Resource-Performance Trade-offs in Open-Source Large Language Models: A Comparative Analysis of Deployment Optimization and Lifecycle Management

Organizations face significant challenges when deploying Large Language Models (LLMs) in production environments. High computational requirements and resource constraints are the main causes of these challenges, particularly in situations where optimizing resources is necessary. While recent studies primarily focused on training efficiency, deployment optimization, and lifecycle management have received limited attention. To address this gap, we introduce (1) A comprehensive framework to evaluate resource-performance trade-offs across deployment scenarios, as well as metrics like Resource Utilization Efficiency (RUE) and Lifecycle Impact Factor (LIF), and (2) A thorough analysis of four well-known 7B-parameter models in various production environments, from cloud to edge computing. This study provides useful insights for organizations that use open-source LLMs in resource-constrained environments. Our findings offer practical guidelines for organizations to enhance their deployment strategies while sustaining performance standards and regulatory compliance.