mlsanityData to 14 Oct 2026

From Caveman to Expert Analyst: Energy Consumption of Variable LLM Tasks

arXiv 2608.12350 ↗First posted 2 Jul 2026cs.CY cs.AIcited by 0

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Abstract (arXiv)

The energy demand growth and environmental impacts of artificial intelligence (AI) have generated substantial interest in supplying sufficient low-cost electricity for AI-driven data center development. Research on the ability of demand-side management to address these challenges has been more limited. Shifting the amount or timing of demand from retail, corporate, and other organizational behaviors is a plausible option but only if changes in demand-related behavior have important effects on the envi- ronmental and electricity effects of AI. This article tests four retail (i.e., consumer) user behaviors with high behavioral plasticity to assess their technical abatement potential. The research concludes that non- reasoning models provide sufficient quality while consuming close to one-twentieth of energy compared to reasoning models, saving an amount equal to the annual electricity requirement of at least 141,000 US households under daily usage assumptions. Simple prompt modifications can yield additional reduc- tions in energy consumption by up to 65% using non-reasoning models. Specifically, the practice that maintains the highest degree of similarity with the baseline reduces electricity demand in the range of 4 to 35%, an amount equal to the annual electricity requirement of up to 7,200 US households. Although AI advancements make precise estimates of environmental and electricity impacts difficult to assess, the results confirm that certain minimally intrusive best practices aimed at the majority of users can reduce the energy and environmental burdens imposed by AI.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 18 of its 44 references that are in the map. First posted on arXiv 2 Jul 2026.

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18 of 44 references.

References · 18

  1. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  2. 2022Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Modelcited by 265
  3. 2023Power Hungry Processing: Watts Driving the Cost of AI Deployment?cited by 191
  4. 2023From Words to Watts: Benchmarking the Energy Costs of Large Language Model Inferencecited by 170
  5. 2025How Hungry is AI? Benchmarking Energy, Water, and Carbon Footprint of LLM Inferencecited by 76
  6. 2025Energy Considerations of Large Language Model Inference and Efficiency Optimizationscited by 55
  7. 2025Measuring the environmental impact of delivering AI at Google Scalecited by 32
  8. 2024Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systemscited by 28
  9. 2024The Price of Prompting: Profiling Energy Use in Large Language Models Inferencecited by 23
  10. 2026Incompressible Knowledge Probes: Estimating Black-Box LLM Parameter Counts via Factual Capacitycited by 17
  11. 2024Toward Sustainable GenAI using Generation Directives for Carbon-Friendly Large Language Model Inferencecited by 13
  12. 2025Energy Use of AI Inference, Efficiency Pathways, and Test-Time Scalingcited by 13
6 more
  1. 2025From Prompts to Power: Measuring the Energy Footprint of LLM Inferencecited by 12
  2. 2025A Systematic Characterization of LLM Inference on GPUscited by 8
  3. 2025Prompt engineering and its implications on the energy consumption of Large Language Modelscited by 5
  4. 2026The Energy Footprint of LLM-Based Environmental Analysis: LLMs and Domain Productscited by 2
  5. 2026Green LLM Techniques in Action: How Effective Are Existing Techniques for Improving the Energy Efficiency of LLM-Based Applications in Industry?cited by 1
  6. 2026Small Bottle, Big Pipe: Quantifying and Addressing the Impact of Data Centers on Public Water Systemscited by 1

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  3. 2026WattCouncil: Context-Aware Household Energy Scenario Generation With Governed LLMs
  4. 2026How Do Tool-Augmented LLM Agents Perform on Real-World Energy Analytics Tasks?
  5. 2025Context-aware LLM-based AI Agents for Human-centered Energy Management Systems in Smart Buildings
  6. 2025Knowledge Distillation from Large Language Models for Household Energy Modeling
  7. 2026Identifying and Prioritizing Generative AI Use Cases in an Organization: An Industrial Case Study ↗
  8. 2026LLM-Mediated Demand Response Coordination in Smart Microgrids