Sparse Mixture of Experts (SMoE) performs conditional computation by selectively activating a subset of experts, thereby enabling scalable parameter growth in large language models (LLMs). However, the expanded parameter scale exceeds the memory capacity of a single device, necessitating distributed deployment for inference. This setup introduces two critical challenges: (1) Communication Issue: Transferring features to devices with activated experts leads to significant communication overhead. (2) Computational Load Issue: Skewed expert activation overloads certain GPUs, resulting in load imbalance across devices. Among these, communication overhead is identified as the main bottleneck in SMoE inference. Nevertheless, reducing communication between devices may exacerbate computational load imbalance, leading to device idleness and resource waste. Therefore, we present GRACE-MoE, short for Grouping and Replication with Locality-Aware Routing for SMoE inference. GRACE-MoE is a co-optimization framework that jointly reduces communication overhead and alleviates computational load imbalance. Specifically, the framework comprises two key phases: (1) Grouping & Replication: This phase groups experts based on their affinity to reduce cross-device communication. Additionally, dynamic replication is applied to address load skew, improving computational load balance across GPUs. (2) Routing: This phase employs a locality-aware routing strategy with load prediction. It prioritizes local replicas to minimize communication overhead and balances requests across remote replicas when necessary. Experiments on diverse models and multi-node, multi-GPU environments demonstrate that GRACE-MoE efficiently reduces end-to-end inference latency, achieving up to 3.79x speedup over state-of-the-art systems. Code for GRACE-MoE will be released upon acceptance.

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