Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
DECO: Sparse Mixture-of-Experts with Dense-Comparable Performance on End-Side Devices
Chenyang Song, Weilin Zhao, Xu Han +3
While Mixture-of-Experts (MoE) scales model capacity without proportionally increasing computation, its massive total parameter footprint creates significant storage and memory-acc…
cs.LG2026
GAMMA: Global Bit Allocation for Mixed-Precision Models under Arbitrary Budgets
Zhangyang Yao, Haiyan Zhao, Haoyu Wang +1
Mixed-precision quantization improves the budget--accuracy trade-off for large language models (LLMs) by allocating more bits to sensitive modules. However, automating this allocat…