Publication
EcoLLM: Energy-Aware Benchmarking of LLMs for Data Processing Workloads
Pratyush Agnihotri; Manisha Luthra Agnihotri; Carsten Binnig (Hrsg.)
International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM-2026), Nineth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, located at aiDM, Bengaluru, India, ACM, Sigmod 2026, 6/2026.
Abstract
Large language models (LLMs) are increasingly integrated into data management systems, yet their energy consumption remain largely unexplored. In this paper, we present EcoLLM, a workload-centric benchmark for studying energy-aware trade-offs of LLMs in data-processing tasks. EcoLLM models workload families, operator complexity, and data scale as well as supports both local and API-based models, and employs a hybrid energy measurement methodology. Our evaluation reveals several non-obvious findings. First, energy efficiency and task effectiveness are not aligned: highly efficient models can fail completely on pipeline generation tasks, while more energy-intensive models achieve correct results. Second, we observe a consistent trade-off between latency and energy, where lower latency is often achieved at disproportionately higher
energy cost, leading to distinct execution regimes across deployment modes. Finally, per-task energy consumption appears small yet it scales to substantial cost at production workloads, making energy a critical factor in system design. These findings highlight the need for energy-aware and workload-aware model selection in LLM-based data systems.