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The Price of Prompting: Profiling Energy Use in Large Language Models Inference

arXiv – CS AI|Erik Johannes Husom, Arda Goknil, Lwin Khin Shar, Sagar Sen|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce MELODI, a framework for monitoring energy consumption during large language model inference, revealing substantial disparities in energy efficiency across different deployment scenarios. The study creates a comprehensive dataset analyzing how prompt attributes like length and complexity correlate with energy expenditure, highlighting significant opportunities for optimization in LLM deployment.

Key Takeaways

→MELODI framework enables detailed monitoring and analysis of energy consumption during LLM inference processes.
→The research reveals substantial disparities in energy efficiency across different LLM deployment frameworks and models.
→Prompt attributes including length and complexity significantly correlate with energy expenditure patterns.
→The study creates a novel dataset that can be expanded by other researchers for further energy efficiency research.
→Findings suggest ample scope for optimization and adoption of sustainable measures in LLM deployment.

#llm #energy-efficiency #ai-sustainability #inference-optimization #research-framework #environmental-impact #machine-learning #computational-efficiency

Read Original →via arXiv – CS AI

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