Quantization-Aware Healing: When 4-Bit Compression Beats Full-Precision Models
The Promise of Compression Without Compromise
Model quantization—reducing the numerical precision of neural network weights from 32-bit or 16-bit floats to 4-bit or even lower—has long been a key strategy for deploying large models on resource-constrained hardware. The conventional wisdom holds that this compression comes at a cost: