OSC: Hardware Efficient W4A4 Quantization via Outlier Separation in Channel Dimension

📰 ArXiv cs.AI

arXiv:2604.12782v1 Announce Type: cross Abstract: While 4-bit quantization is essential for high-throughput deployment of Large Language Models, activation outliers often lead to significant accuracy degradation due to the restricted dynamic range of low-bit formats. In this paper, we systematically investigate the spatial distribution of outliers and demonstrate a token-persistent structural clustering effect, where high-magnitude outliers consistently occupy fixed channels across tokens. Build

Published 15 Apr 2026
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