What is the impact of varying expert count and routing granularity in SMoES-based MoE-VLMs on throughput and V

Abstract

Abstract: Abstract The rapid evolution of large language models (LLMs) has driven a transformative shift in artificial intelligence (AI), reshaping both research paradigms and practical applications. Distinguished from their predecessors by unprecedented scale and advanced capabilities, LLMs necessitate new frameworks for understanding their development, behavior, and societal impact. This survey systematically reviews recent advancements in LLM techniques across four key dimensions: (1) pre-training methodologies, which establish core model capabilities through large-scale self-supervised training, arc

Research Question

What is the impact of varying expert count and routing granularity in SMoES-based MoE-VLMs on throughput and VQA score on the MMMU benchmark relative to dense VLMs of equivalent parameter count at 7B scale?

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Correction Record

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