Strengths
- Enables scaling of model capacity with minimal increase in inference cost.
- Specialized experts improve model behavior on narrow domains.
- Multiple variants and open-source implementations available.
Limitations
- Not a standalone product; requires implementation within models.
- Training instability and token dropping issues in traditional sparse MoE approaches, though some variants address these.