Tutorials

NVIDIA Jetson™ is a powerful platform for developing innovative edge AI and robotics solutions across industries.

An onboarding guide for new NVIDIA Jetson developers, covering the official developer kit user guides and remote development with VS Code or Cursor over SSH.

Flash a prebuilt OE4T demo-image-full Yocto image (JetPack 7.2) to Jetson AGX Thor, AGX Orin, or Orin Nano. No BitBake build required.

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 Google Gemma 4 models on Jetson with vLLM or llama.cpp. Covers E2B, E4B, 26B-A4B, and 31B on Orin and Thor, including reasoning, tool calling, and runtime selection.

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.

Deploy NVIDIA Isaac GR00T N1.7 Vision-Language-Action (VLA) model on NVIDIA Jetson AGX Thor with TensorRT mixed NVFP4 quantization.

Run a conversational AI pipeline on Jetson Thor with on-device ASR, LLM/VLM, and TTS.

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

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

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

An easy introduction to NVIDIA NemoClaw on Jetson using a free local Ollama model, with Telegram as a simple way to chat with your agent from your phone.

Use Jetson agent skills to build a memory-optimized multimodal application on Jetson Orin Nano 8GB.

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. The SDK supports Llama, Qwen3/3.5/3.6, InternVL3/3.5, Phi-4-Multimodal, Nemotron-Nano, Alpamayo R1, and more.

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.

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.