Tutorials

Complete setup guide for Jetson Orin Nano Developer Kit, covering firmware updates, JetPack 6.2 flashing via microSD card, and enabling MAXN SUPER performance mode.

Alternative setup method using NVIDIA SDK Manager to flash firmware and JetPack to your Jetson Orin Nano Developer Kit, including NVMe SSD installation support.

Set up NVMe SSD storage and configure Docker on your Jetson for optimal performance with AI containers and large models.

Optimize system RAM usage on Jetson devices by disabling the desktop GUI, unnecessary services, and mounting swap for large model workloads.

A practical intro to running LLMs and VLMs on Jetson. Use Ollama for fast experimentation, and vLLM for best performance (LLMs + VLMs supported).

Learn how to benchmark Large Language Models and Vision Language Models on your Jetson using vLLM. Measure throughput, latency, and understand key performance metrics.

Learn how to install and run Ollama on your Jetson device for easy local LLM deployment. Covers native installation, Docker containers, and Open WebUI setup.

Run NVIDIA Cosmos Reason2 (2B / 8B) models on Jetson with vLLM and connect to Live VLM WebUI for real-time vision inference.

Deploy Physical Intelligence's OpenPi π₀.₅ Vision-Language-Action (VLA) model on NVIDIA Jetson AGX Thor with TensorRT NVFP4 quantization for low-latency end-to-end inference.

Build and deploy AI-powered edge applications using Jetson Platform Services (JPS). Learn to create modular microservices for video analytics, VLM alerts, and zero-shot detection.

Run NanoOWL, OWL-ViT optimized to run real-time on Jetson with NVIDIA TensorRT for open-vocabulary object detection.

Run a fully local AI personal assistant on Jetson with OpenClaw and WhatsApp, no cloud APIs needed.

A convenient interface for evaluating Vision Language Models in real-time with WebRTC webcam streaming, OpenAI-compatible API support, and interactive prompt editor.

Learn how to fine-tune large language models directly on Jetson using PyTorch and Hugging Face TRL. Covers Full SFT (4B), LoRA (9B), and QLoRA (27B).

Use NVIDIA TensorRT Edge-LLM with two example models: Cosmos Reason2 8B (VLM) on Jetson Thor and Qwen3-4B-Instruct (LLM) on Jetson Orin Nano. Covers quantization, ONNX export, TensorRT engine builds, and pure C++ on-device inference.

Master inference optimization on Jetson Thor with vLLM. Learn to deploy production-grade LLM serving, quantization strategies (FP16 → FP8 → FP4), and advanced optimizations like speculative decoding.

Everything you need to get started with NVIDIA Jetson at a hackathon. Setup tips, project ideas, and resources to help your team build an impressive AI project.

100-minute hands-on workshop experiencing Jetson Thor's Physical AI capabilities. Learn to deploy AI microservices, run Vision Language Models, and build conversational AI pipelines.