dgx-spark-playbooks/nvidia/trt-llm/README.md

TRT LLM for Inference

Install and configure TRT LLM to run on a single Spark or on two Sparks

Table of Contents

• Overview
• Single Spark
  • Step 1. Configure Docker permissions
  • Step 2. Verify environment prerequisites
  • Step 3. Set environment variables
  • Step 4. Validate TensorRT-LLM installation
  • Step 5. Create cache directory
  • Step 6. Validate setup with quickstart_advanced
  • Step 7. Validate setup with quickstart_multimodal
  • Step 8. Serve LLM with OpenAI-compatible API
  • Step 9. Troubleshooting
  • Step 10. Cleanup and rollback
• Run on two Sparks
  • Step 1. Configure Docker permissions
  • Step 2. Configure network connectivity
  • Step 3. Install NVIDIA Container Toolkit
  • Step 4. Enable resource advertising
  • Step 5. Initialize Docker Swarm
  • Step 6. Join worker nodes and deploy
  • Step 7. Create hosts file
  • Step 8. Find your Docker container ID
  • Step 8. Generate configuration file
  • Step 9. Download model
  • Step 10. Serve the model
  • Step 11. Validate API server
  • Step 12. Troubleshooting
  • Step 14. Cleanup and rollback
  • Step 15. Next steps

---

Overview

Basic idea

NVIDIA TensorRT-LLM (TRT-LLM) is an open-source library for optimizing and accelerating large language model (LLM) inference on NVIDIA GPUs.

It provides highly efficient kernels, memory management, and parallelism strategies—like tensor, pipeline, and sequence parallelism—so developers can serve LLMs with lower latency and higher throughput.

TRT-LLM integrates with frameworks like Hugging Face and PyTorch, making it easier to deploy state-of-the-art models at scale.

What you'll accomplish

You'll set up TensorRT-LLM to optimize and deploy large language models on NVIDIA Spark with Blackwell GPUs, achieving significantly higher throughput and lower latency than standard PyTorch inference through kernel-level optimizations, efficient memory layouts, and advanced quantization.

What to know before starting

• Python proficiency and experience with PyTorch or similar ML frameworks
• Command-line comfort for running CLI tools and Docker containers
• Basic understanding of GPU concepts including VRAM, batching, and quantization (FP16/INT8)
• Familiarity with NVIDIA software stack (CUDA Toolkit, drivers)
• Experience with inference servers and containerized environments

Prerequisites

• NVIDIA Spark device with Blackwell architecture GPUs
• NVIDIA drivers compatible with CUDA 12.x: nvidia-smi
• Docker installed and GPU support configured: docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi
• Hugging Face account with token for model access: echo $HF_TOKEN
• Sufficient GPU VRAM (16GB+ recommended for 70B models)
• Internet connectivity for downloading models and container images
• Network: open TCP ports 8355 (LLM) and 8356 (VLM) on host for OpenAI-compatible serving

Ancillary files

All required assets can be found here on GitHub

• discover-sparks.sh — script to automatically discover and configure SSH between Spark nodes
• trtllm-mn-entrypoint.sh — container entrypoint script for multi-node setup
• docker-compose.yml — Docker Compose configuration for multi-node deployment

Model Support Matrix

The following models are supported with TensorRT-LLM on Spark. All listed models are available and ready to use:

