How to reduce MoE (Mixture of Experts) inference cost with dynamic expert selection?
Summary The core challenge in reducing Mixture-of-Experts (MoE) inference cost lies in avoiding uniform compute allocation across all inputs. Standard MoE architectures, like Mixtral 8x7B, utilize a fixed top-k (K=2) routing mechanism, which applies the same computational budget regardless of input complexity. This leads to significant inefficiency for simple or redundant tokens. The proposed solution … Read more