What is Mixture of Experts (MoE)?

Mixture of Experts (MoE) is a neural network architecture that applies a classic engineering mindset to deep learning: divide and conquer. Instead of pushing every input through a monolithic model, MoE splits the workload across a large set of specialized subnetworks — called experts — and intelligently decides which ones to activate per input. It’s like having a room full of specialists, and only calling on the 2 or 3 most relevant ones for the problem at hand.

For anyone designing large-scale models, MoE offers a clever trade-off: scale without paying the full computational bill. Where traditional models activate every parameter on every input, MoE activates just a fraction — often 2 to 4 experts out of dozens or even hundreds — for any given input. The rest remain inactive, conserving resources without compromising the model’s capacity to learn complex functions.

Technically, this sparse activation is enabled by a gating network — a lightweight component that scores the experts and selects the top ones based on the input. This creates a sparse computation graph, which allows you to scale up the model's total parameter count dramatically (often into the hundreds of billions) while keeping per-example compute costs manageable.

What makes MoE powerful isn’t just efficiency — it’s specialization. Each expert can focus on a different region of the input space or a different task, and the gating mechanism learns over time which expert combinations work best. You end up with a model that’s both massive and smart about how it uses its capacity — routing each input through the experts best suited to handle it, rather than treating all data the same.

Basically, Mixture of Experts helps you scale deep learning models in a way that’s modular, efficient, and aligned with real-world complexity — where not every problem needs the same solution.