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README.md

TRT LLM for Inference

Install and use TensorRT-LLM on DGX Spark

Overview
Single Spark
Run on two Sparks
Open WebUI for TensorRT-LLM
Troubleshooting

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 your DGX Spark, 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

DGX Spark device
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:1.2.0rc6 nvidia-smi
Hugging Face account with token for model access: echo $HF_TOKEN
Sufficient GPU VRAM (40GB+ 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

trtllm-mn-entrypoint.sh — container entrypoint script for multi-node setup

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
Nemotron-3-Super-120B	NVFP4	✅	`nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4`
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`
Qwen3-32B	NVFP4	✅	`nvidia/Qwen3-32B-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`
Qwen3-30B-A3B	NVFP4	✅	`nvidia/Qwen3-30B-A3B-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.
Last Updated: 03/12/2026
- Introduce Nemotron-3-Super-120B support on TRT-LLM

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 so that you don't need to run the command with sudo .

sudo usermod -aG docker $USER
newgrp docker

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:1.2.0rc6 nvidia-smi

Step 3. Set environment variables

## Set `HF_TOKEN` for model access.
export HF_TOKEN=<your-huggingface-token>

export DOCKER_IMAGE="nvcr.io/nvidia/tensorrt-llm/release:1.2.0rc6"

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 \
  $DOCKER_IMAGE \
  python -c "import tensorrt_llm; print(f'TensorRT-LLM version: {tensorrt_llm.__version__}')"

Expected output:

[TensorRT-LLM] TensorRT-LLM version: 1.2.0rc6
TensorRT-LLM version: 1.2.0rc6

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 \
  $DOCKER_IMAGE \
  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 \
  $DOCKER_IMAGE \
  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 \
  $DOCKER_IMAGE \
  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.

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 \
  $DOCKER_IMAGE \
  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/ \
  $DOCKER_IMAGE \
  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/ \
  $DOCKER_IMAGE \
  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. 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 $DOCKER_IMAGE

Run on two Sparks

Step 1. Configure network connectivity

Follow the network setup instructions from the Connect two Sparks playbook to establish connectivity between your DGX Spark nodes.

This includes:

Physical QSFP cable connection
Network interface configuration (automatic or manual IP assignment)
Passwordless SSH setup
Network connectivity verification

Step 2. 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

sudo usermod -aG docker $USER
newgrp docker

Repeat this step on both nodes.

Step 3. Create OpenMPI hostfile

Create a hostfile with the IP addresses of both nodes for MPI operations. On each node, get the IP address of your network interface:

ip a show enp1s0f0np0

Or if you're using the second interface:

ip a show enp1s0f1np1

Look for the inet line to find the IP address (e.g., 192.168.1.10/24).

On your primary node, create the hostfile ~/openmpi-hostfile with the collected IPs:

cat > ~/openmpi-hostfile <<EOF
192.168.1.10
192.168.1.11
EOF

Replace the IP addresses with your actual node IPs.

Step 4. Start containers on both nodes

On each node (primary and worker), run the following command to start the TRT-LLM container:

  docker run -d --rm \
  --name trtllm-multinode \
  --gpus '"device=all"' \
  --network host \
  --ulimit memlock=-1 \
  --ulimit stack=67108864 \
  --device /dev/infiniband:/dev/infiniband \
  -e UCX_NET_DEVICES="enp1s0f0np0,enp1s0f1np1" \
  -e NCCL_SOCKET_IFNAME="enp1s0f0np0,enp1s0f1np1" \
  -e OMPI_MCA_btl_tcp_if_include="enp1s0f0np0,enp1s0f1np1" \
  -e OMPI_MCA_orte_default_hostfile="/etc/openmpi-hostfile" \
  -e OMPI_MCA_rmaps_ppr_n_pernode="1" \
  -e OMPI_ALLOW_RUN_AS_ROOT="1" \
  -e OMPI_ALLOW_RUN_AS_ROOT_CONFIRM="1" \
  -e CPATH=/usr/local/cuda/include \
  -e TRITON_PTXAS_PATH=/usr/local/cuda/bin/ptxas \
  -v ~/.cache/huggingface/:/root/.cache/huggingface/ \
  -v ~/.ssh:/tmp/.ssh:ro \
  nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5 \
  sh -c "curl https://raw.githubusercontent.com/NVIDIA/dgx-spark-playbooks/refs/heads/main/nvidia/trt-llm/assets/trtllm-mn-entrypoint.sh | sh"

