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Jetson AI Lab

Jetson AI Lab is NVIDIA's open-source resource for deploying generative AI on NVIDIA Jetson edge devices (Orin Nano, AGX Orin, Thor). It provides tutorials, model deployment guides, and benchmarks for running LLMs, VLMs, and VLA models locally on Jetson hardware.

For agents: NVIDIA's open-source guide to running generative AI (LLMs, VLMs, VLA models) on Jetson edge devices, with tutorials, deployment guides and benchmarks.

nvidia jetsonedge aillm deploymentdockerjetson orinon-device inference

Their llms.txt

The beginning of the file, from our validated copy saved 2026-10-21T03:22:06.753Z. Cached copy · Source on jetson-ai-lab.com

# Jetson AI Lab

> Jetson AI Lab is NVIDIA's open-source resource for deploying generative AI on NVIDIA Jetson edge devices (Orin Nano, AGX Orin, Thor). It provides tutorials, model deployment guides, and benchmarks for running LLMs, VLMs, and VLA models locally on Jetson hardware.

Jetson AI Lab covers the full workflow: initial device setup, inference engine installation (vLLM, Ollama, llama.cpp, TensorRT), model deployment, fine-tuning, and benchmarking. All content targets on-device edge AI — no cloud required.

- Source code: https://github.com/NVIDIA-AI-IOT/jetson-ai-lab
- Full documentation: https://www.jetson-ai-lab.com/llms-full.txt

## Getting Started

- [Getting Started with Jetson](https://www.jetson-ai-lab.com/tutorials/getting-started-with-jetson/): Official developer kit user guides and remote SSH setup with VS Code or Cursor
- [AI-assisted development on NVIDIA Jetson](https://www.jetson-ai-lab.com/tutorials/ai-assisted-development-on-jetson/): Use AI coding agents directly on Jetson with practical prompts, guided workflows, and on-device validation
- [Jetson Agent Skills](https://www.jetson-ai-lab.com/tutorials/jetson-agent-skills/): Install Jetson Device Skills and Jetson BSP Skills for Cursor, Claude Code, Codex, Cursor projects, or OpenClaw
- [SSD + Docker Setup](https://www.jetson-ai-lab.com/tutorials/ssd-docker-setup/): Set up NVMe SSD storage and configure Docker on Jetson for optimal performance with AI containers
- [RAM Optimization](https://www.jetson-ai-lab.com/tutorials/ram-optimization/): Optimize system RAM by disabling desktop GUI, unnecessary services, and mounting swap for large model workloads

## Inference Engines & Fundamentals

- [Introduction to GenAI on Jetson](https://www.jetson-ai-lab.com/tutorials/genai-on-jetson-llms-vlms/): Practical intro to running LLMs and VLMs on Jetson using Ollama for experimentation and vLLM for production performance
- [Ollama on Jetson](https://www.jetson-ai-lab.com/tutorials/ollama/): Install and run Ollama for easy local LLM deployment, covering native installation, Docker containers, and Open WebUI
- [GenAI Benchmarking](https://www.jetson-ai-lab.com/tutorials/genai-benchmarking/): Benchmark LLMs and VLMs on Jetson using vLLM — measure throughput, latency, TTFT, and key performance metrics

## Model Optimization

- [Speculative Decoding on Jetson](https://www.jetson-ai-lab.com/tutorials/speculative-decoding/): Learn how MTP, DFlash, and DSpark accelerate local LLM generation and how to run compatible checkpoints with vLLM and llama.cpp
- [Fine-tune LLMs on Jetson](https://www.jetson-ai-lab.com/tutorials/finetune-on-jetson/): Fine-tune large language models directly on Jetson using PyTorch and Hugging Face TRL — Full SFT (4B), LoRA (9B), and QLoRA (27B)
- [TensorRT Edge-LLM on Jetson](https://www.jetson-ai-lab.com/tutorials/tensorrt-edge-llm/): Use NVIDIA TensorRT Edge-LLM for quantization, ONNX export, engine builds, and pure C++ on-device inference

## Applications

- [Reac
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