Optimizing MoE Inference on Edge Hardware

Summary & Methodology Analysis

OrderMoE addresses the performance bottleneck inherent in deploying Mixture of Experts (MoE), a model architecture that activates only a subset of its parameters per input, on distributed edge infrastructure. The core problem is the high cross-server communication overhead caused by the frequent need to access remote experts. OrderMoE mitigates this by constructing an expert similarity model derived from router-induced logits representations. By identifying experts with similar behaviors, the system can partition these experts into specific similarity groups and deploy them to optimize local coverage, reducing the reliance on network-intensive remote calls.

At runtime, the system employs a quality-aware and trajectory-aware selection algorithm. This algorithm decides on a per-request basis whether to incur the latency of invoking a remote target expert or to substitute it with a local expert from the same similarity group. By intelligently balancing these choices, the architecture shifts the operational load toward local hardware, significantly lowering both average and tail latency while simultaneously reducing cross-server traffic.

While effective, the system faces a fundamental trade-off. The paper notes that reducing remote expert invocation and maintaining exact inference quality are inherently conflicting objectives. Consequently, while the framework achieves performance gains, users should expect minor inference quality degradation as a result of substituting remote experts with local counterparts. The paper does not specify the exact magnitude of the quality drop or the specific latency improvements in milliseconds.