3 papers
cs.LG2025
Block Rotation is All You Need for MXFP4 Quantization
Yuantian Shao, Peisong Wang, Yuanteng Chen +3
Large language models (LLMs) have achieved remarkable success, but their rapidly growing scale imposes prohibitive costs in memory, computation, and energy. Post-training quantizat…
cs.LG2025
DartQuant: Efficient Rotational Distribution Calibration for LLM Quantization
Yuantian Shao, Yuanteng Chen, Peisong Wang +5
Quantization plays a crucial role in accelerating the inference of large-scale models, and rotational matrices have been shown to effectively improve quantization performance by sm…
cs.LG2025
EAC-MoE: Expert-Selection Aware Compressor for Mixture-of-Experts Large Language Models
Yuanteng Chen, Yuantian Shao, Peisong Wang +1
Mixture-of-Experts (MoE) has demonstrated promising potential in scaling LLMs. However, it is hindered by two critical challenges: (1) substantial GPU memory consumption to load al…