Note

Make sure to run this command on both the primary and worker nodes.

Step 5. Verify containers are running

On each node, verify the container is running:

docker ps

You should see output similar to:

CONTAINER ID   IMAGE                                                 COMMAND                  CREATED          STATUS          PORTS     NAMES
abc123def456   nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc5         "sh -c 'curl https:…"    10 seconds ago   Up 8 seconds              trtllm-multinode

Step 6. Copy hostfile to primary container

On your primary node, copy the OpenMPI hostfile into the container:

docker cp ~/openmpi-hostfile trtllm-multinode:/etc/openmpi-hostfile

Step 7. Save container reference

On your primary node, save the container name in a variable for convenience:

export TRTLLM_MN_CONTAINER=trtllm-multinode

Step 8. Generate configuration file

On your primary node, generate the configuration file inside the container:

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

We can download a model using the following command. You can replace nvidia/Qwen3-235B-A22B-FP4 with the model of your choice.

## 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 "hf download $MODEL"'

Step 10. Serve the model

On your primary node, start the TensorRT-LLM server:

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 8355'

This will start the TensorRT-LLM server on port 8355. You can then make inference requests to http://localhost:8355 using the OpenAI-compatible API format.

Note

You might see a warning such as UCX WARN network device 'enp1s0f0np0' is not available, please use one or more of. You can ignore this warning if your inference is successful, as it's related to only one of your two CX-7 ports being used, and the other being left unused.

Expected output: Server startup logs and ready message.

Step 11. Validate API server

Once the server is running, you can test it with a CURL request. Run this on the primary node:

curl -s http://localhost:8355/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "nvidia/Qwen3-235B-A22B-FP4",
    "messages": [{"role": "user", "content": "Paris is great because"}],
    "max_tokens": 64
  }'

Expected output: JSON response with generated text completion.

Step 12. Cleanup and rollback

Stop and remove containers on each node. SSH to each node and run:

docker stop trtllm-multinode

Warning

This removes all inference data and performance reports. Copy any necessary files before cleanup if needed.

Remove downloaded models to free disk space on each node:

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

Step 13. Next steps

You can now deploy other models on your DGX Spark cluster.

Open WebUI for TensorRT-LLM

Step 1. Set up the prerequisites to use Open WebUI with TRT-LLM

After setting up TensorRT-LLM inference server in either single-node or multi-node configuration, you can deploy Open WebUI to interact with your models through Open WebUI. To get setup, just make sure the following is in order

TensorRT-LLM inference server running and accessible at http://localhost:8355
Docker installed and configured (see earlier steps)
Port 3000 available on your DGX Spark

Step 2. Launch Open WebUI container

Run the following command on the DGX Spark node where you have the TensorRT-LLM inference server running. For multi-node setup, this would be the primary node.