Model	Quantization	Support Status	HF Handle
GPT-OSS-20B	MXFP4	✅	openai/gpt-oss-20b
GPT-OSS-120B	MXFP4	✅	openai/gpt-oss-120b
Llama-3.1-8B-Instruct	FP8	✅	nvidia/Llama-3.1-8B-Instruct-FP8
Llama-3.1-8B-Instruct	NVFP4	✅	nvidia/Llama-3.1-8B-Instruct-FP4
Llama-3.3-70B-Instruct	NVFP4	✅	nvidia/Llama-3.3-70B-Instruct-FP4
Qwen3-8B	FP8	✅	nvidia/Qwen3-8B-FP8
Qwen3-8B	NVFP4	✅	nvidia/Qwen3-8B-FP4
Qwen3-14B	FP8	✅	nvidia/Qwen3-14B-FP8
Qwen3-14B	NVFP4	✅	nvidia/Qwen3-14B-FP4
Phi-4-multimodal-instruct	FP8	✅	nvidia/Phi-4-multimodal-instruct-FP8
Phi-4-multimodal-instruct	NVFP4	✅	nvidia/Phi-4-multimodal-instruct-FP4
Phi-4-reasoning-plus	FP8	✅	nvidia/Phi-4-reasoning-plus-FP8
Phi-4-reasoning-plus	NVFP4	✅	nvidia/Phi-4-reasoning-plus-FP4
Llama-3_3-Nemotron-Super-49B-v1_5	FP8	✅	nvidia/Llama-3_3-Nemotron-Super-49B-v1_5-FP8
Qwen3-30B-A3B	NVFP4	✅	nvidia/Qwen3-30B-A3B-FP4
Qwen2.5-VL-7B-Instruct	FP8	✅	nvidia/Qwen2.5-VL-7B-Instruct-FP8
Qwen2.5-VL-7B-Instruct	NVFP4	✅	nvidia/Qwen2.5-VL-7B-Instruct-FP4
Llama-4-Scout-17B-16E-Instruct	NVFP4	✅	nvidia/Llama-4-Scout-17B-16E-Instruct-FP4
Qwen3-235B-A22B (two Sparks only)	NVFP4	✅	nvidia/Qwen3-235B-A22B-FP4

Note: You can use the NVFP4 Quantization documentation to generate your own NVFP4-quantized checkpoints for your favorite models. This enables you to take advantage of the performance and memory benefits of NVFP4 quantization even for models not already published by NVIDIA.

Reminder: not all model architectures are supported for NVFP4 quantization.

Time & risk

Duration: 45-60 minutes for setup and API server deployment

Risk level: Medium - container pulls and model downloads may fail due to network issues

Rollback: Stop inference servers and remove downloaded models to free resources.

Single Spark

Step 1. Configure Docker permissions

To easily manage containers without sudo, you must be in the docker group. If you choose to skip this step, you will need to run Docker commands with sudo.

Open a new terminal and test Docker access. In the terminal, run:

docker ps

If you see a permission denied error (something like permission denied while trying to connect to the Docker daemon socket), add your user to the docker group:

sudo usermod -aG docker $USER

Warning

: After running usermod, you must log out and log back in to start a new session with updated group permissions.

Step 2. Verify environment prerequisites

Confirm your Spark device has the required GPU access and network connectivity for downloading models and containers.

## Check GPU visibility and driver
nvidia-smi

## Verify Docker GPU support
docker run --rm --gpus all nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev nvidia-smi

Step 3. Set environment variables

Set HF_TOKEN for model access.

export HF_TOKEN=<your-huggingface-token>

Step 4. Validate TensorRT-LLM installation

After confirming GPU access, verify that TensorRT-LLM can be imported inside the container.

docker run --rm -it --gpus all \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  python -c "import tensorrt_llm; print(f'TensorRT-LLM version: {tensorrt_llm.__version__}')"

Expected output:

[TensorRT-LLM] TensorRT-LLM version: 1.1.0rc3
TensorRT-LLM version: 1.1.0rc3

Step 5. Create cache directory

Set up local caching to avoid re-downloading models on subsequent runs.

## Create Hugging Face cache directory
mkdir -p $HOME/.cache/huggingface/

Step 6. Validate setup with quickstart_advanced

This quickstart validates your TensorRT-LLM setup end-to-end by testing model loading, inference engine initialization, and GPU execution with real text generation. It's the fastest way to confirm everything works before starting the inference API server.

LLM quickstart example

Llama 3.1 8B Instruct

export MODEL_HANDLE="nvidia/Llama-3.1-8B-Instruct-FP4"

docker run \
  -e MODEL_HANDLE=$MODEL_HANDLE \
  -e HF_TOKEN=$HF_TOKEN \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
  --gpus=all --ipc=host --network host \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
    hf download $MODEL_HANDLE && \
    python examples/llm-api/quickstart_advanced.py \
      --model_dir $MODEL_HANDLE \
      --prompt "Paris is great because" \
      --max_tokens 64
    '

