For years, “bigger is better” has dominated the AI conversation. Massive models, enormous datasets, and cloud-scale compute have defined what’s possible. But that story is changing—fast. In this session, we’ll explore how small AI models are getting surprisingly smart, and why they’re becoming the most practical way to bring intelligence directly to Android devices. Advances in training techniques, distillation, and efficient architectures mean that models with a fraction of the parameters can now deliver high-quality results—and can be fine-tuned quickly, cheaply, and even on modest hardware. We’ll dive into how developers can take advantage of this shift. You’ll learn how modern small language models can be tailored to your app’s domain with minimal cost, and how techniques like quantization and on-device optimization make it possible to run these models efficiently on Android. The result? Ultra-low latency, offline capability, and dramatically improved user privacy. We’ll also look at the hardware side: how today’s Android devices are evolving to support AI workloads better than ever. From increasingly capable CPUs to dedicated acceleration paths, the handset is no longer just a client—it’s becoming an intelligent edge compute platform. By the end of this session, you’ll understand: •Why small models are closing the gap with large models •How fine-tuning is becoming accessible to every developer •How to deploy and run models directly on Android devices •Why on-device AI unlocks new UX through speed, reliability, and privacy If you’ve been waiting for AI to truly belong in your app—not just behind an API—this is the session for you!