GUIDE: A Global Unified Inference Engine for Deploying Large Language Models in Heterogeneous Environments

TL;DR

GUIDE is a comprehensive framework that uses dynamic modeling and simulation-based optimization to improve the deployment efficiency of large language models across diverse hardware and workload scenarios, addressing key bottlenecks.

Contribution

We introduce GUIDE, a systematic inference engine that predicts and optimizes LLM performance in heterogeneous environments, enabling non-experts to deploy models efficiently.

Findings

Prediction errors between 9.9% and 42.3% for key metrics.

Effectively bridges the gap between theoretical and practical performance.

Addresses memory, latency, and throughput bottlenecks in LLM deployment.

Abstract

Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities.Efficiently deploying large language models (LLMs) in real-world scenarios remains a critical challenge, primarily due to hardware heterogeneity, inference framework limitations, and workload complexities. These challenges often lead to inefficiencies in memory utilization, latency, and throughput, hindering the effective deployment of LLMs, especially for non-experts. Through extensive experiments, we identify key performance bottlenecks, including sudden drops in memory utilization, latency fluctuations with varying batch sizes, and inefficiencies in multi-GPU configurations. These insights reveal a vast optimization space shaped by the intricate interplay of hardware, frameworks, and workload parameters. This underscores the need for a systematic approach to optimize LLM inference, motivating the design of our framework, GUIDE. GUIDE leverages dynamic modeling and simulation-based optimization to address these issues, achieving prediction errors between 9.9% and 42.3% for key metrics such as batch latency, TTFT, and decode throughput. By effectively bridging the gap between theoretical performance and practical deployment, our framework empowers practitioners, particularly non-specialists, to make data-driven decisions and unlock the full potential of LLMs in heterogeneous environments cheaply.