GPT-OSS 20B

export MODEL_HANDLE="openai/gpt-oss-20b"

docker run \
  -e MODEL_HANDLE=$MODEL_HANDLE \
  -e HF_TOKEN=$HF_TOKEN \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
  --gpus=all --ipc=host --network host \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
    export TIKTOKEN_ENCODINGS_BASE="/tmp/harmony-reqs" && \
    mkdir -p $TIKTOKEN_ENCODINGS_BASE && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken && \
    hf download $MODEL_HANDLE && \
    python examples/llm-api/quickstart_advanced.py \
      --model_dir $MODEL_HANDLE \
      --prompt "Paris is great because" \
      --max_tokens 64
    '

GPT-OSS 120B

export MODEL_HANDLE="openai/gpt-oss-120b"

docker run \
  -e MODEL_HANDLE=$MODEL_HANDLE \
  -e HF_TOKEN=$HF_TOKEN \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
  --gpus=all --ipc=host --network host \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
    export TIKTOKEN_ENCODINGS_BASE="/tmp/harmony-reqs" && \
    mkdir -p $TIKTOKEN_ENCODINGS_BASE && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken && \
    hf download $MODEL_HANDLE && \
    python examples/llm-api/quickstart_advanced.py \
      --model_dir $MODEL_HANDLE \
      --prompt "Paris is great because" \
      --max_tokens 64
    '

Step 7. Validate setup with quickstart_multimodal

VLM quickstart example

This demonstrates vision-language model capabilities by running inference with image understanding. The example uses multimodal inputs to validate both text and vision processing pipelines.

Qwen2.5-VL-7B-Instruct

export MODEL_HANDLE="nvidia/Qwen2.5-VL-7B-Instruct-FP4"

docker run \
  -e MODEL_HANDLE=$MODEL_HANDLE \
  -e HF_TOKEN=$HF_TOKEN \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
  --gpus=all --ipc=host --network host \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
  python3 examples/llm-api/quickstart_multimodal.py \
    --model_dir $MODEL_HANDLE \
    --modality image \
    --media "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png" \
    --prompt "What is happening in this image?" \
  '

Phi-4-multimodal-instruct

This model requires LoRA (Low-Rank Adaptation) configuration as it uses parameter-efficient fine-tuning. The --load_lora flag enables loading the LoRA weights that adapt the base model for multimodal instruction following.

export MODEL_HANDLE="nvidia/Phi-4-multimodal-instruct-FP4"

docker run \
  -e MODEL_HANDLE=$MODEL_HANDLE \
  -e HF_TOKEN=$HF_TOKEN \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  --rm -it --ulimit memlock=-1 --ulimit stack=67108864 \
  --gpus=all --ipc=host --network host \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
  python3 examples/llm-api/quickstart_multimodal.py \
    --model_type phi4mm \
    --model_dir $MODEL_HANDLE \
    --modality image \
    --media "https://huggingface.co/datasets/YiYiXu/testing-images/resolve/main/seashore.png" \
    --prompt "What is happening in this image?" \
    --load_lora \
    --auto_model_name Phi4MMForCausalLM
  '

Note: If you hit a host OOM during downloads or first run, free the OS page cache on the host (outside the container) and retry:

sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'

Step 8. Serve LLM with OpenAI-compatible API

Serve with OpenAI-compatible API via trtllm-serve:

Llama 3.1 8B Instruct

export MODEL_HANDLE="nvidia/Llama-3.1-8B-Instruct-FP4"

docker run --name trtllm_llm_server --rm -it --gpus all --ipc host --network host \
  -e HF_TOKEN=$HF_TOKEN \
  -e MODEL_HANDLE="$MODEL_HANDLE" \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
    hf download $MODEL_HANDLE && \
    cat > /tmp/extra-llm-api-config.yml <<EOF
print_iter_log: false
kv_cache_config:
  dtype: "auto"
  free_gpu_memory_fraction: 0.9
cuda_graph_config:
  enable_padding: true
disable_overlap_scheduler: true
EOF
    trtllm-serve "$MODEL_HANDLE" \
      --max_batch_size 64 \
      --trust_remote_code \
      --port 8355 \
      --extra_llm_api_options /tmp/extra-llm-api-config.yml
  '

