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Related Experiment Videos

Comparing energy consumption and accuracy in text classification inference.

Johannes Zschache1, Tilman Hartwig2

  • 1Application Lab for AI and Big Data, German Environment Agency, Alte Messe 6, 04103, Leipzig, Saxony, Germany.

Scientific Reports
|April 17, 2026
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) in natural language processing (NLP) show varied energy use during inference. Accuracy and energy efficiency are distinct, requiring careful evaluation for sustainable AI.

Keywords:
Large Language ModelNLPResource EfficiencySustainable AI

Related Experiment Videos

Area of Science:

  • Artificial Intelligence
  • Computer Science
  • Sustainable Computing

Background:

  • Growing use of large language models (LLMs) in natural language processing (NLP) raises energy efficiency and sustainability concerns.
  • Prior research primarily focused on energy consumption during model training, neglecting the inference phase.

Purpose of the Study:

  • To systematically evaluate the trade-offs between model accuracy and energy consumption during text classification inference.
  • To analyze these trade-offs across diverse model architectures and hardware configurations.

Main Methods:

  • Empirical analysis of text classification inference across various models and hardware.
  • Measurement of energy consumption (mWh to kWh) and accuracy.
  • Correlation analysis between inference energy consumption and model runtime.

Main Results:

  • LLMs can consume significantly more energy than traditional models, sometimes with comparable or lower accuracy in zero-shot settings.
  • Energy consumption varies substantially based on model type, size, and hardware.
  • Inference energy consumption strongly correlates with model runtime, suggesting runtime as a proxy for energy use.

Conclusions:

  • Accuracy and energy efficiency are distinct dimensions and do not always align.
  • Sustainable AI development necessitates systematic evaluation of both performance and resource efficiency.
  • Execution time can be a practical proxy for energy usage when direct measurement is difficult.