Sparse Mixture of Experts
Definition
Sparse Mixture of Experts distributes individual input elements to a few selected specialized subnetworks.
A sparse Mixture-of-Experts architecture routes each token only to a small selection of specialized subnetworks.
Simply explained
Not the entire expert team works on every part of the task, but only a suitable small selection.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A language model activates two of many experts per token to increase capacity without proportional computational costs.
Limits and pitfalls
- Unequal routing, communication overhead, and expert collapse can reduce efficiency and quality.
Origin & history
Shazeer and co-authors scaled thinly activated expert layers in 2017; Switch Transformer simplified routing in 2021.
Use in mid-sized companies
- Possible application: A language model activates two of many experts per token to increase capacity without proportional computational costs.
Frequently asked questions
What does Sparse Mixture of Experts mean?
A sparse Mixture-of-Experts architecture routes each token only to a small selection of specialized subnetworks. What should be considered? Unequal routing, communication overhead, and expert collapse can reduce efficiency and quality.
Sources
- Outrageously Large Neural Networks · Shazeer et al., Outrageously Large Neural Networks
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
Editorially reviewed on: