AI RESEARCH
DuQuant++: Fine-grained Rotation Enhances Microscaling FP4 Quantization
arXiv CS.CL
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ArXi:2604.17789v1 Announce Type: cross The MXFP4 microscaling format, which partitions tensors into blocks of 32 elements sharing an E8M0 scaling factor, has emerged as a promising substrate for efficient LLM inference, backed by native hardware on NVIDIA Blackwell Tensor Cores. However, activation outliers pose a unique challenge under this format: a single outlier inflates the shared block scale, compressing the effective dynamic range of the remaining elements and causing significant quantization error.