🤖 RAI Studio · Xiaohongshu AI Infrastructure

Building Leading & Developer-Friendly AI Large Model Full-Stack Infrastructure

🏠 Who We Are

We are the Large Model Infrastructure Team at Xiaohongshu (Rednote), responsible for the company's end-to-end infrastructure for large models.

We build across three layers — compute, frameworks, and platforms — to support model training, compression, deployment, online and offline serving, and agent development and publishing. Our work helps teams turn model capabilities into production applications with greater efficiency, lower cost, reliable operation, and repeatable delivery at scale.

🚀 Our Mission

Build Xiaohongshu's foundation for productivity in the AI era — making AI as reliable, efficient, and accessible as water and electricity for every business scenario.

We believe better models need infrastructure that consistently turns their capabilities into real-world value. We focus on:

• 💡 Faster experimentation and delivery — Help teams validate ideas, produce models, and deploy AI applications through a unified toolchain.
• 🪶 Efficient compute and execution — Improve resource utilization through unified GPU scheduling, heterogeneous hardware support, and training and inference optimization.
• 🧮 Reliable services at scale — Build dependable model services and observable agent applications that can grow with demand.
• 🌍 Open collaboration and research — Share reusable systems and research with the community, and advance AI infrastructure together.

🤝 Get Involved

We welcome developers and researchers working on efficient, reliable AI infrastructure.

• 🚀 Try a project: Start with its README for setup, examples, and supported configurations.

• 🐛 Report a problem: Open an issue in the relevant repository with reproduction steps.

• 💬 Share an idea: Open an issue with a concrete proposal or feedback.

• 🔧 Contribute: Submit code, documentation, examples, or reproducible benchmarks, following the repository's contribution guidance where available.

• 📚 Build on our research: Read the papers linked from our organization profile and use each project's citation instructions when referencing the work.

• 🌟 Spread the word: Star and share projects you find useful.

Browse all RAI Studio repositories to find a project that matches your interests.

---

⭐ Star our projects if you find them helpful!

Made with ❤️ by the Xiaohongshu AI Infrastructure Team