Deploy NVIDIA NIM for LLM Inference

Verify environment prerequisites

Check that your system meets the basic requirements for running GPU-enabled containers.

nvidia-smi
docker --version
docker run --rm --gpus all nvcr.io/nvidia/cuda:13.0.1-devel-ubuntu24.04 nvidia-smi

Expected output should show GPU details from nvidia-smi inside the container.

If you see a permission-denied error connecting to the Docker daemon socket, add your user to the docker group so you do not need sudo:

sudo usermod -aG docker $USER
newgrp docker

Configure NGC authentication

Set up access to NVIDIA's container registry using your NGC API key.

export NGC_API_KEY="<YOUR_NGC_API_KEY>"
echo "$NGC_API_KEY" | docker login nvcr.io --username '$oauthtoken' --password-stdin

Select and configure NIM container

Choose a specific LLM NIM from NGC and set up local caching for model assets. The default image below matches the Supported hardware platforms matrix; swap IMG_NAME for another NIM from Find model recipes when needed.

export CONTAINER_NAME="nim-llm-demo"
export IMG_NAME="nvcr.io/nim/meta/llama-3.1-8b-instruct-dgx-spark:latest"
export LOCAL_NIM_CACHE=~/.cache/nim
export LOCAL_NIM_WORKSPACE=~/.local/share/nim/workspace
mkdir -p "$LOCAL_NIM_WORKSPACE"
chmod -R a+w "$LOCAL_NIM_WORKSPACE"
mkdir -p "$LOCAL_NIM_CACHE"
chmod -R a+w "$LOCAL_NIM_CACHE"

Launch NIM container

Start the containerized LLM service with GPU acceleration and shared memory for model loading.

docker run -it --rm --name=$CONTAINER_NAME \
  --gpus all \
  --shm-size=16GB \
  -e NGC_API_KEY=$NGC_API_KEY \
  -v "$LOCAL_NIM_CACHE:/opt/nim/.cache" \
  -v "$LOCAL_NIM_WORKSPACE:/opt/nim/workspace" \
  -p 8000:8000 \
  $IMG_NAME

The container downloads the model on first run and may take several minutes to start. Look for startup messages indicating the service is ready.

Validate inference endpoint

Test the deployed service with a basic chat completion request. Run the following curl command in a new terminal while the container is running.

curl -X 'POST' \
    'http://0.0.0.0:8000/v1/chat/completions' \
    -H 'accept: application/json' \
    -H 'Content-Type: application/json' \
    -d '{
      "model": "meta/llama-3.1-8b-instruct",
      "messages": [
        {
          "role":"system",
          "content":"detailed thinking on"
        },
        {
          "role":"user",
          "content":"Can you write me a song?"
        }
      ],
      "top_p": 1,
      "n": 1,
      "max_tokens": 15,
      "frequency_penalty": 1.0,
      "stop": ["hello"]
    }'

Expected output should be a JSON response with a choices array containing generated text.

From another device on the same network, replace 0.0.0.0 with your hardware platform's reachable address.

Cleanup

Stop and remove the container when you are done testing. Cleanup is optional rollback — not required to complete the playbook.

docker stop $CONTAINER_NAME
docker rm $CONTAINER_NAME

To remove cached models and free disk space:

rm -rf "$LOCAL_NIM_CACHE"

Next steps

With a working NIM deployment, you can:

1. Integrate the API endpoint into your applications using the OpenAI-compatible interface
2. Experiment with different models from the NGC catalog (see Find model recipes)
3. Scale the deployment using container orchestration tools
4. Monitor resource usage with nvidia-smi and optimize container resource allocation

Test the integration with your preferred HTTP client or SDK to begin building applications.