Jetson AI Lab

www.jetson-ai-lab.com

The NVIDIA Jetson AI Lab is your guide to running generative AI models entirely on-device with NVIDIA Jetson. Explore optimized tutorials, benchmarks, and hands-on examples for LLMs, VLMs, image generation, speech recognition, and more.

About

Jetson AI Lab pairs a modern Astro frontend with a content-driven workflow, enabling new tutorials, models, and posts to be published without touching layout code.

Key Features

• Optimized showcase for flagship models: Llama 4, Gemma 3, Qwen, SDXL, Whisper, and more
• Curated tutorials, benchmarks, and community resources with consistent styling
• Local-first architecture: everything runs on Jetson hardware with no cloud dependency
• Content authored in Markdown/JSON for fast iteration and git-friendly reviews
• Archive of legacy documentation at /archive/

Technology Stack

• Astro 5 - Static-first frontend framework
• Tailwind CSS - Design system and styling
• MDX & Markdown - Rich content with interactive elements
• TypeScript - Type safety across data collections
• Chart.js - Interactive benchmark visualizations

Project Structure

/
├── src/
│   ├── components/           Shared UI elements
│   ├── content/              Markdown and JSON content repositories
│   │   ├── home.json         Homepage metrics and featured models
│   │   ├── models/           Model deep dives authored in Markdown
│   │   ├── posts/            Blog articles
│   │   └── tutorials/        Long-form tutorials with frontmatter
│   ├── layouts/              Base layouts for pages and tutorials
│   └── pages/                Astro routes for the site
├── public/
│   ├── archive/              Legacy MkDocs documentation (static)
│   └── images/               Static assets
├── astro.config.mjs          Astro configuration with redirects
├── tailwind.config.mjs       Tailwind theme definitions
└── TUTORIAL_TEMPLATE.md      Authoring guide for new tutorials

Getting Started

Prerequisites

• Node.js 18+ and npm
• Git

Local Development

1. Clone the repository

git clone https://github.com/NVIDIA-AI-IOT/jetson-ai-lab.git
cd jetson-ai-lab

2. Install dependencies

npm install

3. Start the development server

npm run dev

The development server will be available at http://localhost:4321

4. Build for production

npm run build
npm run preview

Available Scripts

Command	Description
npm run dev	Start development server with hot reload
npm run build	Build static site for production
npm run preview	Preview production build locally
npm run astro	Run Astro CLI commands

Content Authoring

Adding Tutorials

1. Create a Markdown file in src/content/tutorials/ with appropriate frontmatter
2. Create a matching .astro wrapper in src/pages/tutorials/
3. Follow the TUTORIAL_TEMPLATE.md guide for formatting

Adding Models

Add a Markdown file under src/content/models/ with frontmatter for overview, benchmarks, and usage examples.

Updating Homepage

Edit src/content/home.json to update hero metrics, featured models, and stats.

Content Tips

• Use Markdown headings and tables for consistent styling
• Set difficulty in tutorial frontmatter: Beginner, Intermediate, or Advanced
• Store media assets under public/ and reference with absolute paths

URL Redirects

Old MkDocs URLs are automatically redirected:

Old URL	New URL
/tutorial_ollama.html	/tutorials/ollama/
/tutorial_live-vlm-webui.html	/tutorials/live-vlm-webui/
/models.html	/models/
Other .html URLs	/archive/[original-path]

Redirects are configured in astro.config.mjs and the custom 404.astro page.

Deployment

The site is automatically deployed to GitHub Pages via GitHub Actions on every push to main.

• Production URL: https://www.jetson-ai-lab.com
• Build Output: Static HTML/CSS/JS in dist/

CI/CD Pipeline

The .github/workflows/ci.yml workflow handles:

• ✅ Automated builds on push to main
• ✅ Deployment to GitHub Pages
• ✅ Node.js 20 with npm caching

Archive

Legacy MkDocs documentation is preserved at /archive/ with a deprecation banner linking to the new site. This ensures old bookmarks and external links continue to work.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/new-tutorial)
3. Commit your changes (git commit -m 'Add new tutorial')
4. Push to the branch (git push origin feature/new-tutorial)
5. Open a Pull Request

License

See LICENSE for details.

Contact

For questions or contributions, please open an issue or contact the maintainers.

• Khalil Ben Khaled
• Aditya Sahu
• Chitoku Yato