React Native AI: Bringing on-device LLMs with AI SDK to React Native

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Local, on-device LLMs unlock private-by-default, low-latency AI experiences that work offline - ideal for mobile. In this talk, I’ll show how to run LLMs directly inside React Native apps using an AI SDK that provides a nice abstraction layer to simplify building AI applications.

Join us as we explore the creation of react-native-ai, a library that enables local LLM execution. We’ll dive deep into the provider architecture and demonstrate how we integrated it with the MLC LLM Engine and Apple’s foundation models on mobile devices.

This talk has been presented at React Advanced 2025, check out the latest edition of this React Conference.

FAQ

MLC LLM acts as a universal engine that allows on-device AI models to run on multiple platforms supported by React Native.

React Native AI is a library that allows developers to run on-device AI models using React Native.

Szymon Rybczak is a senior React Native developer at Callstack, known for creating many React Native libraries, including React Native AI.

React Native AI allows on-device AI models to be run without an internet connection by downloading the model once and using it offline, leveraging device capabilities.

On-device AI models provide benefits such as privacy, offline functionality, and fast response times without relying on cloud computing.

The Apple Foundation Models Framework is a way to run on-device LLMs created by Apple on iOS, macOS, and iPadOS without downloading additional models.

Yes, custom tool calls can be created in React Native AI to access native APIs and perform various tasks like creating calendar events or accessing photos.

Real-life applications include privacy-focused apps, offline applications, health apps for analyzing documents, and scenarios where internet connectivity is limited.

While on-device AI models can be used offline, they may not be as powerful as cloud-based models, and training them offline might have limitations.

Built-in AI models, like those from Apple, are optimized for specific devices and consume less memory compared to downloaded third-party models, which can use more RAM.

Szymon Rybczak
Szymon Rybczak
26 min
28 Nov, 2025

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Video Summary and Transcription
Hello everyone, React Native AI, AI SDK from Brazil, connect AI SDK with React Native, Szymon Rybczak, senior React Native developer, React Native AI, run AI on device, powerful devices, on-device AI on mobile, iPhone 15 Pro performance. Devices are powerful. Inspired by Guillermo Rauch's tweet on Google Chrome's Gemini model. Researched topic, learned about providers in AISDK, create and plug providers for various APIs. Excitement for WWDC, experimenting with Apple Foundation Model in React Native, impressive performance. Apple investment in React Native, code sharing success with Vercel SDK and React Native AI Apple package, extensive features and community support. Integration with ASDK, expanding providers for on-device models, Android models support, real-life privacy-focused applications. Simulator compatibility on macOS 26 and Pixel 9 Pro, upcoming Android models, LLM offline training trade-offs, privacy and efficiency benefits. Image and video generation potential, Device model parameter size comparison, Apple's optimized on-device model performance, Implications for other AI apps like Gemini and Jacky P.T. On-device model implementation for offline scenarios, Support for multiple runtimes, Memory consumption comparison, Whisper.RN and LLM.RN functionality, Device compatibility verification via APIs.
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