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> For a complete documentation index, see https://docs.nvidia.com/aerial/aodt/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/aerial/aodt/_mcp/server.

# Prerequisites

Complete these steps before installing the [client](/client-installation) or [worker](/worker-installation). The requirements depend on your deployment type.

## Deployment types

| Deployment        | What it means                                                | Prerequisites on this page                                                                                                               |
| ----------------- | ------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------- |
| **Colocated**     | Client and worker run on the same GPU-enabled Linux machine. | The prerequisites on this page apply to that machine.                                                                                    |
| **Non-colocated** | Client and worker run on different machines.                 | The prerequisites on this page apply to the **worker host only**. The client machine does not require a GPU or the software listed here. |

For non-colocated setups, install the items below on the worker system, then install the client on a separate machine using the OS-specific steps in [Client Installation](/client-installation).

## Summary

The worker host must provide:

* Ubuntu Linux 22.04
* An NVIDIA GPU (RTX 6000 Ada, L40, A100, or H100)
* NVIDIA driver 575.57.0 (576.80 on AWS)
* Docker with Docker Compose v2 (CLI plugin) 2.3 or newer
* NVIDIA Container Toolkit

The legacy standalone `docker-compose` v1 is not supported. Use the Docker Compose v2 CLI plugin (`docker compose`).

## Install Docker

Install Docker from the official repository. This includes the `docker-compose-plugin` package.

```bash
sudo apt-get install -y ca-certificates curl
sudo install -m 0755 -d /etc/apt/keyrings
sudo mkdir -p /etc/apt/sources.list.d
sudo curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
sudo chmod a+r /etc/apt/keyrings/docker.asc
echo "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu $(. /etc/os-release && echo "$VERSION_CODENAME") stable" | sudo tee /etc/apt/sources.list.d/docker.list
sudo apt-get update
sudo apt-get install -y docker-ce docker-ce-cli containerd.io docker-buildx-plugin docker-compose-plugin
sudo usermod -aG docker $USER
```

Log out and back in after `usermod` so Docker commands work without `sudo`.

Verify the installation:

```bash
docker --version
docker compose version
```

## Install NVIDIA Container Toolkit

The worker requires GPU access inside Docker containers. Install and configure the NVIDIA Container Toolkit:

```bash
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
```

Verify that the host can see the GPU:

```bash
nvidia-smi
```

## Install NVIDIA driver

Install the NVIDIA driver if it is not already present. Skip this step if `nvidia-smi` reports a supported driver version.

```bash
# List available driver packages
apt-cache search nvidia-driver

# Install using the driver series number (e.g. nvidia-driver-535).
# Do NOT supply a patch-level version number like 590.48.01.
sudo apt-get install -y nvidia-driver-<driver-series>
sudo reboot
```

After reboot, confirm the driver is active:

```bash
nvidia-smi
```

See the [CUDA compatibility guide](https://docs.nvidia.com/deploy/cuda-compatibility/) for driver and toolkit version requirements.

## Optional: CUDA toolkit

CUDA Toolkit is not required for the worker and it is optional for running the client. It is required on the client only when you want colocated `LOCAL_IPC` transport for direct GPU memory access. See [Client Installation](/client-installation) for CUDA setup on the client.

## Next steps

When all prerequisites are installed and verified:

1. [Download the AODT package](/client-installation#download-the-aodt-package-from-ngc) from NGC.
2. Install the [client](/client-installation) and [worker](/worker-installation).
3. Follow the [Quickstart](/quickstart) guide to validate your deployment.