ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency
Abstract: Large language models (LLMs) have surged in popularity and are extensively used in commercial
applications, where the efficiency of model serving is crucial for the user experience. Most current
research focuses on optimizing individual sub-procedures, e.g. local inference and communication,
however, there is no comprehensive framework that provides a holistic system view for optimizing LLM
serving in an end-to-end manner. In this work, we conduct a detailed analysis to identify major bottlenecks
that impact end-to-end latency in LLM serving systems. Our analysis reveals that a comprehensive LLM
serving endpoint must address a series of efficiency bottlenecks that extend beyond LLM inference.
We then propose ScaleLLM, an optimized system for resource-efficient LLM serving. Our extensive
experiments reveal that with 64 concurrent requests, ScaleLLM achieves a 4.3× speed up over vLLM
and outperforms state-of-the-arts with 1.5× higher throughput.
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