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# Llama 4 Maverick Instruct with Eagle Speculative Decoding on SLURM

This guide demonstrates how to deploy Llama 4 Maverick Instruct with Eagle Speculative Decoding on GB200x4 nodes. We will be following the [multi-node deployment instructions](./multinode/multinode-examples.md) to set up the environment for the following scenarios:

- **Aggregated Serving:**
  Deploy the entire Llama 4 model on a single GB200x4 node for end-to-end serving.

- **Disaggregated Serving:**
  Distribute the workload across two GB200x4 nodes:
    - One node runs the decode worker.
    - The other node runs the prefill worker.

For advanced control over how requests are routed between prefill and decode workers in disaggregated mode, refer to the [Disaggregation Strategy](./README.md#disaggregation-strategy) section.

## Notes
* To run Eagle Speculative Decoding with Llama 4, ensure the container meets the following criteria:
  * Built with a version of TensorRT-LLM based on the 0.21 release [Link](https://github.com/NVIDIA/TensorRT-LLM/tree/release/0.21)
  * The TensorRT-LLM build includes the changes from this PR [Link](https://github.com/NVIDIA/TensorRT-LLM/pull/5975)
* If you need to download model weights off huggingface, make sure you run the command `huggingface-cli login` and have access to the necessary gated models.


## Setup

Assuming you have already allocated your nodes via `salloc`, and are
inside an interactive shell on one of the allocated nodes, set the
following environment variables based:

```bash
cd $DYNAMO_ROOT/examples/tensorrt_llm

export IMAGE="<dynamo_trtllm_image>"
# export MOUNTS="${PWD}/:/mnt,/lustre:/lustre"
export MOUNTS="${PWD}/:/mnt"
export MODEL_PATH="nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8"
export SERVED_MODEL_NAME="nvidia/Llama-4-Maverick-17B-128E-Instruct-FP8"
```

See [this](./multinode/multinode-examples.md#setup) section from multinode guide to learn more about the above options.


## Aggregated Serving
```bash
export NUM_NODES=1
export ENGINE_CONFIG="/mnt/engine_configs/llama4/eagle/eagle_agg.yaml"
./multinode/srun_aggregated.sh
```
* Known Issue: In Aggregated Serving, setting `max_num_tokens` to higher values (e.g. `max_num_tokens: 8448`) can lead to Out of Memory (OOM) errors. This is being investigated by the TRTLLM team.

## Disaggregated Serving

```bash
export NUM_PREFILL_NODES=1
export PREFILL_ENGINE_CONFIG="/mnt/engine_configs/llama4/eagle/eagle_prefill.yaml"
export NUM_DECODE_NODES=1
export DECODE_ENGINE_CONFIG="/mnt/engine_configs/llama4/eagle/eagle_decode.yaml"
./multinode/srun_disaggregated.sh
```
* Known Issue: In Aggregated Serving, setting `max_num_tokens` to higher values (e.g. `max_num_tokens: 8448`) can lead to Out of Memory (OOM) errors. This is being investigated by the TRTLLM team.


## Example Request

See [here](./multinode/multinode-examples.md#example-request) to learn how to send a request to the deployment.