Ruikang Liu

The work introduces G²PTQ, a post-training quantization framework that combines first- and second-order information under a globally supervised, block-wise objective: it recomputes gradient and Hessian estimates before quantizing each Transformer block to avoid staleness and uses trust-region scaling to bound the exact first-order compensation step, achieving lower KL divergence and higher downstream QA accuracy than baselines such as GPTQ, GuidedQuant, and GPTAQ across 13 dense and 2 MoE models from 0.6B to 125B parameters at 2/3/4-bit weight and 4-bit weight-activation settings.

The work introduces G²PTQ, a post-training quantization framework that combines first- and second-order information under a globally supervised, block-wise objective: it recomputes gradient and Hessian estimates before quantizing each Transformer block to avoid staleness and uses trust-region scaling to bound the exact first-order compensation step, achieving lower KL divergence and higher downstream QA accuracy than baselines such as GPTQ, GuidedQuant, and GPTAQ across 13 dense and 2 MoE models from 0.6B to 125B parameters at 2/3/4-bit weight and 4-bit weight-activation settings.