AI’s Environmental Footprint: The Energy & Water Consumption of Leading AI Models

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Artificial Intelligence (AI) has revolutionized industries, from automating customer support to generating creative content. However, as AI models grow in complexity, their environmental impact becomes a pressing concern. The energy and water required to train and run these models contribute significantly to their carbon footprint. This article explores the energy and water consumption of leading AI models and their broader environmental consequences.

 

 

The Energy Demands of AI Models

AI models require substantial computational power, primarily driven by GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units). Training large-scale models can consume as much energy as powering thousands of homes.

 

AI Service Energy per Text Prompt (kWh) Energy per Image (kWh) Water per Text Prompt (Liters) Water per Image (Liters) Estimated Number of GPUs
ChatGPT (OpenAI) 0.001 – 0.01 0.03 – 0.1 0.05 – 0.3 1.5 – 3.0 Approximately 30,000 Nvidia GPUs as of 2023
Perplexity AI 0.0005 – 0.005 0.0029 0.025 – 0.15 1.5–3.0 10,000 H100 GPUs
Blackbox AI 0.001 – 0.008 0.04 – 0.12 0.05 – 0.24 1.5–3.0 100 ~1,000+
Deep Seek AI 0.001 – 0.01 0.035 – 0.1 0.05 – 0.3 N/A 2,048 GPUs for the R1 model
Gemini (Google) 0.001 – 0.007 0.02 – 0.08 0.03 – 0.21 0.6 – 2.4         NA
Microsoft Copilot 0.001 – 0.01 0.03 – 0.1 0.05 – 0.3 1.5 – 3.0 25,000 GPUs

 

Mitigating the Environmental Impact

Despite the challenges, there are ways to mitigate AI’s environmental footprint. These include:

  • Employing more efficient models: Using foundational models can diminish the environmental impact of AI
  • Optimizing data processing sites: Optimizing data processing sites can diminish the environmental impact of AI
  • Investing in energy-efficient processors: Investing in energy-efficient processors can diminish the environmental impact of AI.
  • Utilizing renewable energy sources: Data centres using renewable energy sources can lower their carbon footprint.
  • Improving AI algorithms: AI algorithms can improve climate models and identify more efficient ways to make digital tech, reduce waste in transport, and otherwise cut carbon and water us.

 

Conclusion

In My Opinion as AI continues to advance, so does its environmental impact. While AI-driven innovation offers immense benefits, it is crucial to address the energy and water consumption associated with these technologies. Sustainable AI practices, from green energy sourcing to model efficiency improvements, are essential to minimizing AI’s environmental footprint.

 

Aman Gupta

Shahjahanpur, Uttar Pradesh

MBA 1st Year Student

Universal Ai University