This talk digs into what it really takes to run LLMs efficiently on mobile hardware in a React Native environment. We’ll examine the constraints teams face - memory limits, model loading strategies, inference performance, platform-specific APIs - and how they shape real-world product decisions. From there, we’ll introduce a React Native library that provides two complementary ways to integrate on-device AI: cross-platform, state-of-the-art models that run locally on both Android and iOS, and a dedicated path for leveraging Apple Intelligence capabilities on supported iOS devices.
We’ll walk through the architecture, usage patterns, and trade-offs of each approach, and discuss best practices for delivering smooth, low-latency AI experiences without relying on the cloud. We will cover both native libraries for mobile AI & LLM model inference, and the available wrappers for React Native, along with the trade-offs, capabilities, hardware compatibility, model format compatibility and compile-time model optimizations (operator fusing, vectorization, memory planning, operations accelerated for specific hardware) considerations so that the audience is aware to pick the best solution for their specific use cases.
