MoE推理的通信瓶颈终于有了任务感知的解法——做多任务MoE部署的团队可以直接参考TACG的静态分组+GESR动态复制策略，实测通信成本降三成，公平性几乎无损。
稀疏激活的混合专家（MoE）模型在分布式推理中面临跨GPU通信和负载不均问题。现有方法通过全局路由痕迹平均化处理专家共激活模式，忽略了多任务场景下不同任务族的异质性。本文提出TACG框架，利用任务族特定的调度和共激活痕迹，为每个专家推导任务族偏好，重新加权共激活图，使同族专家优先部署在同一GPU上。同时引入GESR机制，复制通用专家到少量辅助GPU，在线推理时兼顾局部性和负载均衡。在三个开源MoE模型上，该方法平均降低通信成本31.39%，Jain公平指数达0.9975，且对推理数据分布偏移具有鲁棒性。
Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference
Sparsely activated Mixture-of-Experts (MoE) models scale capacity via conditional computation, but distributed inference suffers from cross-GPU expert communication and routing-induced load imbalance. Existing placement methods reduce this cost by co-locating frequently co-activated experts; however, they derive a single deployment plan from globally aggregated routing traces, thereby averaging away the heterogeneous, task-specific co-activation patterns that actually drive communication in multi-task serving. We observe that expert co-activation is strongly task-conditioned: pairs tightly coupled in one task family are often uncorrelated in another, so effective deployment should group experts by task-aware co-activation rather than by a task-agnostic average. Based on this insight, we propose \emph{Task-Aware Coactivation Grouping} (TACG), a deployment-time framework that uses family-specific dispatch and co-activation traces to derive per-expert task-family preferences, reweights the co-activation graph so that intra-family locality dominates grouping, and assigns each expert to a primary GPU under exact capacity constraints. To keep the static placement robust under online workload skew, we further introduce \emph{Generic Expert Shared Replication} (GESR), a lightweight companion that identifies generic experts with consistently central co-activation profiles, replicates them across a small set of secondary GPUs, and applies locality- and load-aware selection at serving time. Experiments on three representative open-source MoE models demonstrate that our framework reduces the average communication cost by 31.39\% over the baseline, while preserving an average Jain fairness index of 0.9975. This advantage persists even under severe distribution shifts in the inference data, consistently outperforming strong baselines.