Beyond FP32: The Android Developer's Guide to High-Performance Custom Quantized Model Integration

Short summary

A technical guide for Android developers on integrating custom quantized AI models to replace FP32 floating-point models on mobile hardware. Covers linear quantization math (scale and zero-point), symmetric vs asymmetric quantization tradeoffs, per-tensor vs per-channel strategies, and hardware acceleration paths including NPU, GPU, and CPU. Provides production-ready Kotlin architecture patterns for balancing precision and efficiency on edge devices.

• •FP32 models fail on mobile due to RAM, heat, and latency; quantization maps floats to integers for edge deployment
• •Per-channel quantization is the gold standard, isolating outliers to preserve model accuracy
• •Android offers NPU, GPU, and CPU acceleration paths, each with different quantization requirements

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