What is the core idea behind mixture of experts?
A mixture-of-experts model is large in total but only turns on the few experts it needs for each token.
How does mixture of experts differ from related concepts?
| Concept | Difference |
|---|---|
| MoE vs Dense Model | A dense model uses all its parameters for every input. An MoE model activates only a subset. |
| MoE vs Ensemble | An ensemble runs several full models and combines them. MoE routes each input to parts of one model. |
| MoE vs Fine-Tuning | Fine-tuning adapts a model to a task. MoE is an architecture choice about how the model is built. |
How does mixture of experts work?
- The model contains many expert sub-networks
- A router decides which experts handle each token
- Only those experts activate, saving computation
- Their outputs are combined into the final result
Why do labs use mixture of experts?
- It scales total parameters without scaling cost per token
- It enables very large models that stay fast to run
- It reduces inference cost at a given capability
- It powers several leading 2026 open and closed models
Why is mixture of experts important?
Mixture of experts is a key reason 2026 models can be enormous yet remain affordable to serve. Models like DeepSeek and Kimi K3 use it to reach trillions of total parameters while activating only a fraction per token.
How is mixture of experts used in practice?
MoE is used in frontier language models to balance capability against serving cost. Its challenges include harder training, load balancing across experts, and higher memory needs, since all experts must be stored even if few are active.
Frequently Asked Questions
What does mixture of experts do?
It lets a model have a very large number of parameters while only using a small, relevant subset for each input, which keeps computation and cost down.
Which models use mixture of experts?
Many leading 2026 models, including open-weight systems like DeepSeek and Kimi K3, use MoE architectures to scale efficiently.