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

2024-06-20

arXiv.org

AI Summary

Local deployment of large language models (LLMs) in data-sensitive domains (e.g., healthcare, finance) faces prohibitive hardware costs and benchmarking redundancy due to frequent model iterations; existing evaluation tools neglect economic metrics, hindering informed deployment decisions. Method: We propose the first multi-objective benchmarking framework tailored for LLM local deployment, systematically incorporating cost per query ($) alongside accuracy, latency, throughput, and inference overhead modeling. It enables configuration-driven, reproducible economic evaluation via a modular, open-source Python toolkit integrating hardware telemetry and cross-model (Llama-3, Phi-3, Qwen) and cross-hardware (A10, A100, RTX 4090) benchmarking. Contribution/Results: Experiments demonstrate that our framework significantly improves deployment decision efficiency and enhances predictability of cost savings, thereby filling a critical gap in industrial-grade, economically grounded LLM evaluation.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

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 url{https://github.com/amademicnoboday12/CEBench}.

Problem

• Balancing LLM effectiveness with financial costs in local deployments
• Addressing benchmarking redundancy due to rapid model evolution
• Overcoming limitations of effectiveness-focused toolkits ignoring economic factors

Innovation

• Open-source toolkit for cost-effectiveness benchmarking
• Evaluates trade-offs between expenditure and LLM effectiveness
• Configurable assessments for optimizing deployment decisions