In our previous post, Mobile On-device AI: Smarter Faster Private Apps, we explored the fundamentals of running AI locally on mobile devices. Now, it’s time to get hands-on and see this technology in action!
This practical guide walks you through implementing mobile on-device AI using Google’s powerful Gemma model family, including the cutting-edge Gemma 3n. You’ll learn to deploy these models across iOS, Android, and web platforms using industry-standard frameworks.
While cloud computing drives many AI breakthroughs, a parallel revolution is happening right in our hands: Mobile On-device AI. As mobile devices become ever more powerful, the ability to run AI locally offers a compelling path to faster, more private, and smarter app experiences. As a developer who loves exploring AI and has built mobile apps, I am fascinated by the idea of these two worlds of mobile and AI converging.
Every day we’re seeing fantastic advancements in AI, thanks to more data and powerful computers. This may make it seem like the future of AI is all about getting even more data and bigger computers. But I believe a critical and rapidly evolving piece of the puzzle is about bringing the Intelligence of Artificial Intelligence onto the devices where the data originates (eg: our phones, cameras, and IoT devices) and doing the “smarts” using their own computing capabilities.