MoE (Mixture of Experts)
A model architecture that activates only selected experts per input to improve cost-performance efficiency
#MoE#Mixture of Experts#LLM#inference efficiency
What is MoE?
MoE (Mixture of Experts) is an architecture where only a subset of expert modules is activated for each input instead of running the full model every time. This helps large models preserve capacity while reducing practical inference cost.
Why does it matter?
As LLM quality increases, inference cost can scale quickly. MoE improves cost-performance by computing only the most relevant paths for a given request, which is why it appears frequently in large-scale production model designs.
Practical checkpoints
- Active parameter size: Effective cost is driven more by active parameters than total parameters.
- Routing stability: Output quality can vary based on expert selection, so benchmark consistency matters.
- Infrastructure optimization: Distributed inference, memory placement, and batching strategy heavily affect latency and throughput.
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