Does token scheduling in sparse MoE inference improve tokens-per-second throughput for document-based question

Abstract

Abstract: Selective parameter activation provided by Mixture-of-Expert (MoE) models have made them a popular choice in modern foundational models. However, MoEs face a fundamental tension when employed for serving. Batching, critical for performance in serving, forces the activation of all experts, thereby negating MoEs'benefits and exacerbating memory bandwidth bottlenecks. Existing work on efficient MoE inference are unable to resolve this tension even with extensive workload-specific tuning. We present LYNX, a system that enables efficient MoE inference in a workload-agnostic fashion. LYNX leverages

Research Question

Does token scheduling in sparse MoE inference improve tokens-per-second throughput for document-based question answering tasks (DocVQA, ChartQA) on dense vs. sparse models?

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