `. ## 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)