most citedARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

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cs.LG2026

SharQ: Bridging Activation Sparsity and FP4 Quantization for LLM Inference

Haoqian Meng, Yilun Luo, Yafei Zhao +4

Low-bit floating-point formats and semi-structured sparsity are increasingly supported by modern accelerators, yet combining them for LLM activation compression remains challenging…

cs.LG2026★ 1 cited

ARCQuant: Boosting NVFP4 Quantization with Augmented Residual Channels for LLMs

Haoqian Meng, Yilun Luo, Yafei Zhao +3

The emergence of fine-grained numerical formats like NVFP4 presents new opportunities for efficient Large Language Model (LLM) inference. However, it is difficult to adapt existing…