Gemma 4 with Quantization-Aware Training Available

Gemma 4 models are now available with quantization-aware training (QAT), enabling multiple weight precision options including Q4_k_M and QAT-specific variants. This approach bakes quantization into the training process rather than applying it post-hoc, typically yielding better accuracy retention at lower bit depths.

For operators, QAT variants reduce the inference compute and memory footprint required for local deployment. Models previously requiring GPU acceleration or high-end consumer hardware become viable on standard CPUs or edge devices. This directly lowers operational costs for on-premise inference and reduces dependency on cloud inference APIs.

Builders testing these configurations face a new decision matrix: trading off between model accuracy, latency, and deployment hardware constraints. The availability of multiple weight configurations signals that no single quantization strategy works uniformly across use cases. Teams will need to benchmark QAT variants against their specific workload profiles before production deployment. This shifts optimization from a binary "quantize or don't" decision to a tuning problem requiring empirical validation.