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:
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Benchmarking Framework: We introduce a novel methodology that combines:
- Public API performance data
- Region-specific environmental multipliers
- Statistical inference of hardware configurations
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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.
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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! 🌱💡