Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference

The paper explores the trade-offs required to optimize energy efficiency in the deployment of LLMs under specific performance service-level agreements (SLOs). By adjusting various operational knobs, this research seeks to understand how to deliver LLM services sustainably in data centers.

• Dives into the balance between model power usage and performance meeting SLOs.
• Dissects the impact of tweaks on latency, throughput, and, importantly, energy consumption.
• Offers practical insights for optimizing LLMs deployment without service quality compromises.
• Positions energy efficiency as a priority in the artificial intelligence industry.

This study is critical in emphasizing the importance of energy-efficient approaches to deploying LLMs, which are typically resource-hungry. It contributes to the ongoing discussion on making AI more sustainable, ensuring that solutions powered by LLMs are both environmentally and economically viable.