English(EN) MACaQ: Fast, Model Agnostic Calibrated Quantization for Low-Precision Weights and Activations

MACaQ 量化方法加速 AI 模型推理

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-07 00:00

研究人员推出了一种新颖的训练后量化方法 MACaQ，旨在实现高效的低精度神经网络推理。该方法独立量化每个线性层，并结合在线激活变换和 Hessian 感知精炼，以在不牺牲准确性的情况下降低计算成本。与 Qwen3-32B 和 Llama-3-8B 等模型上的现有方法相比，MACaQ 展现出更快的量化时间和相当或更优的性能，并已由华为-CSL 开源。 AI

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排序理由该条目描述了一篇关于新型神经网络量化方法的新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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报道来源 [1]

Hugging Face Daily Papers TIER_1 English(EN) · 2026-11-07 00:00

MACaQ：用于低精度权重和激活的快速、模型无关的校准量化

MACaQ is an MXFP4 W4A4 post-training quantization method that quantizes each linear layer independently using online activation transforms and Hessian-aware refinement, reducing cost while preserving accuracy across language and diffusion models.

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报道来源 [1]

MACaQ：用于低精度权重和激活的快速、模型无关的校准量化

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