GPT-OSS 20B

export MODEL_HANDLE="openai/gpt-oss-20b"

docker run --name trtllm_llm_server --rm -it --gpus all --ipc host --network host \
  -e HF_TOKEN=$HF_TOKEN \
  -e MODEL_HANDLE="$MODEL_HANDLE" \
  -v $HOME/.cache/huggingface/:/root/.cache/huggingface/ \
  nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev \
  bash -c '
    export TIKTOKEN_ENCODINGS_BASE="/tmp/harmony-reqs" && \
    mkdir -p $TIKTOKEN_ENCODINGS_BASE && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/o200k_base.tiktoken && \
    wget -P $TIKTOKEN_ENCODINGS_BASE https://openaipublic.blob.core.windows.net/encodings/cl100k_base.tiktoken && \
    hf download $MODEL_HANDLE && \
    cat > /tmp/extra-llm-api-config.yml <<EOF
print_iter_log: false
kv_cache_config:
  dtype: "auto"
  free_gpu_memory_fraction: 0.9
cuda_graph_config:
  enable_padding: true
disable_overlap_scheduler: true
EOF
    trtllm-serve "$MODEL_HANDLE" \
      --max_batch_size 64 \
      --trust_remote_code \
      --port 8355 \
      --extra_llm_api_options /tmp/extra-llm-api-config.yml
  '

Minimal OpenAI-style chat request. Run this from a separate terminal.

curl -s http://localhost:8355/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "'"$MODEL_HANDLE"'",
    "messages": [{"role": "user", "content": "Paris is great because"}],
    "max_tokens": 64
  }'

Step 9. Troubleshooting

Common issues and their solutions:

Symptom	Cause	Fix
OOM during weight loading (e.g., Nemotron Super 49B)	Parallel weight-loading memory pressure	export TRT_LLM_DISABLE_LOAD_WEIGHTS_IN_PARALLEL=1
"CUDA out of memory"	GPU VRAM insufficient for model	Reduce free_gpu_memory_fraction: 0.9 or batch size or use smaller model
"Model not found" error	HF_TOKEN invalid or model inaccessible	Verify token and model permissions
Container pull timeout	Network connectivity issues	Retry pull or use local mirror
Import tensorrt_llm fails	Container runtime issues	Restart Docker daemon and retry

Step 10. Cleanup and rollback

Remove downloaded models and containers to free up space when testing is complete.

Warning: This will delete all cached models and may require re-downloading for future runs.

## Remove Hugging Face cache
sudo chown -R "$USER:$USER" "$HOME/.cache/huggingface"
rm -rf $HOME/.cache/huggingface/

## Clean up Docker images
docker image prune -f
docker rmi nvcr.io/nvidia/tensorrt-llm/release:spark-single-gpu-dev

Run on two Sparks

Step 1. Configure Docker permissions

To easily manage containers without sudo, you must be in the docker group. If you choose to skip this step, you will need to run Docker commands with sudo.

Open a new terminal and test Docker access. In the terminal, run:

docker ps

If you see a permission denied error (something like permission denied while trying to connect to the Docker daemon socket), add your user to the docker group:

sudo usermod -aG docker $USER

Warning

: After running usermod, you must log out and log back in to start a new session with updated group permissions.

Step 2. Configure network connectivity

You have two options for configuring network connectivity between your DGX Spark nodes:

Option 1: Automatic IP assignment (recommended)

Follow these steps on both DGX Spark nodes to configure network interfaces using netplan:

## Create the netplan configuration file
sudo tee /etc/netplan/40-cx7.yaml > /dev/null <<EOF
network:
  version: 2
  ethernets:
    enp1s0f0np0:
      link-local: [ ipv4 ]
    enp1s0f1np1:
      link-local: [ ipv4 ]
EOF

## Set appropriate permissions
sudo chmod 600 /etc/netplan/40-cx7.yaml

## Apply the configuration
sudo netplan apply

Option 2: Manual IP assignment (advanced)

First, identify which network ports are available and up:

## Check network port status
ibdev2netdev

Example output:

roceP2p1s0f0 port 1 ==> enP2p1s0f0np0 (Up)
roceP2p1s0f1 port 1 ==> enP2p1s0f1np1 (Down)
rocep1s0f0 port 1 ==> enp1s0f0np0 (Up)
rocep1s0f1 port 1 ==> enp1s0f1np1 (Down)

Use an interface that shows as "(Up)" in your output. In this example, we'll use enP2p1s0f0np0.

