  In the rapidly evolving realm of artificial intelligence, deploying large
language models (LLMs) poses increasingly pressing computational and
environmental challenges. This paper introduces MELODI - Monitoring Energy
Levels and Optimization for Data-driven Inference - a multifaceted framework
crafted to monitor and analyze the energy consumed during LLM inference
processes. MELODI enables detailed observations of power consumption dynamics
and facilitates the creation of a comprehensive dataset reflective of energy
efficiency across varied deployment scenarios. The dataset, generated using
MELODI, encompasses a broad spectrum of LLM deployment frameworks, multiple
language models, and extensive prompt datasets, enabling a comparative analysis
of energy use. Using the dataset, we investigate how prompt attributes,
including length and complexity, correlate with energy expenditure. Our
findings indicate substantial disparities in energy efficiency, suggesting
ample scope for optimization and adoption of sustainable measures in LLM
deployment. Our contribution lies not only in the MELODI framework but also in
the novel dataset, a resource that can be expanded by other researchers. Thus,
MELODI is a foundational tool and dataset for advancing research into
energy-conscious LLM deployment, steering the field toward a more sustainable
future.
