MoE Analysis Qwen[3.5|3.6]35B-A3B

Recent analysis of the Mixture-of-Experts (MoE) framework in the Qwen[3.5|3.6]35B-A3B model reveals critical insights into the efficacy of the model's routing mechanism. By applying MT-Bench prompts and examining the inference outputs from two MoE models, researchers captured detailed statistics across every MoE layer, including which experts were selected for each token and the confidence levels associated with those selections. This analysis sheds light on how well the routing system is operating, potentially influencing future model enhancements.

For the AI/ML community, the significance of these findings lies in the ability to better understand the decision-making processes within large models. By quantifying the confidence of expert selection, developers can refine routing algorithms to optimize performance and resource allocation, ensuring that the most relevant experts contribute to generating accurate outputs. This could lead to more efficient models that leverage the strengths of conditional computation, ultimately enhancing their capability in various applications.