On Device AI
in Mobile Apps

How to build intelligent Flutter applications that run AI models directly on the device, no cloud dependency, no latency, no privacy trade-offs.

Key Takeaways

• ✓Select the right inference framework per use case, TFLite for custom Keras models, ONNX Runtime for PyTorch pipelines, and Google ML Kit for standard vision and NLP tasks with zero model management.
• ✓Reduce model size by ~75% using INT8 quantisation, pruning, and knowledge distillation, with under 2% accuracy degradation for most classification and NLP tasks.
• ✓Run inference in a dedicated Dart isolate to keep the UI thread fully unblocked enabling 3–10× latency improvement over cloud round trips via NNAPI and Core ML hardware delegates.
• ✓Deploy OTA model updates via a CDN hosted manifest ship model improvements to users without requiring an app store release.
• ✓Case study: inference latency dropped from 300–800ms to 20–120ms on-device, offline task completion improved from 61% to 95%, and zero user data transmitted to external servers.