What is the inference throughput and memory efficiency trade-off of SMoES MoE-VLMs relative to dense VLMs of e

Abstract

Abstract: The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These models aim to bridge the gap between text and visual information, enabling a more comprehensive understanding of multimedia data. However, as these models become larger and more complex, they also become more challenging to train and deploy. One approach to addressing this challenge is the use of sparsely-gated mixture-of-experts (MoE) techniques, which divide the model into smaller, specialized sub-models that can join

Research Question

What is the inference throughput and memory efficiency trade-off of SMoES MoE-VLMs relative to dense VLMs of equivalent parameter count (7B and 34B) on multimodal reasoning benchmarks like MMMU and MathVista?

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