MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems
The Mixture-of-Experts (MoE) architecture is increasingly favored for scaling Large Language Models (LLMs). Its key feature, sparse activation, selectively activates only a subset of parameters (experts) per token, reducing memory bandwidth and compute FLOPs compared to dense models. To capitalize on this, MoE designers leverage heterogeneous compute and memory hardware to lower system costs. However, the interaction between model sparsity and hardware heterogeneity introduces trade-offs in Cost, Accuracy, and Performance (CAP). To address this, we introduce MoE-CAP, a benchmarking method for evaluating sparse MoE systems across these three dimensions. Its key innovation is a sparsity-aware CAP analysis model, the first to integrate cost, performance, and accuracy metrics into a single diagram while estimating the impact of sparsity on system performance. MoE-CAP helps practitioners optimize hardware provisioning for an MoE model-or vice versa. MoE-CAP supports various MoE models and provides more accurate metrics than existing methods.
PDF Abstract
