arXiv:2606. 04980v1 Announce Type: new Abstract: Mixture-of-Experts (MoE) architectures scale model capacity through sparse expert activation, but their deployment remains memory-bound because all expert weights must reside in memory.
By Wanqi Yang, Yuexiao Ma, Alexander Conzelmann, Xiawu Zheng, Michael W. Mahoney, T. Konstantin Rusch, Shiwei Liu
arXiv:2608. 08081v1 Announce Type: cross Abstract: Large mixture-of-experts (MoE) language models with 26--120 billion parameters exceed the memory capacity of consumer devices through three simultaneous pressures: resident weight matrices, key-value (KV) cache state that grows linearly with context, and dozens of expert sublayers that must be paged on demand.
By Anthony. Lui, Mohamed. Elsaied, N. P. Savani
arXiv:2609.06161v1 Announce Type: cross
Abstract: Large language models (LLMs) have achieved remarkable progress, yet their massive storage and memory-bandwidth demands still hinder efficient deploym...
By Zhixiong Zhao, Zukang Xu, Guangyu Sun, Lifeng Liu, Dawei Yang
arXiv:2605. 08692v2 Announce Type: replace Abstract: Post-training weight-only quantization to 4 bits is widely used to reduce the memory and compute costs of large language model inference.
By Beshr IslamBouli, David Jin
arXiv:2410. 13056v4 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have demonstrated remarkable success across a wide range of language tasks, but their deployment on edge devices remains challenging due to the substantial memory requirements imposed by their large parameter sizes.
By Zihan Chen, Bike Xie, Jundong Li, Cong Shen
The paper introduces Colla-Q, a Mixture-of-Experts (MoE) quantization technique that uses activation entropy to allocate bit-widths across experts. By balancing performance among experts, Colla-Q improves overall MoE accuracy and reduces reliance on calibration datasets. The method aims to maintain robustness and stability in quantized MoE models.
By Eunju Shin, Jongbin Ryu