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

**Authors:** Jovan Stojkovic, Esha Choukse, Chaojie Zhang, Inigo Goiri, Josep Torrellas  
**arXiv:** [2403.20306](https://arxiv.org/abs/2403.20306)  
**Date:** 2024-03-29  
**Summary page:** https://www.summarizepaper.com/en/paper/2403.20306v1/

## Key points

- The paper focuses on addressing energy consumption challenges posed by modern large language models (LLMs) in data center environments.
- Efficient inference serving has become a crucial demand as LLMs are widely adopted across industries.
- Deploying top-of-the-line GPUs due to high computational and memory requirements of LLMs brings energy availability to the forefront as a major obstacle.
- Examining trade-offs involved in prioritizing energy efficiency while meeting performance Service Level Objectives (SLOs).
- Identifying adjustable parameters such as inputs, model complexity, and service-level agreements to enhance energy efficiency without compromising performance.
- Analysis of how these parameters impact latency, throughput, and overall energy consumption for optimizing energy usage while maintaining high performance standards.
- Aim of the study is to pave the way for sustainable and cost-effective deployment of LLMs in data center environments by exploring trade-offs comprehensively.
- Highlighted considerations include managing power consumption effectively and navigating SLOs within current LLM frameworks.
- Emphasizes the importance of addressing energy efficiency challenges in LLM deployment for long-term sustainability and cost-effectiveness in data center operations.

## Summary

The paper "Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference" by Jovan Stojkovic et al. from Microsoft Azure Research - Systems and the University of Illinois at Urbana-Champaign focuses on addressing the energy consumption challenges posed by modern large language models (LLMs) in data center environments. As LLMs continue to be widely adopted across industries, efficient inference serving has become a crucial demand. However, with the high computational and memory requirements of these models, there has been a surge in deploying top-of-the-line GPUs which brings energy availability to the forefront as a major obstacle. The paper delves into the trade-offs involved in prioritizing energy efficiency while meeting performance Service Level Objectives (SLOs). By examining factors such as inputs, model complexity, and service-level agreements, the authors identify adjustable parameters that can enhance energy efficiency without compromising performance. Through an analysis of how these parameters impact latency, throughput, and overall energy consumption, valuable insights are provided on optimizing energy usage while maintaining high performance standards. The study aims to pave the way for sustainable and cost-effective deployment of LLMs in data center environments by comprehensively exploring these trade-offs. Additionally, key considerations such as managing power consumption effectively and navigating SLOs within current LLM frameworks are highlighted. This paper underscores the importance of addressing energy efficiency challenges in LLM deployment for long-term sustainability and cost-effectiveness in data center operations.