ConQuR: Corner Aligned Activation Quantization via Optimized Rotations for LLMs

Excerpt

arXiv:2605.10793v2 Announce Type: replace Abstract: Large language models (LLMs) are costly to deploy due to their large memory footprint and high inference cost. Weight-activation quantization can reduce these costs, but low-bit activation quantization remains difficult because activation outliers induce large quantization error. Recent rotation-based methods address this by applying orthogonal transformations that redistribute activation magnitude across dimensions, but existing approaches eit