How does the inference throughput and accuracy of SMoES-based MoE-VLMs with soft modality-guided routing compa

Abstract

Abstract: The transformer architecture has become a cornerstone of modern AI, fueling remarkable progress across applications in natural language processing, computer vision, and multi-modal learning. As these models continue to scale explosively for performance, implementation efficiency remains a critical challenge. Mixture-of-Experts (MoE) architectures, selectively activating specialized subnetworks (experts), offer a unique balance between model accuracy and computational cost. However, the adaptive routing in MoE architectures—where input tokens are dynamically directed to specialized experts base

Research Question

How does the inference throughput and accuracy of SMoES-based MoE-VLMs with soft modality-guided routing compare to dense VLMs of equivalent parameter count on the MMMU benchmark at 7B and 13B scales?

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