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Mixture-of-Experts (MoE) has emerged as a favorable architecture in the eraof large models due to its inherent advantage, i.e., enlarging model capacitywithout incurring notable computational overhead. Yet, the realization of suchbenefits often results in ineffective GPU memory utilization, as large portionsof the model parameters remain dormant during inference. Moreover, the memorydemands of large models consistently outpace the memory capacity ofcontemporary GPUs. Addressing this, we introduce SiDA (Sparsity-inspiredData-Aware), an efficient inference approach tailored for large MoE models.SiDA judiciously exploits both the system's main memory, which is now abundantand readily scalable, and GPU memory by capitalizing on the inherent sparsityon expert activation in MoE models. By adopting a data-aware perspective, SiDAachieves enhanced model efficiency with a neglectable performance drop.Specifically, SiDA attains a remarkable speedup in MoE inference with up to3.93X throughput increasing, up to 75% latency reduction, and up to 80% GPUmemory saving with down to 1% performance drop. This work paves the way forscalable and efficient deployment of large MoE models, even inmemory-constrained systems.

SiDA: Sparsity-Inspired Data-Aware Serving for Efficient and Scalable Large Mixture-of-Experts Models