AI Interview Series #4: Transformers vs Mixture of Experts (MoE)

Mixture of Experts (MoE) models achieve faster inference speeds despite containing significantly more parameters than traditional Transformers by employing a sparse activation mechanism. Unlike standard Transformers, where all parameters are engaged for each token, MoE models utilize a routing network to activate only a small subset of expertstypically the top-Kper token, drastically reducing computational load. For example, the Mixtral 87B model has 46.7 billion total parameters but activates only around 13 billion during inference, enabling more efficient processing. This sparse compute approach allows MoE models to scale to larger sizes, such