MoE-CAP: Benchmarking Cost, Accuracy and Performance of Sparse Mixture-of-Experts Systems

The MoE-CAP framework introduces a novel approach to benchmarking sparse Mixture-of-Experts (MoE) systems, which are increasingly utilized for scaling Large Language Models efficiently. By focusing on cost, accuracy, and performance metrics, MoE-CAP addresses the shortcomings of existing benchmarks that have limited practical deployment decisions. This framework aims to provide clearer insights into the trade-offs involved in deploying sparse MoE architectures, facilitating more informed choices in real-world applications. As sparse MoE systems gain popularity for their efficiency benefits, MoE-CAP’s comprehensive evaluation criteria help standardize performance assessments. The framework’s development reflects ongoing efforts within the AI research community to optimize large-scale model deployment. By simplifying benchmarking processes, MoE-CAP could accelerate the adoption of sparse MoE models in various domains. This advancement aligns with broader trends in AI toward balancing computational cost and model accuracy.