전문가의 혼합

A standard transformer runs every input through the same dense layers, so making the model smarter usually means making every computation more expensive. Mixture of Experts breaks that link. It replaces the big feed-forward layer with many smaller 'expert' networks plus a small 'router' that decides which experts handle each token. Typically only the top 1 or 2 experts fire, so a model can have hundreds of billions of total parameters but only activate a small fraction per token. This is why models like Mixtral 8x7B and Switch Transformer reach high quality without proportionally high inference cost. The trade-off is complexity: all expert weights need accessible storage, potentially spread across devices or offloaded, and the router can misroute or overload some experts, so training and serving require careful routing and resource management.