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cs.CL2025
SignRoundV2: Toward Closing the Performance Gap in Extremely Low-Bit Post-Training Quantization for LLMs
Wenhua Cheng, Weiwei Zhang, Heng Guo +2
Extremely low-bit quantization is critical for efficiently deploying Large Language Models (LLMs), yet it often leads to severe performance degradation at 2 bits and even at 4 bits…
cs.AR2025
Faster Inference of LLMs using FP8 on the Intel Gaudi
Joonhyung Lee, Shmulik Markovich-Golan, Daniel Ohayon +9
Low-precision data types are essential in modern neural networks during both training and inference as they enhance throughput and computational capacity by better exploiting avail…