EDGE OSDEFINITION
Mixture of experts (MoE) is a model architecture that splits parts of a neural network into many smaller sub-networks called experts. A router sends each token to only a few of them, so only a fraction of the model’s parameters work on any given token. This lets a model hold hundreds of billions or even trillions of parameters while keeping the computation per token closer to that of a much smaller model.
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Get a free sizing checkWhy Mixture of Experts Matters
- Efficiency: Gives the quality of a very large model at a lower compute cost per token.
- Speed: Fewer active parameters per token means faster generation than a dense model of the same size.
- Memory: Every expert must still be loaded, so VRAM needs follow total parameters, not active ones.
- Scale: Many of the largest open-weight models, such as GLM-5.2 and Kimi K3, use MoE.
Use Cases
- Large open-weight models for coding, reasoning and chat.
- Serving high-quality models with less compute per request.
- Multilingual and multi-domain models where experts specialize.
- Spreading experts across GPUs in large deployments (expert parallelism).
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