Prepare the DGX Spark host and local services

Prepare the DGX Spark host environment

Confirm that the Arm CPU and NVIDIA GPU are visible:

    

        
        
uname -m
nvidia-smi

    

The expected CPU architecture is:

    

        
        aarch64

        
    

Confirm Docker GPU access:

    

        
        
docker run --rm --gpus all ubuntu nvidia-smi

    

Install the NVIDIA driver and NVIDIA Container Toolkit so that this container can access the GPU.

You don’t need to install the vLLM Python package or start a vLLM server directly on the DGX Spark host. The project’s compose.yaml pulls a container image that already includes vLLM and starts the local inference server for you.

Configure Ollama for local embeddings

Unlike vLLM, Ollama isn’t included as a service in the project’s compose.yaml. Install and run Ollama separately on the DGX Spark host before starting the reference runtime.

Install Ollama using the official Linux installer :

    

        
        
curl -fsSL https://ollama.com/install.sh | sh

    

The project containers connect to Ollama through the Docker host gateway. Create a systemd override that configures Ollama to listen on the host interfaces:

    

        
        
sudo install -d -m 0755 /etc/systemd/system/ollama.service.d
printf '%s\n' '[Service]' 'Environment="OLLAMA_HOST=0.0.0.0:11434"' | \
  sudo tee /etc/systemd/system/ollama.service.d/override.conf

    

Confirm the override file:

    

        
        
sudo systemctl cat ollama

    

The output should include the override:

    

        
        # /etc/systemd/system/ollama.service.d/override.conf
[Service]
Environment="OLLAMA_HOST=0.0.0.0:11434"

        
    

Reload systemd and restart Ollama:

    

        
        
sudo systemctl daemon-reload
sudo systemctl enable --now ollama
sudo systemctl restart ollama

    

Pull the embedding model that you’ll use:

    

        
        
ollama pull nomic-embed-text

    

Confirm that Ollama lists the model:

    

        
        
curl http://127.0.0.1:11434/api/tags

    

The output is similar to:

    

        
        {
  "models": [
    {
      "name": "nomic-embed-text:latest",
      "capabilities": ["embedding"]
    }
  ]
}

        
    

Start Qdrant for persistent vector storage

Create a Docker volume so that vector data remains available when the Qdrant container is replaced:

    

        
        
docker volume create openclaw-qdrant-data

    

Start Qdrant on the host using the official container image :

    

        
        
docker run -d \
  --name openclaw-qdrant \
  --restart unless-stopped \
  -p 6333:6333 \
  -p 6334:6334 \
  -v openclaw-qdrant-data:/qdrant/storage \
  qdrant/qdrant

    

The docker run command creates the container and is needed only the first time. If openclaw-qdrant already exists but is stopped, start it instead:

    

        
        
docker start openclaw-qdrant

    

Confirm that the Qdrant API responds:

    

        
        
curl http://127.0.0.1:6333/collections

    

The output is similar to:

    

        
        {
  "result": {"collections": []},
  "status": "ok"
}

        
    

The empty list is expected at this stage before the reference runtime creates its collections. The runtime creates collections when you save or ingest content.

Clone the reference repository

Clone the repository and check out the release that you’ll use:

    

        
        
git clone https://github.com/odincodeshen/openclaw-arm-continuum.git
cd openclaw-arm-continuum
git checkout v1.2

    

The tag fixes the tutorial source version. Unversioned container images and model artifacts can still change when downloaded.

What you’ve accomplished and what’s next

You’ve prepared the DGX Spark host, configured local embeddings, started Qdrant, and checked out the reference repository.

Next, you’ll configure the Telegram bot and start the OpenClaw runtime.