#1Gemma 4 QAT Models: Optimizing for Mobile and Laptop Efficiency
Google DeepMind released Quantization-Aware Training checkpoints for the Gemma 4 model family, cutting memory requirements by roughly 40% while retaining about 95% of model quality — the 26B model now fits in the footprint of a 14B model. Unlike standard post-training quantization, QAT bakes compression into training itself so the model learns to compensate for precision loss. Weights are on Hugging Face in GGUF, mobile-optimized, and Compressed Tensors formats, compatible with Ollama, LM Studio, vLLM, MLX, and LiteRT-LM.





