In a previous article, we saw how to deploy OpenClaw on Nutanix AHV. However, before you can fully enjoy this autonomous AI agent, it is crucial to prepare the ground. OpenClaw is not just a simple script: it’s an ecosystem that relies on several technological building blocks to “think” (local LLM), “execute” (Node.js) and “search” (SearXNG).

In this article, we are going to detail the step-by-step installation of all the necessary prerequisites on your virtual machine (running Ubuntu), explaining for each one why it is essential for OpenClaw to work properly.

1. NVIDIA Drivers: Unleashing GPU Power

OpenClaw can rely on large language models (LLMs) running locally. While it is technically possible to run these models on a processor (CPU), performance would be extremely slow. Installing NVIDIA drivers allows the system to leverage the graphics card (GPU) allocated to your VM via Nutanix AHV (vGPU or Passthrough), thereby ensuring fast and smooth AI inference.

Here is how to update your system and install the appropriate drivers:

# System update
sudo apt update && sudo apt upgrade -y

# NVIDIA server driver installation
sudo apt install nvidia-driver-535-server -y

# Reboot required to take effect
sudo reboot

After the reboot, check that your GPU is correctly recognized with the following command:

nvidia-smi

2. Node.js (v24): OpenClaw’s Execution Engine

The core logic of OpenClaw is developed in JavaScript/TypeScript. Node.js is the execution environment that allows running the agent’s code, managing its dependencies, and orchestrating calls between the interface, the AI model, and the various tools. We are targeting version 24.x here to ensure optimal compatibility.

# Adding the NodeSource repository and installing
curl -fsSL [https://deb.nodesource.com/setup_24.x](https://deb.nodesource.com/setup_24.x) | sudo -E bash -
sudo apt install -y nodejs

# Verifying the installed version
node --version  # should display v24.x

3. Ollama & Qwen 2.5: The Local Brain of the Operation

Ollama is the tool that will allow us to easily download and run our AI model locally. For OpenClaw, we are going to use the Qwen2.5 (7B) model. Why this choice? It’s a lightweight model (suitable for GPUs with about 8 GB of VRAM) and, above all, it is excellent at “Tool Calling”. It is this capability that allows OpenClaw to understand that it needs to execute a web search or run a script rather than simply generating text.

# Installing Ollama
curl -fsSL [https://ollama.ai/install.sh](https://ollama.ai/install.sh) | sh

# Start the Ollama service (in the background)
ollama serve &

# In another terminal, verify that Ollama detects the GPU:
nvidia-smi  # You should see ollama in the list of GPU processes

# Pull (download) the recommended model
ollama pull qwen2.5:7b

# Test the model and verify its execution on the GPU
ollama run qwen2.5:7b "say hello"

# While the model is generating a response, check VRAM usage
nvidia-smi

4. Docker: The Containerization Infrastructure

In order to extend OpenClaw’s capabilities (especially for web search which we will see next), we need third-party services. Docker allows these services to be deployed in an isolated, standardized, and fast manner, without polluting our host system with multiple complex dependencies.

# Installing initial dependencies
sudo apt update
sudo apt install -y ca-certificates curl gnupg

# Adding Docker's official GPG key
sudo install -m 0755 -d /etc/apt/keyrings
curl -fsSL [https://download.docker.com/linux/ubuntu/gpg](https://download.docker.com/linux/ubuntu/gpg) | sudo gpg --dearmor -o /etc/apt/keyrings/docker.gpg
sudo chmod a+r /etc/apt/keyrings/docker.gpg

# Configuring the Docker repository
echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.gpg] \
  [https://download.docker.com/linux/ubuntu](https://download.docker.com/linux/ubuntu) \
  $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | \
  sudo tee /etc/apt/sources.list.d/docker.list > /dev/null

# Installing Docker packages
sudo apt update
sudo apt install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin

# Adding your user to the docker group to avoid using 'sudo' every time
sudo usermod -aG docker $USER
newgrp docker

# Verifying the installation
docker --version

5. SearXNG: Giving AI Access to the Web

For OpenClaw to be a truly performant search agent, it cannot rely solely on the data contained in the Qwen model (which stops at its training date). SearXNG is a privacy-respecting metasearch engine. By installing it via Docker, we provide OpenClaw with an API (on port 8080) that it can use to retrieve real-time information from the web and source its answers.

# Create a dedicated directory for configuration
mkdir -p ~/searxng && cd ~/searxng

# Run the SearXNG container
docker run -d \
  --name searxng \
  --restart unless-stopped \
  -p 8080:8080 \
  searxng/searxng

# Verify that the container is running correctly
docker ps

# Test that the SearXNG API is responding
curl http://localhost:8080

6. Homebrew & GCC: The Compilation Tools

The OpenClaw Node.js ecosystem sometimes relies on low-level libraries or native packages that need to be compiled directly on your machine during installation. Homebrew (the well-known package manager on macOS, also very handy on Linux) and the GCC compiler are essential to avoid errors during the final dependency installation phase (the famous npm install).

# Install the base Ubuntu compilation package
sudo apt-get install build-essential

# Install Homebrew
/bin/bash -c "$(curl -fsSL [https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh](https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh))"

# The end-of-installation instructions for Homebrew will ask you
# to add Homebrew to your PATH. Generally, it looks like this:
# (Make sure to adapt the path if necessary)
echo 'eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"' >> /home/$USER/.bashrc
eval "$(/home/linuxbrew/.linuxbrew/bin/brew shellenv)"

# Install GCC via Homebrew
brew install gcc

Conclusion

Your virtual machine on Nutanix AHV is now a rich and complete environment, equipped with high-performance GPU drivers, an execution engine (Node.js), an LLM ready to answer (Ollama), web access (SearXNG), and the right compilation tools.

The groundwork is perfectly laid. All that’s left is for you to finalize the installation of the application itself by following the rest of our guide on OpenClaw.