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The Mixture of Experts (MoE) (https://www.cs.toronto.edu/~fritz/absps/jjnh91.pdf) architecture is a pivotal innovation for Large Language Models, addressing the unsustainable scaling costs of traditional dense models. Instead of activating all parameters for every input, MoE uses a gating network to dynamically route tasks to a small subset of specialized "expert" networks.

This "divide and conquer" approach enables models with massive parameter counts, like the successful Mixtral 8x7B (https://arxiv.org/pdf/2401.04088), to achieve superior performance with faster, more efficient computation. While facing challenges such as high memory (VRAM) requirements and training complexities like load balancing, MoE's scalability and specialization make it a foundational technology for the next generation of AI.

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