INT8 vs FP16: How Android Quantization Cuts AI Model Size and Battery Drain

Running large AI models directly on Android devices causes overheating, frame rate drops, and rapid battery drain due to the high memory and energy cost of standard FP32 floating-point operations. Quantization addresses this by compressing neural networks into lower-precision formats — FP16 halves memory usage with minimal accuracy loss, while INT8 reduces model size by 4x and is highly efficient on dedicated Neural Processing Units. Post-Training Quantization (PTQ) is the most practical approach for Android developers, as it converts a pre-trained model without requiring the original training data or significant compute resources. The mathematical core of PTQ is affine quantization, which maps floating-point values to integers using a scale factor and zero-point, with symmetric and asymmetric variants offering different trade-offs. Implementing a production-ready quantization pipeline in Kotlin enables developers to deploy capable AI models on-device while preserving battery life and thermal performance.