Beyond Task-Agnostic: Task-Aware Grouping for Communication-Efficient Multi-Task MoE Inference

Researchers propose Task-Aware Coactivation Grouping (TACG), a framework for optimizing Mixture-of-Experts (MoE) model inference across distributed GPUs by grouping experts based on task-specific activation patterns rather than global averages. The approach reduces communication costs by 31.39% while maintaining load balance, addressing a critical efficiency bottleneck in multi-task AI serving.

This research tackles a fundamental challenge in scaling sparse neural networks: the inefficiency of deploying Mixture-of-Experts models across multiple GPUs. MoE models conditionally activate subsets of parameters, enabling massive capacity gains, but distributed inference creates communication overhead when experts reside on different devices. Prior work assumed a single optimal expert placement by averaging routing patterns globally, missing the reality that different tasks activate expert combinations uniquely.

The insight driving this work reflects a broader maturation in AI infrastructure optimization. As organizations deploy large models in production, performance gains shift from raw computation to communication and orchestration efficiency. Task-aware grouping recognizes that inference patterns vary significantly: an expert pair tightly coupled in language tasks may be uncorrelated in vision tasks. By deriving per-task expert affinities and reweighting co-activation graphs accordingly, TACG achieves substantially better locality.

The addition of Generic Expert Shared Replication (GESR) provides practical robustness against runtime distribution shifts—a critical consideration for production systems where workloads diverge from training assumptions. This two-tier approach balances static optimization with dynamic adaptation.

For AI infrastructure developers and organizations running MoE models, this framework directly impacts operational costs and latency. A 31% communication reduction translates to faster inference and lower GPU utilization, enabling more efficient resource allocation. The maintained Jain fairness index (0.9975) indicates equitable load distribution, preventing scenarios where certain experts become bottlenecks. As MoE adoption accelerates in large language models and multimodal systems, such deployment optimizations become critical competitive advantages in inference efficiency.

• →Task-aware expert grouping reduces cross-GPU communication by 31.39% compared to task-agnostic baseline methods
• →Expert co-activation patterns vary significantly across task families, invalidating single global deployment strategies
• →Generic Expert Shared Replication provides robustness against runtime workload distribution shifts in production environments
• →Framework maintains near-perfect load balancing (0.9975 Jain fairness index) while optimizing communication locality
• →Results hold across multiple open-source MoE architectures, demonstrating generalizability across model variants