As AI models grow to hundreds of billions or even trillions of parameters, a key challenge is keeping them fast. The answer lies in a technique called Mixture of Experts (MoE).
Instead of activating the entire model for every input token, MoE uses a router network to select only the top one or two most relevant experts. This dramatically reduces computational cost while allowing the total parameter count to scale. Models like Mixtral demonstrate the practicality of this approach, achieving high performance without the typical slowdown.
For those interested in the technical details, the original paper is available on arXiv (2401.04088) and more information can be found on the Mistral AI blog.