Your browser does not support the audio element. Listen to article This content is generated by Google AI. Generative AI is experimental [[duration]] minutes Since releasing Gemma 4 two months ago, we've been continuously working to expand its capabilities. First, we introduced Multi-Token Prediction (MTP) to accelerate inference, and just a couple of days ago, we released a 12B model to bridge the gap between our E4B and 26B MOE models. Today, we are releasing new checkpoints optimized with Quantization-Aware Training (QAT) to make Gemma 4 even more efficient, so you can run models locally on everyday edge devices and consumer GPUs. By simulating quantization during training, QAT minimizes quality loss when the model is compressed. This release includes QAT checkpoints for the popular Q4_0 quantization format as well as a novel quantization format specialized for mobile use cases. Using this mobile format, we’ve reduced the memory footprint of Gemma 4 E2B to 1GB.…