NVIDIA Jetson AGX Thor - Development in the Board

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NVIDIA®Jetson AGX Thor™
Introduction
SoM Overview Carrier Boards NVIDIA Developer Kit Auvidea X241 Connect Tech Gauntlet
JetPack 7.0
Getting Started Installing JetPack Flashing the Board with GUI Installer JetPack Components Compiling Kernel and DTB Flashing the Board from Command Line Flashing the Board from USB Performance Tuning Evaluating Performance Set Values Manually Tuning Power Maximizing Performance
Development in the Board
Board Access Methods Using Serial Console Using SSH Using Ubuntu Login Installing Packages
Camera Support
Camera Over Ethernet (CoE) MIPI CSI IMX477 + J20 Driver
Blackwell GPU
GStreamer Pipelines
Capture and Display H.264 H.265
Holoscan
PVA
DeepStream
Installation Running Examples Performance Benchmarking
RidgeRun Products
GstCUDA RTSP Server Video Stitching Video Stabilization Bird's‑Eye View Qt Overlay GStreamer Browser Sink Development Suite Spherical Video PTZ
RidgeRun Open Source Projects
GstPerf GStreamer Daemon GstInterpipe GstShark
RidgeRun Reference Designs
Panoramic Stitching and WebRTC Streaming
Reference Documentation
Contact Us

Once JetPack 7.0 is installed and Jetson AGX Thor has been successfully flashed, you are ready to begin development directly on the board. Jetson Thor is a full-featured embedded Linux system running Ubuntu 24.04, with access to standard development tools, AI libraries, multimedia stacks, and networking interfaces.

This section introduces how to work with the board in runtime - from connecting via terminal or desktop, to installing packages and setting up the environment for development.

It is divided into two key areas:

• Board Access Methods: How to connect to Jetson Thor via UART serial console, SSH, or directly through the Ubuntu desktop GUI.

• Installing Packages: Learn how to use apt and other tools to install AI frameworks, compilers, multimedia libraries, and more.

Whether you're debugging kernel logs over serial or installing deep learning frameworks via the terminal, Jetson AGX Thor supports flexible workflows for testing, research, and deployment.