How does the inference throughput of MoE-based multimodal streaming recommenders compare to dense baselines (e

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 of MoE-based multimodal streaming recommenders compare to dense baselines (e.g., DeepFM, DCN) under varying numbers of active experts on the Amazon Reviews and MovieLens-20M benchmarks?

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