arXiv AI
Oct 7

Test-Time Adaptation of Quantized ViTs via Single-Pass Quantizer-Aligned Recalibration

The paper introduces Quantizer‑Aligned Recalibration (QuAR), a single‑pass test‑time adaptation technique for quantized vision transformers that does not require backpropagation or parameter updates. QuAR recalibrates activations at the input of frozen quantizers by aligning per‑channel statistics with the source calibration, thereby correcting the distorted code distribution caused by distribution shift. On ImageNet‑C, QuAR outperforms state‑of‑the‑art backprop‑free methods across 3‑, 4‑, 6‑, and 8‑bit precisions, achieving higher accuracy, lower latency, and minimal memory overhead while maintaining performance across diverse shift scenarios.

By Hyeongheon Cha, Young D. Kwon, Sung-Ju Lee
arXiv AI
Oct 12

DualQuant: Dual-scale Calibration-free Weight-and-Activation Quantization for LLMs

DualQuant introduces a dual‑scale, calibration‑free post‑training quantization framework that jointly optimizes weight‑reconstruction MSE and derives per‑channel activation rescalings, enabling effective 4‑bit weight and activation quantization without calibration data. The method employs a non‑differentiable optimization solved via variable splitting and block coordinate descent, supporting a wide range of formats including MX and NVFP4. Experiments on Llama and Qwen models show DualQuant matches or surpasses calibration‑based SmoothQuant and significantly outperforms other calibration‑free baselines, achieving up to 2.61× memory reduction and 2.46× throughput speedup on an RTX 4090.

By Mahsa Salmani, Sayeh Sharify, Hesham Mostafa
arXiv Computation and Language
2d ago

MoBiQuant: Mixture-of-Bits Quantization for Token-Adaptive Any-Precision LLM

MoBiQuant introduces a Mixture-of-Bits quantization framework that allows large language models to be inferred at varying precisions during runtime. It addresses the poor generalization of existing post‑training quantization methods by mitigating precision‑dependent outlier migration through a recursive residual quantization scheme and a token‑aware router that selects the optimal precision per token. Experiments demonstrate that MoBiQuant matches or exceeds single‑precision PTQ performance while providing significant memory savings and up to 1.34× throughput gains over current any‑precision approaches.

By Dongwei Wang, Jinhee Kim, Seokho Han, Denis Gudovskiy, Yohei Nakata, Tomoyuki Okuno, KhayTze Peong, Kang Eun Jeon, Jong Hwan Ko, Yiran Chen, Huanrui Yang