Mixture of Experts (MoE) is a neural network architecture that optimizes computational efficiency by activating only a subset of specialized sub-networks, or "experts," for specific inputs, thus reducing the computational cost during inference. This architecture uses a gating mechanism to dynamically route inputs to the most relevant experts, allowing for targeted computation and efficient large-scale deployments through parallel processing across multiple devices. MoEs offer faster training and comparable or superior performance to dense Large Language Models (LLMs) in multi-domain tasks while facing challenges such as load balancing, distributed training complexity, and tuning for stability. With the potential to scale LLMs to trillions of parameters, MoEs provide a promising approach to handling diverse data inputs without incurring prohibitive computational costs, though they require significant infrastructure and careful hyperparameter tuning. Recent advancements and models such as Google's Gemini 1.5 and IBM's Granite 3.0 demonstrate the growing interest and potential of MoE models, suggesting a shift towards more scalable and efficient LLM architectures.