ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency

TL;DR

ScaleLLM is a comprehensive framework designed to optimize end-to-end efficiency in large language model serving, significantly reducing latency and increasing throughput in commercial applications.

Contribution

The paper introduces ScaleLLM, a holistic system that addresses multiple bottlenecks in LLM serving, surpassing existing solutions in speed and throughput.

Findings

Achieves 4.3x speedup over vLLM with 64 concurrent requests.

Outperforms state-of-the-art LLM serving systems in throughput.

Identifies key bottlenecks beyond inference affecting end-to-end latency.

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 4.3x speed up over vLLM and outperforms state-of-the-arts with 1.5x higher throughput.