On Node 1:

## Assign static IP and bring up interface
sudo ip addr add 192.168.100.10/24 dev enp1s0f0np0
sudo ip link set enp1s0f0np0 up

On Node 2:

## Assign static IP and bring up interface
sudo ip addr add 192.168.100.11/24 dev enp1s0f0np0
sudo ip link set enp1s0f0np0 up

Set up passwordless SSH authentication

Run the DGX Spark discover-sparks.sh script on both nodes to automatically configure SSH:

bash ./discover-sparks.sh

Expected output similar to the below, with different IPs and node names. The first time you run the script, you'll be prompted for your password for each node.

Found: 192.168.100.10 (spark-1b3b.local)
Found: 192.168.100.11 (spark-1d84.local)

Copying your SSH public key to all discovered nodes using ssh-copy-id.
You may be prompted for your password on each node.
Copying SSH key to 192.168.100.10 ...
Copying SSH key to 192.168.100.11 ...
nvidia@192.168.100.11's password:

SSH key copy process complete. These two sparks can now talk to each other.

Step 3. Install NVIDIA Container Toolkit

Ensure the NVIDIA drivers and the NVIDIA Container Toolkit are installed on each node (both manager and workers) that will provide GPU resources. This package enables Docker containers to access the host's GPU hardware. Ensure you complete the installation steps, including the Docker configuration for NVIDIA Container Toolkit.

Step 4. Enable resource advertising

Modify the NVIDIA Container Runtime to advertise the GPUs to the Swarm by uncommenting the swarm-resource line in the config.toml file. You can do this either with your preferred text editor (e.g., vim, nano...) or with the following command:

sudo sed -i 's/^#\s*\(swarm-resource\s*=\s*".*"\)/\1/' /etc/nvidia-container-runtime/config.toml

To apply the changes, restart the Docker daemon

sudo systemctl restart docker

Step 5. Initialize Docker Swarm

On whichever node you want to use as primary, run the following swarm initialization command

docker swarm init --advertise-addr $(ip -o -4 addr show enp1s0f0np0 | awk '{print $4}' | cut -d/ -f1) $(ip -o -4 addr show enp1s0f1np1 | awk '{print $4}' | cut -d/ -f1)

The typical output of the above would be similar to the following:

Swarm initialized: current node (node-id) is now a manager.

To add a worker to this swarm, run the following command:

    docker swarm join --token <worker-token> <advertise-addr>:<port>

To add a manager to this swarm, run 'docker swarm join-token manager' and follow the instructions.

Step 6. Join worker nodes and deploy

Now we can proceed with setting up other nodes of your cluster.

Run the command suggested by the docker swarm init on each worker node to join the Docker swarm

docker swarm join --token <worker-token> <advertise-addr>:<port>

On your primary node, deploy the TRT-LLM multi-node stack by downloading the docker-compose.yml and trtllm-mn-entrypoint.sh files into your home directory and running the following command:

chmod +x $HOME/trtllm-mn-entrypoint.sh
docker stack deploy -c $HOME/docker-compose.yml trtllm-multinode

Note: Ensure you download both files into the same directory from which you are running the command.

You can verify the status of your worker nodes using the following

docker stack ps trtllm-multinode

If everything is healthy, you should see a similar output to the following:

nvidia@spark-1b3b:~$ docker stack ps trtllm-multinode
ID             NAME                            IMAGE                                          NODE         DESIRED STATE   CURRENT STATE             ERROR     PORTS
oe9k5o6w41le   trtllm-multinode_trtllm.1       nvcr.io/nvidia/tensorrt-llm/release:1.0.0rc3   spark-1d84   Running         Running 2 minutes ago
phszqzk97p83   trtllm-multinode_trtllm.2       nvcr.io/nvidia/tensorrt-llm/release:1.0.0rc3   spark-1b3b   Running         Running 2 minutes ago

