What is Mixture of Experts (MoE)?
Last updated May 2026An architecture that activates only a subset of model parameters for each input, improving efficiency.
Definition
Mixture of Experts (MoE) is a neural network architecture where the model contains multiple "expert" sub-networks, but only activates a small subset for each input token. A routing network decides which experts are most relevant. This allows models to have more total parameters (more knowledge) while using less computation per request.
💡 Example
GPT-4 is rumored to use MoE with 8 expert networks, activating 2 per token. This means it has the knowledge of a much larger model but runs at the speed of a smaller one. Mixtral by Mistral is an explicitly MoE model.
Related concepts
A type of AI trained on massive text datasets to understand and generate human language.
The neural network architecture that powers modern AI language models.
Why this matters
Mixture of Experts (MoE) is the architecture trick that makes huge models run fast. Instead of using all parameters for every query, MoE routes each request to the most relevant subset. This is how Mixtral and GPT-4 achieve high quality at reasonable inference costs.
Real-world example
Mixtral 8x7B has 46 billion total parameters but only activates 12 billion per query — making it as fast as a 12B model while being as smart as a much larger one. GPT-4 is rumored to use a similar MoE approach. This is why some huge models feel surprisingly fast.
The process of running a trained AI model to generate predictions or outputs.
Explore AI tools
Find tools that use mixture of experts (moe) in practice.
What is Mixture of Experts (MoE)?
Mixture of Experts (MoE) is a neural network architecture where the model contains multiple "expert" sub-networks, but only activates a small subset for each input token. A routing network decides which experts are most relevant. This allows models to have more total parameters (more knowledge) while using less computation per request.
How does Mixture of Experts (MoE) work in practice?
GPT-4 is rumored to use MoE with 8 expert networks, activating 2 per token. This means it has the knowledge of a much larger model but runs at the speed of a smaller one. Mixtral by Mistral is an explicitly MoE model.
How does Mixture of Experts improve AI model efficiency?
MoE models activate only a fraction of their total parameters for each input, routing tokens to specialized expert sub-networks. This means a model can have massive total capacity while keeping inference costs manageable, since only the relevant experts process each request.
Which AI models use Mixture of Experts architecture?
GPT-4 is widely believed to use MoE architecture, and Mixtral from Mistral AI is an openly documented MoE model. Google's Switch Transformer also uses MoE. The architecture is becoming more common as companies seek to build larger models without proportionally increasing inference costs.
What are the tradeoffs of Mixture of Experts models?
MoE models offer better performance per compute cost but require more total memory since all expert parameters must be loaded. They can also have inconsistent quality if the routing mechanism sends tokens to suboptimal experts. Training MoE models is more complex than training dense models.