InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization 事件

InfoQuant: Shaping Activation Distributions for Low-Bit LLM Quantization arXiv:2605.26175v1 Announce Type: cross Abstract: Low-bit activation quantization remains a major bottleneck in efficient large language model (LLM) deployment. The difficulty is not only that activations contain outliers, but that their distributions are often poorly matched to a low-bit uniform quantizer. Existing post-training quantization (PTQ) methods suppress peaks, balance channels, or minimize reconstruction error,