CEBench: A Benchmarking Toolkit for the Cost-Effectiveness of LLM Pipelines

TL;DR

CEBench is an open-source toolkit designed to evaluate and optimize the trade-offs between cost and effectiveness in deploying large language models, aiding stakeholders in making economically viable AI decisions.

Contribution

It introduces a multi-objective benchmarking framework focused on cost-effectiveness, addressing limitations of existing effectiveness-only tools and supporting practical deployment decisions.

Findings

Streamlines evaluation of LLM deployment trade-offs

Enables configuration-based customization for diverse scenarios

Facilitates development of economically viable AI solutions

Abstract

Online Large Language Model (LLM) services such as ChatGPT and Claude 3 have transformed business operations and academic research by effortlessly enabling new opportunities. However, due to data-sharing restrictions, sectors such as healthcare and finance prefer to deploy local LLM applications using costly hardware resources. This scenario requires a balance between the effectiveness advantages of LLMs and significant financial burdens. Additionally, the rapid evolution of models increases the frequency and redundancy of benchmarking efforts. Existing benchmarking toolkits, which typically focus on effectiveness, often overlook economic considerations, making their findings less applicable to practical scenarios. To address these challenges, we introduce CEBench, an open-source toolkit specifically designed for multi-objective benchmarking that focuses on the critical trade-offs between expenditure and effectiveness required for LLM deployments. CEBench allows for easy modifications through configuration files, enabling stakeholders to effectively assess and optimize these trade-offs. This strategic capability supports crucial decision-making processes aimed at maximizing effectiveness while minimizing cost impacts. By streamlining the evaluation process and emphasizing cost-effectiveness, CEBench seeks to facilitate the development of economically viable AI solutions across various industries and research fields. The code and demonstration are available in https://github.com/amademicnoboday12/CEBench.