QUASAR: How Saliency-Weighted Reconstruction Closes the Loss Floor Gap in LLM Quantization-Aware Training

QUASAR: How Saliency-Weighted Reconstruction Closes the Loss Floor Gap in LLM Quantization-Aware Training

QUASAR: How Saliency-Weighted Reconstruction Closes the Loss Floor Gap in LLM Quantization-Aware Training Quantization is one of the most practical tools in the LLM deployment toolkit. Shrinking a model from 16-bit to 4-bit or even 2-bit precision can cut memory requirements by 4–8×, making it possible to run large models on consumer hardware, edge devices, or cost-constrained cloud instances. But quantization is not free — and the further you p…