moe-reduces-inference-cost

IN premise — entries/2026/06/21/wiki-Large_language_model-chunk-6.md

Created 2026-06-21T09:50:09+00:00

Mixture of Experts (MoE) routes inputs to specialized sub-networks, reducing inference cost since only a fraction of parameters activate per input

Summary

By splitting a large model into specialized sub-networks and only activating the few relevant to each input, Mixture of Experts lets you get the quality of a big model while paying the compute bill of a much smaller one. This matters because it decouples model capability from per-query cost, making large-model inference far more affordable.

Dependents

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IN moe-enables-parameter-scaling-without-inference-cost — Mixture-of-Experts decouples total parameter count from inference cost by activating only a subset per token, as demonstrated by Llama 4's architecture where Scout activates 17B of 109B total and Maverick activates 17B of 400B total.