Google shrinks Gemma 4 to under 1GB for phones
Google just released Quantization-Aware Training (QAT) checkpoints for its Gemma 4 models, optimizing them for laptops, phones, and consumer GPUs. By integrating compression directly into the training process, a custom mobile schema squeezes the Gemma 4 E2B model down to under 1GB of memory without sacrificing performance. Why it matters: running high-quality local AI on everyday edge hardware just became drastically lighter on VRAM.