Energy-Aware LLMs: A step towards sustainable AI for downstream applications
This work addresses energy sustainability for AI applications in communication networks, but it is incremental as it applies existing techniques to a specific domain.
The study tackled the high energy consumption of Large Language Models (LLMs) in communication networks by proposing an end-to-end pipeline to balance energy efficiency and performance, showing that quantization and pruning techniques reduced energy consumption while improving model performance.
Advanced Large Language Models (LLMs) have revolutionized various fields, including communication networks, sparking an innovation wave that has led to new applications and services, and significantly enhanced solution schemes. Despite all these impressive developments, most LLMs typically require huge computational resources, resulting in terribly high energy consumption. Thus, this research study proposes an end-to-end pipeline that investigates the trade-off between energy efficiency and model performance for an LLM during fault ticket analysis in communication networks. It further evaluates the pipeline performance using two real-world datasets for the tasks of root cause analysis and response feedback in a communication network. Our results show that an appropriate combination of quantization and pruning techniques is able to reduce energy consumption while significantly improving model performance.