2026
SINQ: Sinkhorn-Normalized Quantization for Calibration-Free Low-Precision LLM Weights
ICML 2026poster
Post-training quantization has emerged as the most widely used strategy for deploying large language models at low precision. Still, current methods show perplexity degradation at bit-widths $\leq 4$, partly because representing outliers causes precision issues in parameters that share the same scal…