How does the PowerInfer approach scale in terms of memory efficiency and throughput when applied to multi-moda

Abstract

Abstract: The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is vibrant, there hasn't been a concise framework that analyzes the various methods of LLM Inference to provide a clear understanding of this domain. Our survey stands out from traditional literature reviews by not only summarizing the current state of research but also by introducing a framework based on roofline model for systematic analysis of LLM inference techniques. This framework identifies the bottlenecks when de

Research Question

How does the PowerInfer approach scale in terms of memory efficiency and throughput when applied to multi-modal LLM inference (e.g., LLaVA) on consumer-grade GPUs compared to dense inference methods?

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