Generative AI Series

Mixture of Experts

Mixture of Experts is orchestrating a set of models that are trained on a specific domain, to achieve a broader input space.

This blog is an ongoing series on Generative AI and an introduction to mixture of experts model architectures. It provides scalable, high-performing, and efficient LLMs.

We are witnessing an exponential increase in the usage of Language Models (LLMs) in enterprise solutions. There is a growing demand for more capable LLMs, rather than simply increasing the size of existing models and building more generic ones, to cover broader scope. To address this, we require an alternative approach that is both scalable and efficient. As we construct larger models to cover a broader range of use cases, the training and utilization of these models necessitate higher levels of memory and computational resources.

The Mixture of Experts (MoE) model is a new approach that is transforming the way we deal with this situation. It represents an approach that incorporates a dynamic routing mechanism that assigns different “experts” (feed forward networks) to handle different types of data or tasks. This approach helps for a more efficient allocation of computational resources and improved performance.

MoE architecture consists of two main components. The following pictures shows how a generate Mixture of Experts network works.