Part 10
Completed
Two-Expert Router
Implemented a Mixture of Experts layer with a learned routing mechanism, load balancing, and expert capacity management. Started with a two-expert system to understand routing dynamics, then scaled to multiple experts with top-k routing.
What I Built
Implemented a Mixture of Experts layer with a learned routing mechanism, load balancing, and expert capacity management. Started with a two-expert system to understand routing dynamics, then scaled to multiple experts with top-k routing.
Key Concepts
Mixture of ExpertsLearned RoutingLoad BalancingExpert CapacityTop-K RoutingConditional Computation
Architecture
1
Router Network2
Expert Networks3
Load Balancer4
Capacity Manager5
Gating MechanismResults
Two-expert system achieves 90% of dense model performance with 50% active parameters. Load balancing prevents expert collapse.
Key Learnings
- Routing is the critical component—bad routing destroys model quality
- Load balancing losses are essential for training stability
- Expert specialization emerges naturally with proper routing
Challenges
- Preventing expert collapse (all tokens routed to one expert)
- Balancing load while maintaining routing quality
- Managing expert capacity constraints