How does the inference throughput and memory efficiency of SMoES-based 7B VLMs compare against dense and hard-

Abstract

Abstract: Sparse Mixture-of-Experts (MoE) models can outperform dense large language models at similar computation by activating only a small set of experts per token. However, stacking many expert modules introduces substantial parameter memory, which makes MoE models difficult to deploy in memory-constrained environments such as single-GPU devices. Offloading alleviates this issue by storing inactive experts in CPU memory and loading them on demand, but existing methods remain limited: static caches disregard input-dependent routing, and methods that train separate models to predict expert usage ahead

Research Question

How does the inference throughput and memory efficiency of SMoES-based 7B VLMs compare against dense and hard-routing MoE baselines on MMBench and SEED-Bench under varying batch sizes and sequence lengths?

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