MoE

Extensive Reading Author Info ‪Rongjie Yi‬ - ‪Google Scholar‬ Homepage - Liwei Guo / Assistant Professor: a tenure-track Assistant Professor at University of UESTC. Mengwei Xu Background Challenges End-to-end latency is I/O-dominated because expert weights are loaded on demand from slow storage (tail delay inflation). Quantization trilemma: compress aggressively, preserve accuracy, and keep dequantization nearly free on low-power CPUs/NPUs. Dynamic routing obscures which experts will be needed, making prefetch hard and naive caching ineffective when activations are balanced. Tiny RAM budgets (~1.5–3 GB) constrain the expert buffer, demanding careful eviction to avoid thrashing. Hardware heterogeneity and variable storage speeds complicate a one-size-fits-all pipeline and bitwidth plan. Insights Non-expert weights are held in device memory; while expert weights are held on external storage and fetched to memory only when activated. ...