# Hardware Setup

> Provision the Xisom Edge AI Box, pick an execution-provider mode, and connect it to your network.

Provision the box, choose how inference runs (GPU vs CPU), and wire it into your
plant network before installing the software.

## Supported hardware

| Tier | CPU | GPU | RAM | Inference mode |
|------|-----|-----|-----|----------------|
| Lite | 8-core x86_64 | — | 16 GB | CPU-only |
| Pro  | 8-core x86_64 | NVIDIA T4 / A2 | 32 GB | TensorRT-ready |
| Max  | 16-core x86_64 | NVIDIA L4 / A10 | 64 GB | Multi-model |
| Edge | NVIDIA Jetson (Orin / Xavier) | integrated | 16–32 GB | TensorRT on L4T (arm64) |

## Edge GPU prerequisites

If the box has an NVIDIA GPU (Pro, Max, or Jetson tiers), the operating-system
image must have these in place before you install Xisom:

- A compatible **NVIDIA driver** for the GPU.
- The **NVIDIA Container Toolkit**, so containers can access the GPU.
- For Jetson modules: an **L4T (arm64)** base image with the toolkit wired in.

On a CPU-only (Lite) box you need none of the above — inference runs on the CPU.

## Execution-provider modes

The inference runtime picks an **execution provider** that matches your hardware.
You choose the matching profile when you install (see
[Offline Bundle Install](/install-deploy/offline-bundle-install/)).

  
**TensorRT (GPU)**

  **Best performance.** Uses the NVIDIA GPU with TensorRT acceleration for the
  lowest inference latency. Available on Pro, Max, and Jetson tiers.

  - Requires the NVIDIA driver + Container Toolkit on the host.
  - The first run compiles a TensorRT engine — expect a 2–3 minute warm-up before
    the inference service reports healthy.

  
  
**CUDA (GPU)**

  **GPU acceleration without TensorRT engine compilation.** Runs models on the GPU
  via CUDA. A good fallback when a model is not TensorRT-compatible, or to avoid the
  first-run compile delay.

  - Requires the NVIDIA driver + Container Toolkit on the host.

  
  
**CPU**

  **No GPU required.** Inference runs entirely on the CPU. Use the `amd64-cpu`
  install profile on Lite-tier boxes or anywhere a GPU is unavailable.

  - Highest portability, higher latency than the GPU modes.

  

Pick the GPU mode that matches your model. If a GPU box falls back to CPU
unexpectedly, the dashboard shows the active execution provider so you can spot it.
See [Monitoring](/operate/monitoring/).

## Network requirements

- One LAN port for management (dashboard + API).
- An optional second port for the OT network where your sensors and PLCs live.
- Outbound HTTPS to your license and update servers, if used (configurable
  allowlist). Air-gapped sites install from the [offline
  bundle](/install-deploy/offline-bundle-install/) and need no outbound access.

## First boot

1. Connect power and both network ports.
2. Browse to `https://your-device-ip` (replace with the box's management IP).
3. Sign in with the admin account created during install.
4. Continue to [Connect an input datasource](/configure/input-datasources/).

## If something goes wrong

- GPU box falling back to CPU, or stuck `unhealthy` on first boot — see the
  [Troubleshooting](/troubleshooting/) runbook.

## Next steps

  - [Install from the offline bundle](/install-deploy/offline-bundle-install/) — Air-gapped, USB-shippable install runbook.
  - [Connect a datasource](/configure/input-datasources/) — Stream sensor and process data in.