What Are “Experts” in AI Models Like Llama 4?

If you've been keeping up with the latest advancements in artificial intelligence, you may have come across the term "experts" in relation to new models like Llama 4. At first glance, it might sound like we're talking about human specialists or domain experts — but in the world of AI, “experts” mean something very different.

In this context, "experts" refers to a powerful technique used in large AI models called Mixture of Experts (MoE). This method is a clever way to scale up model capacity while keeping computational costs manageable — a win-win for performance and efficiency.

What Is a Mixture of Experts?

Imagine a traditional AI model as a single, monolithic brain that processes every input the same way, activating all its neurons for every task. While effective, this approach becomes very expensive and inefficient as models grow larger.

A Mixture of Experts model, by contrast, is like a team of specialized brains, each trained to handle different types of inputs or tasks. These specialized components are called “experts,” and the model learns which ones to activate based on the context of the input. Rather than firing up the entire network every time, it selectively routes the input through only a few experts — typically 2 or 4 out of dozens or even hundreds.

Why Use Experts?

This selective activation dramatically reduces the computational load. For example, a model may contain 128 experts, but only use 2 at a time, resulting in the performance benefits of a much larger model without the prohibitive cost of running all 128 experts simultaneously.

The Llama 4 series from Meta exemplifies this approach. One variant, Llama 4 Maverick, has 128 experts and a total of 17 billion active parameters — meaning that only a fraction of its total capacity is used at any moment, but it can still produce high-quality, context-sensitive outputs. Another version, Llama 4 Scout, uses 16 experts, optimized for lighter-weight use cases.

This method enables the model to be both scalable and flexible — two characteristics that are crucial as we push the boundaries of what AI can do.

How Does It Work?

Under the hood, a gating mechanism decides which experts to activate for a given input. Think of it as a traffic controller that routes information through the most relevant pathways. During training, the model learns which experts are most useful for different types of data, leading to improved specialization and generalization.

Why It Matters

MoE architectures are a big deal because they offer a path forward for building more capable AI systems without needing exponential increases in compute power. They represent a shift from brute-force scaling to smart scaling, where efficiency and performance grow together.

So next time you see a model boasting about its “experts,” you’ll know it’s not relying on a team of PhDs — it’s using a sophisticated architectural design that lets it think smarter, not harder.