D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs

• URL: http://arxiv.org/abs/2602.02546v2
• Date: Fri, 06 Feb 2026 13:11:49 GMT
• Title: D$^2$Quant: Accurate Low-bit Post-Training Weight Quantization for LLMs
• Authors: Xianglong Yan, ChengZhu Bao, Zhiteng Li, Tianao Zhang, Shaoqiu Zhang, Ruobing Xie, Samm Sun, Yulun Zhang,
• Abstract summary: Weight-only post-training quantization (PTQ) is appealing as it reduces memory usage and enables practical speedup without low-bit operators or specialized hardware.<n> accuracy often degrades significantly in weight-only PTQ at sub-4-bit precision.<n>We propose D$2$Quant, a novel weight-only PTQ framework that improves quantization from both the weight and activation perspectives.
• Score: 33.883527341335856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) deliver strong performance, but their high compute and memory costs make deployment difficult in resource-constrained scenarios. Weight-only post-training quantization (PTQ) is appealing, as it reduces memory usage and enables practical speedup without low-bit operators or specialized hardware. However, accuracy often degrades significantly in weight-only PTQ at sub-4-bit precision, and our analysis identifies two main causes: (1) down-projection matrices are a well-known quantization bottleneck, but maintaining their fidelity often requires extra bit-width; (2) weight quantization induces activation deviations, but effective correction strategies remain underexplored. To address these issues, we propose D$^2$Quant, a novel weight-only PTQ framework that improves quantization from both the weight and activation perspectives. On the weight side, we design a Dual-Scale Quantizer (DSQ) tailored to down-projection matrices, with an absorbable scaling factor that significantly improves accuracy without increasing the bit budget. On the activation side, we propose Deviation-Aware Correction (DAC), which incorporates a mean-shift correction within LayerNorm to mitigate quantization-induced activation distribution shifts. Extensive experiments across multiple LLM families and evaluation metrics show that D$^2$Quant delivers superior performance for weight-only PTQ at sub-4-bit precision. The code and models will be available at https://github.com/XIANGLONGYAN/D2Quant.

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