Quantization-Aware Training Explained: Deploying Accurate AI Models on Android

Deep learning models built with 32-bit floating-point precision are too large and slow for most Android devices, where a 100-million parameter model alone can consume around 400MB of RAM. Standard Post-Training Quantization (PTQ) reduces model size by converting weights to 8-bit integers, but often causes a sharp drop in accuracy known as the quantization cliff. Quantization-Aware Training (QAT) addresses this by simulating quantization noise during the training process itself, allowing the model to learn weights that remain accurate even after compression. QAT delivers key benefits including a roughly 4x reduction in model size, lower inference latency, and better use of hardware accelerators like NPUs on mobile devices. A developer guide on DEV Community walks through QAT implementation for Android, covering integration with AICore, Gemini Nano, and a production-ready Kotlin 2.x example.