What is the Mixture of Experts (MoE) and why is it being talked about now?

Have you been hearing about MoE architecture a lot lately? It is not actually new. The term only became a trend in recent months, when companies like Meta and OpenAI started using it in practice.

Mixture of Experts could solve one of the biggest problems in current AI: how to scale models without blowing the entire budget or needing a datacenter the size of a city.

So what is it, and how does it work? Let's go through it point by point.

MoE (Mixture of Experts) is a type of architecture that activates only a small part of the model on each query, the specific "experts".

Think of a team of specialists. If you have an HR question, you don't bother everyone in the company; you go straight to the person who knows the subject. MoE works on the same principle.

The concept goes all the way back to the 1990s, but only in recent years has it become practical at a larger scale.

Today's AI models keep getting bigger, and so do the costs of running them. That is exactly where MoE helps.

Instead of firing up the whole model on every query, MoE runs only a small part of it, the specific experts suited to the task. That means less compute and faster responses, which is key for real deployments in chatbots, mobile apps, or agent systems.

The MoE architecture also scales far better than traditional "monolithic" models. It can grow without costs growing at the same pace. That is why it shows up in more and more commercial systems: Meta uses it in Llama 4, Mistral introduced a pure MoE model in Mixtral, and OpenAI hints at a similar approach in GPT-4 Turbo.

MoE also suits specialized agents. Each "expert" can focus on something different, which raises the quality of answers and cuts the amount of computation.

Put simply, the MoE architecture is a way to have a powerful model that uses only what it actually needs.

We already said MoE picks from several experts on each query. For one query it might select 2 or 8 out of 64 experts. But how does it decide which ones?

That is the job of the so-called routing mechanism.

It assigns each expert a score based on the input token, then picks only the top-scoring ones.

There are several popular ways to implement the routing mechanism. The most common include top-k routing and expert choice routing. You can read more about the differences between them here.

For efficiency, the network should activate evenly, so that one expert does not handle all queries. That helps optimize the whole set of experts. Models analyze the most frequent prompt areas and build areas of expertise around them.

The outputs of the activated experts are combined with a weighted sum. The weights come from a gating function based on each expert's score. Experts with higher scores have more influence on the final output. With top-k routing, experts with lower scores can also contribute, but their influence is smaller.

Training an MoE model is a bit harder, because several things happen at once. The model has to learn not only the task itself but also how to optimize the routing mechanism that decides which expert fits a given input best.

Another challenge is using all experts evenly. Without extra measures, the routing mechanism can favor some experts, which overloads and overfits them while others stay unused. To balance the load, models often use auxiliary loss functions that penalize an uneven split of inputs across experts.

Mixture of Experts is the architecture most large language models use today. It lets you reach higher performance without constantly increasing the size and cost of the model. On each query it activates only a certain part of the model, the specific "experts".

Models built on the MoE architecture are efficient, scalable, and well suited to practical deployments, from chatbots to dedicated agents.

Related articles: