2 papers
cs.AR2026
Approaching Shannon Bound with Lossless LLM Weight Compression
Hongshi Tan, Yao Chen, Gustavo Alonso +2
Large language models (LLMs) now scale to trillions of parameters, driving weight storage into the terabyte regime and creating an acute mismatch with GPU memory capacity. Although…
cs.CL2025
Pre-Attention Expert Prediction and Prefetching for Mixture-of-Experts Large Language Models
Shien Zhu, Samuel Bohl, Robin Oester +1
Mixture-of-Experts (MoE) Large Language Models (LLMs) efficiently scale-up the model while keeping relatively low inference cost. As MoE models only activate part of the experts, r…