Towards Greener LLMs: Bringing Energy-Efficiency to the Forefront of LLM Inference discusses the pressing challenge of making LLMs more energy-efficient. Authored by Jovan Stojkovic et al., the paper explores various trade-offs in achieving energy savings while adhering to performance SLOs (Service-Level Objectives). The research presents differentiable factors such as input types, model configurations, and strategic use of computational resources.

• The study achieves a thorough understanding of how these factors impact latency, throughput, and energy consumption.

It offers a valuable perspective for AI practitioners who seek to balance efficiency with performance, leading the way to more sustainable and cost-effective deployment of LLMs in data centers.