Running advanced AI and computer vision workloads on small, power-efficient devices at the edge is a growing challenge. Robots, smart cameras, and autonomous machines need real-time intelligence to see, understand, and react without depending on the cloud. The NVIDIA Jetson platform meets this need with compact, GPU-accelerated modules and developer kits purpose-built for edge AI and robotics.
The tutorials below show how to bring the latest open source AI models to life on NVIDIA Jetson, running completely standalone and ready to deploy anywhere. Once you have the basics, you can move quickly from simple demos to building anything from a private coding assistant to a fully autonomous robot.
Tutorial 1: Your Personal AI Assistant – Local LLMs and Vision Models
A great way to get familiar with edge AI is to run an LLM or VLM locally. Running models on your own hardware provides two key advantages: complete privacy and zero network latency.
When you rely on external APIs, your data leaves your control. On Jetson, your prompts—whether personal notes, proprietary code, or camera feeds—never leave the device, ensuring you retain complete ownership of your information. This local execution also eliminates network bottlenecks, making interactions feel instantaneous.
