
Large Language Models (LLMs) have become pivotal in natural language processing, achieving remarkable performance but facing significant challenges in scaling. As these models increase in parameter count to deliver better results, they encounter severe computational and memory constraints. A promising approach to overcome these limitations is the Mixture-of-Experts (MoE) architecture, which efficiently distributes the computational load. This article delves into