Google DeepMind Releases Gemma 4 QAT Checkpoints: Q4_0 and a New Mobile Format Cut On-Device Memory

Google DeepMind released Quantization-Aware Training (QAT) checkpoints for the Gemma 4 family. The release targets local deployment on edge devices and consumer GPUs. It follows the Gemma 4 launch in April and a 12B model two days earlier. We compared the available Gemma 4 edge-model formats using only published numbers. The goal was simple. Show what each precision level costs in memory. Then show what QAT actually changes. Quantization shrinks a model by lowering weight precision. Standard Post-Training Quantization (PTQ) compresses a finished model. That often degrades quality. QAT instead simulates quantization…