Why are MoE models larger but cheaper to run?
This tests your understanding of sparse activation versus dense models. A great answer defines Mixture-of-Experts (MoE) as a system with a router and multiple expert sub-networks, explaining that only a fraction of the total parameters are activated for any given token, which drastically reduces computational cost (FLOPs) during inference. A red flag is describing MoE as a simple ensemble without mentioning the sparse routing mechanism that enables its efficiency.
This question tests your understanding of sparse model architectures and the critical difference between parameter count and computational cost. Interviewers want to see if you grasp the mechanics that make models like Mixtral 8x7B efficient. A strong answer first defines MoE as a model with a router and multiple 'expert' networks. It then explains that for each input token, the router selects a small subset of experts (e.g., 2 of 8) to process it. This means while the total parameter count is large, the active computation per token is low, making it 'cheaper to run' than a dense model of similar size. A common wrong answer is to describe it as a simple ensemble, missing the sparse activation which is the key to its efficiency.
Read the original → Wikipedia: Mixture of experts
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