Thursday, August 21, 2025

Assessing the Environmental Impact of LLM Inference: Energy, Water, and Carbon Footprint Metrics

How Hungry is AI? Benchmarking AI’s Environmental Footprint

Did you know that the environmental impacts of AI are staggering? Our latest paper, 🔍 “How Hungry is AI?”, dives deep into quantifying the energy, water, and carbon footprint of large language models (LLM) across 30 leading AI models.

Key Findings:

  • Benchmarking Framework: We introduce a novel methodology that combines:

    • Public API performance data
    • Region-specific environmental multipliers
    • Statistical inference of hardware configurations
  • Top Performers: Our analysis spotlights models like:

    • o3 and DeepSeek-R1: Most energy-intensive, consuming over 33 Wh per long prompt.
    • Claude-3.7 Sonnet: Highest in eco-efficiency.
  • Stark Reality: A single GPT-4o query consumes just 0.42 Wh, yet scaling it to 700 million queries daily leads to:

    • Electricity use of 35,000 U.S. homes
    • Freshwater evaporation for 1.2 million people
    • Significant carbon emissions

This research is vital for driving accountability in AI sustainability. Curious to learn more? Share your thoughts and insights! 🌱💡

Source link

Share

Read more

Local News