Most AI-powered Flutter apps rely on cloud APIs, but on-device AI opens up a different set of possibilities: lower latency, offline experiences, and improved privacy. It also introduces a completely different set of engineering challenges.

While building a multimodal Flutter application powered by Gemma using flutter_gemma, I discovered that getting the model to run was only the beginning. The real work was managing memory on resource-constrained devices, handling large model downloads, integrating native dependencies, and designing an experience that remained responsive on everyday hardware.

In this session, I'll share what I learned building and testing on-device AI in Flutter. We'll explore how Gemma runs locally, how to integrate it into a Flutter application, and the architectural decisions that make on-device AI practical. Along the way, we'll discuss model management, streaming responses, multimodal interactions, performance optimization, and the trade-offs between on-device and cloud inference. You'll leave with a realistic understanding of when on-device AI is the right choice, the challenges you'll encounter, and the patterns that make it work well in real Flutter applications.

Key takeaways:
• Understand how on-device inference works and when it makes sense over cloud-based AI.
• Learn how to integrate Gemma into Flutter using flutter_gemma.
• Explore practical patterns for model management, streaming responses, and performance optimization.
• Understand the engineering challenges of running multimodal AI on resource-constrained devices, including memory management and native integration.
• Learn from real implementation challenges, engineering decisions, and lessons from building an on-device AI application.

Experience:
This session is based on a ground-up rebuild of my multimodal on-device AI Flutter application on Gemma 4 and flutter_gemma's current architecture, along with a public codelab that walks through the build step by step.

I've spoken at Flutter conferences and community events on Flutter, Firebase, Gemini, and AI-powered application development. This is a new session built from hands-on implementation, experimentation and testing across Flutter's supported platforms. Examples of my previous talks, slides, codelabs, and technical content are available at akanshajain.dev.