Publication
EcoLLM: Energy-Aware Benchmarking of LLMs for Data Processing Workloads
Pratyush Agnihotri; Manisha Luthra; Carsten Binnig
In: Proceedings of the Ninth International Workshop on Exploiting Artificial Intelligence Techniques for Data Management, aiDM 2026, Bengaluru, India, 31 May 2026 - 5 June 2026. International Workshop on Exploiting Artificial Intelligence Techniques for Data Management (aiDM), Pages 3-12, ACM, 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, op-
erator 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. Sec-
ond, 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 deploy-
ment 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.