3 papers
cs.LG2026
Calibration and Transformation-Free Weight-Only LLMs Quantization via Dynamic Grouping
Xinzhe Zheng, Zhen-Qun Yang, Zishan Liu +4
Large Language Models (LLMs) deliver strong performance but are difficult to deploy under tight memory and compute constraints. Low-bit post-training quantization (PTQ) is a promis…
cs.LG2025
SplitQuantV2: Enhancing Low-Bit Quantization of LLMs Without GPUs
Jaewoo Song, Fangzhen Lin
The quantization of large language models (LLMs) is crucial for deploying them on devices with limited computational resources. While advanced quantization algorithms offer improve…
cs.LG2025
SplitQuant: Layer Splitting for Low-Bit Neural Network Quantization
Jaewoo Song, Fangzhen Lin
Quantization for deep neural networks (DNNs) is the process of mapping the parameter values of DNNs from original data types to other data types of lower precision to reduce model…