Step 7. Create hosts file

You can check the available nodes using docker node ls

nvidia@spark-1b3b:~$ docker node ls
ID                            HOSTNAME     STATUS    AVAILABILITY   MANAGER STATUS   ENGINE VERSION
hza2b7yisatqiezo33zx4in4i *   spark-1b3b   Ready     Active         Leader           28.3.3
m1k22g3ktgnx36qz4jg5fzhr4     spark-1d84   Ready     Active                          28.3.3

Generate a file containing all Docker Swarm node addresses for MPI operations, and then copy it over to your container:

docker node ls --format '{{.ID}}' | xargs -n1 docker node inspect --format '{{ .Status.Addr }}' > ~/openmpi-hostfile
docker cp ~/openmpi-hostfile $(docker ps -q -f name=trtllm-multinode):/etc/openmpi-hostfile

Step 8. Find your Docker container ID

You can use docker ps to find your Docker container ID. Alternatively, you can save the container ID in a variable:

export TRTLLM_MN_CONTAINER=$(docker ps -q -f name=trtllm-multinode)

Step 8. Generate configuration file

docker exec $TRTLLM_MN_CONTAINER bash -c 'cat <<EOF > /tmp/extra-llm-api-config.yml
print_iter_log: false
kv_cache_config:
  dtype: "auto"
  free_gpu_memory_fraction: 0.9
cuda_graph_config:
  enable_padding: true
EOF'

Step 9. Download model

## Need to specify huggingface token for model download.
export HF_TOKEN=<your-huggingface-token>

docker exec \
  -e MODEL="nvidia/Qwen3-235B-A22B-FP4" \
  -e HF_TOKEN=$HF_TOKEN \
  -it $TRTLLM_MN_CONTAINER bash -c 'mpirun -x HF_TOKEN bash -c "huggingface-cli download $MODEL"'

Step 10. Serve the model

docker exec \
  -e MODEL="nvidia/Qwen3-235B-A22B-FP4" \
  -e HF_TOKEN=$HF_TOKEN \
  -it $TRTLLM_MN_CONTAINER bash -c '
    mpirun -x HF_TOKEN trtllm-llmapi-launch trtllm-serve $MODEL \
      --tp_size 2 \
      --backend pytorch \
      --max_num_tokens 32768 \
      --max_batch_size 4 \
      --extra_llm_api_options /tmp/extra-llm-api-config.yml \
      --port 8000'

This will start the TensorRT-LLM server on port 8000. You can then make inference requests to http://localhost:8000 using the OpenAI-compatible API format.

Expected output: Server startup logs and ready message.

Step 11. Validate API server

Verify successful deployment by checking container status and testing the API endpoint.

docker stack ps trtllm-multinode

Expected output: Two running containers in the stack across different nodes.

Once the server is running, you can test it with a CURL request. Please ensure the CURL request is run on the primary node where you previously ran Step 11.

curl -X POST http://localhost:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia/Qwen3-235B-A22B-FP4",
    "prompt": "What is artificial intelligence?",
    "max_tokens": 100,
    "temperature": 0.7,
    "stream": false
  }'

Expected output: JSON response with generated text completion.

Step 12. Troubleshooting

Symptom	Cause	Fix
MPI hostname test returns single hostname	Network connectivity issues	Verify both nodes are on reachable IP addresses
"Permission denied" on HuggingFace download	Invalid or missing HF_TOKEN	Set valid token: export HF_TOKEN=<TOKEN>
"CUDA out of memory" errors	Insufficient GPU memory	Reduce --max_batch_size or --max_num_tokens
Container exits immediately	Missing entrypoint script	Ensure trtllm-mn-entrypoint.sh download succeeded and has executable permissions, also ensure you are not running the container already on your node. If port 2233 is already utilized, the entrypoint script will not start.

Step 14. Cleanup and rollback

Stop and remove containers by using the following command on the leader node:

docker stack rm trtllm-multinode

Warning: This removes all inference data and performance reports. Copy /opt/*perf-report.json files before cleanup if needed.

Remove downloaded models to free disk space:

rm -rf $HOME/.cache/huggingface/hub/models--nvidia--Qwen3*

Step 15. Next steps

Compare performance metrics between speculative decoding and baseline reports to quantify speed improvements. Use the multi-node setup as a foundation for deploying other large models requiring tensor parallelism, or scale to additional nodes for higher throughput workloads.