Note

If you used a different port for your OpenAI-compatible API server, adjust the OPENAI_API_BASE_URL="http://localhost:8355/v1" to match the IP and port of your TensorRT-LLM inference server.

docker run \
  -d \
  -e OPENAI_API_BASE_URL="http://localhost:8355/v1" \
  -v open-webui:/app/backend/data \
  --network host \
  --add-host=host.docker.internal:host-gateway \
  --name open-webui \
  --restart always \
  ghcr.io/open-webui/open-webui:main

This command:

Connects to your OpenAI-compatible API server for TensorRT-LLM at http://localhost:8355
Provides access to the Open WebUI interface at http://localhost:8080
Persists chat data in a Docker volume
Enables automatic container restart
Uses the latest Open WebUI image

Step 3. Access the Open WebUI interface

Open your web browser and navigate to:

http://localhost:8080

You should see the Open WebUI interface at http://localhost:8080 where you can:

Chat with your deployed models
Adjust model parameters
View chat history
Manage model configurations

You can select your model(s) from the dropdown menu on the top left corner. That's all you need to do to start using Open WebUI with your deployed models.

Note

If accessing from a remote machine, replace localhost with your DGX Spark's IP address.

Step 4. Cleanup and rollback

Warning

This removes all chat data and may require re-uploading for future runs.

Remove the container by using the following command:

docker stop open-webui
docker rm open-webui
docker volume rm open-webui
docker rmi ghcr.io/open-webui/open-webui:main

Troubleshooting

Common issues for running on a single Spark

Symptom	Cause	Fix
Cannot access gated repo for URL	Certain HuggingFace models have restricted access	Regenerate your HuggingFace token; and request access to the gated model on your web browser
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

Common Issues for running on two Sparks

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>`
Cannot access gated repo for URL	Certain HuggingFace models have restricted access	Regenerate your HuggingFace token; and request access to the gated model on your web browser
"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.
Error response from daemon: error while validating Root CA Certificate	System clock out of sync or expired certificates	Update system time to sync with NTP server `sudo timedatectl set-ntp true`
"invalid mount config for type 'bind'"	Missing or non-executable entrypoint script	Run `docker inspect <container_id>` to see full error message. Verify `trtllm-mn-entrypoint.sh` exists on both nodes in your home directory (`ls -la $HOME/trtllm-mn-entrypoint.sh`) and has executable permissions (`chmod +x $HOME/trtllm-mn-entrypoint.sh`)
"task: non-zero exit (255)"	Container exit with error code 255	Check container logs with `docker ps -a --filter "name=trtllm-multinode_trtllm"` to get container ID, then `docker logs <container_id>` to see detailed error messages
Docker state stuck in "Pending" with "no suitable node (insufficien...)"	Docker daemon not properly configured for GPU access	Verify steps 2-4 were completed successfully and check that `/etc/docker/daemon.json` contains correct GPU configuration
Serving model fails `ptxas fatal` errors	Model needs runtime triton kernel compilation	In Step 10, add `-x TRITON_PTXAS_PATH` to your `mpirun` command

Note

DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:

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

README.md

TRT LLM for Inference

Table of Contents

Overview

Basic idea

What you'll accomplish

What to know before starting

Prerequisites

Ancillary files

Model Support Matrix

Time & risk

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

Llama 3.1 8B Instruct

GPT-OSS 20B

GPT-OSS 120B

Step 7. Validate setup with quickstart_multimodal

Phi-4-multimodal-instruct

Step 8. Serve LLM with OpenAI-compatible API

Llama 3.1 8B Instruct

GPT-OSS 20B

Step 9. Cleanup and rollback

Run on two Sparks

Step 1. Configure network connectivity

Step 2. Configure Docker permissions

Step 3. Create OpenMPI hostfile

Step 4. Start containers on both nodes

Step 5. Verify containers are running

Step 6. Copy hostfile to primary container

Step 7. Save container reference

Step 8. Generate configuration file

Step 9. Download model

Step 10. Serve the model

Step 11. Validate API server

Step 12. Cleanup and rollback

Step 13. Next steps

Open WebUI for TensorRT-LLM

Step 1. Set up the prerequisites to use Open WebUI with TRT-LLM

Step 2. Launch Open WebUI container

Step 3. Access the Open WebUI interface

Step 4. Cleanup and rollback

Troubleshooting

Common issues for running on a single Spark

Common Issues for running on two Sparks