# Maximum-entropy IPF routing-overlap framework

Prior cost models estimated speculative decoding from the expert-weight bytes activated during verification, predicting a concave speedup curve with limited gains at low batch size for sparse MoE targets. Across 10 sparse MoE models from 30B to 1T parameters and 8 to 512 experts, fine-grained models instead often peak near batch size 1. Two effects explain it: routing overlap across adjacent draft positions cuts the unique experts loaded during verification by 21% to 34% relative to an independence baseline, and GPU execution dynamics, namely bandwidth-utilization gaps between MoE and attention kernels plus L2 reuse, decouple runtime from expert bytes.

Prior cost models estimated speculative decoding from the expert-weight bytes activated during verification, predicting a concave speedup curve with limited gains at low batch size for sparse MoE targets. Across 10 sparse MoE models from 30B to 1T parameters and 8 to 512 experts, fine-grained models instead often peak near batch size 1. Two effects explain it: routing overlap across adjacent draft positions cuts the unique experts loaded during verification by 21% to 34% relative to an independence baseline, and GPU execution dynamics, namely bandwidth-utilization gaps between MoE and attention kernels plus L2 reuse, decouple runtime from expert bytes.