Google Ships Gemma 4 QAT Checkpoints: Quantization-Aware Training

What: Google shipped quantization-aware-trained (QAT) checkpoints for the Gemma 4 family — open weights that were trained to survive being squeezed down to 4-bit (and 2-bit on the decode layers). Why: Low-bit weights are how a real model fits on a phone: Google reports the compact E2B size lands at about a 1 GB memory footprint, small enough to run on consumer hardware instead of a datacenter GPU. vs prior: Versus post-training quantization (PTQ) — which rounds the weights to the low-bit grid...