Basic idea
DGX Dashboard is a web application that runs locally on your hardware platform. It provides a graphical interface for system updates, resource monitoring, and an integrated JupyterLab environment. You can open it from the desktop app launcher or reach it remotely through NVIDIA Sync or an SSH tunnel. When you work remotely, the dashboard is the easiest way to apply system package and firmware updates.
What you'll accomplish
You'll access DGX Dashboard on your hardware platform, launch a JupyterLab instance with a pre-configured Python environment, monitor GPU utilization, manage system updates, and run a sample AI workload.
What to know before starting
Required:
- Basic terminal usage for SSH connections and port forwarding
- Familiarity with Python environments and Jupyter notebooks
Optional:
- NVIDIA Sync installed on your local machine for one-click remote dashboard access
Supported hardware platforms
Use the matrix below to confirm your hardware platform, recommended default local settings, and whether multi-node applies.
| Hardware platform | OS | Memory | Recommended default local settings | Multi-node capable hardware |
|---|
| DGX Spark | DGX OS (Linux) | 128 GB Unified Memory | DGX Dashboard at http://localhost:11000; JupyterLab via dashboard | — |
Prerequisites
Hardware requirements
- Supported hardware platform — see Supported hardware platforms matrix above
- Hardware platform powered on, networked, and reachable for local desktop or SSH access
Software requirements
- NVIDIA DGX OS on the hardware platform
- Web browser access to port
11000 (local or tunneled)
- NVIDIA Sync installed (recommended remote access) or an SSH client configured for manual tunneling
Ancillary files
All required assets can be found in the playbook repository.
assets/jupyter-cell.py — Sample Stable Diffusion XL notebook cell for verifying GPU-backed generation in JupyterLab
Time & risk
- Estimated time: 30 MIN (longer on first JupyterLab launch and first model download)
- Risk level: Low
- Normal dashboard and JupyterLab usage makes no permanent system changes
- System updates upgrade packages and firmware and trigger a reboot — save work before updating
- Rollback: Stop JupyterLab instances in the dashboard; delete the JupyterLab working directory if needed. After system updates, restore from a system backup or recovery media if you must roll back.
- Last Updated: 07/31/2026
- Access DGX Dashboard locally or remotely, launch JupyterLab, monitor GPU use, apply updates, and run a sample workload
