On-device AI changes how React Native apps behave when the network disappears.
In this video, we introduce react-native-ai and explain how local LLMs run directly on the device. No HTTP calls, no server queue, no dependency on connectivity. The model runs on the phone itself.
Using a real mobile scenario, we walk through why on-device inference matters for privacy, offline usage, reliability during outages, and cost. With local models, user input never leaves the device and inference is free once the app is installed.
We also cover the architecture behind react-native-ai, including supported engines and the JavaScript layer built on top of the Vercel AI SDK. This episode sets the foundation for the rest of the React Native AI series.
Links:
- react-native-ai repository: https://github.com/callstackincubator/ai
- Vercel AI SDK: https://ai-sdk.dev/
Star the repo, leave a comment if you have questions, and subscribe for the next episodes.
Chapters:
00:00 Health app scenario without network
00:22 Cloud API failure underground
00:38 On-device AI response and local execution
01:09 Privacy and sensitive data handling
01:49 Offline usage and outage resilience
02:30 Running assistants anywhere
02:48 Free inference and cost model
03:43 Why React Native AI exists
03:56 Built-in and third-party models
04:06 Supported engines overview
04:11 Apple Foundation Models on iOS
04:22 MLC LLM on Android and iOS
04:28 Vercel AI SDK on the JS side
04:42 Minimal setup with providers
04:48 Wrapping up and next episodes
