Mixture of Experts
Mixture of Experts is a neural network architecture that splits computation across multiple specialized sub-networks called experts. A gating mechanism selects only a small subset of experts for each input, so the model can store far more knowledge in its parameters without proportionally increasing the compute needed for every prediction. This sparse activation pattern enables trillion-parameter models that remain practical to run. Also known as: MoE
Understand the Fundamentals
Most neural networks route every input through the same parameters. Mixture of Experts breaks this assumption by activating only a fraction of the network per input, creating a fundamentally different efficiency curve worth understanding.
Build with Mixture of Experts
These guides walk through running, fine-tuning, and serving open-weight expert models, covering the tooling choices and hardware trade-offs you will face at each step.
What's Changing in 2026
Expert-based architectures are rapidly becoming the default design for frontier language models. Tracking which scaling strategies win shapes how teams plan infrastructure and model selection.
Updated April 2026
Risks and Considerations
Training trillion-parameter expert models demands massive compute budgets, raising questions about who gets to build them, how routing failures degrade quality, and what concentration of capability means for the broader ecosystem.
