Mixture of Experts (MoE): what it actually does under the hood, and when it pays off
Mixture of Experts (MoE) models offer a way to achieve high performance with lower computational cost per token by activating only a subset of their parameters. While models like Mixtral 8x7B, DeepSeek-MoE, and Qwen2.5-MoE boast large total parameter counts, they only utilize a fraction for each token's processing. This architectural difference means MoE models require significant memory to store all parameters, but offer computational savings once loaded, presenting a trade-off between memory and compute efficiency compared to dense models. AI
IMPACT MoE models offer a path to more efficient inference by reducing active parameters, but require careful consideration of memory constraints.