Personal Note From Patrick, The Editor

Hi Reader, how often do you use ChatGPT?

After discussing how to reduce the energy consumption of -70°C freezers, today we’re tackling the energy demands of AI.

I use AI every day, and yet, there have been at least three times when I couldn’t tell that a video I watched was entirely AI-generated.

While the debate on how AI will transform science is ongoing, its energy consumption is already a pressing issue.

So, let’s explore how AI impacts the environment and just how significant those effects are.

Number Of The Day

NewsCatcher and the AI detection tool from Pangram Labs found that more than 60 000 AI-generated articles are published every day. Note that this excludes other content, such as AI-generated videos published on YouTube. They used their own API and AI detection technology to scan daily published global news articles from over 75,000 sources. While less than 4% of politics or sports articles were AI-generated, more than 10% of science and finance were.

60 000

Maintaning Freezers Sustainably

AI now assists with everything from explaining scientific papers to refining manuscripts—even writing entire books.

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According to the International Energy Agency, electricity consumption of global data centershas surged by 20–40% annually in recent years, now accounting for 1–1.3% of global electricity demand.

​Projections estimate that by 2030, data centers could consume more than 9% of U.S. electricity generation annually—up from an estimated 4% today.

To fully understand AI’s impact, we need to break it down into different lifecycle stages: training, inference, and data storage.

Training an AI Model

AI training is the process of converting raw code into a quasi-intelligent system.

It involves feeding massive datasets (both proprietary and scraped from websites) into AI models so they can learn to generate accurate responses.

• Training 1 billion parameters requires ≈15 MWh of electricity, emitting 5.7 million kg of CO₂e.
• A 6-billion-parameter model consumes ≈103 MWh, releasing
  39 million kg of CO₂e—enough to power 4 U.S. households for a year.
• GPT-3, with 175 billion parameters, used approximately
  1 287 MWh.

OpenAI hasn’t disclosed how many parameters GPT-4 was trained on.
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However, DeepSeek R1—a model that performs at least as well as ChatGPT—was trained on 671 billion parameters (more in our free Slack)

Grok3 allegedly on 2.7 trillion.

Of note, the precise energy consumption depends on training methods, model type, settings, and infrastructure. These factors allow for variations of up to double-digit percentages.

These numbers seem staggering—but only comprise a fraction of the impact.

Inference: The Cost of Every AI Response

Once an AI model is trained, it must generate responses—a process known as inference.

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While training is energy-intensive, inference is an even larger concern because it happens continuously as users interact with AI models.

Tech giants like NVIDIA and Amazon Web Services (AWS) estimate that inference accounts for 80–90% of AI’s total energy consumption

Internal studies by Meta and Google suggest that it requires 1/3-2/3 their total machine learning (ML) energy use.

So what is the cost of using ChatGPT?

A single query to a large language model (LLM) consumes
0.5–50 Wh, releasing 1–5 grams of CO₂.

This is about 10 times the energy required for a Google search.

The energy cost is even higher for image generation, likely 20 times more than text generation.

Research firm SemiAnalysis estimated that ChatGPT required 28,936 GPUs, implying an energy demand of 564 MWh per day.

In other words, ChatGPT’s inference surpasses its total training footprint in just three days.

And it’s not just ChatGPT. With Perplexity, Claude, Grok, and Gemini competing, and Microsoft integrating AI into nearly all applications, AI’s energy consumption will only continue to rise.

Storage: The Cloud’s Carbon Footprint

From emails and lab notes to microscopy images and research papers, we increasingly rely on cloud storage for convenience.

According to some estimates, cloud storage can consume up to 1,000,000 times more energy than local storage on drives.

While it’s difficult to quantify how much of this is specifically due to AI, it’s clear that ChatGPT saves conversations by default, and most image-generation platforms require users to store generated images unless they pay for premium features. The cumulative impact is likely significant.

Already in the early 2020s it was expected that cloud storage alone contributes 1.5% of the entire global carbon footprint.

Applying The Knowledge

Although each prompt to ChatGPT only takes a few Wh, impacts add up. Talking to your ChatGPT app is processing the voice, transcribing it, inferring, searching the web and voice rendering it to talk back to you.

Before using a large language model (LLM), ask yourself:

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If you need the molecular weight of a protein, searching UniProt or Google is probably not only more reliable but also much more sustainable.

Also, model size matters. Energy consumption scales roughly linearly with parameter count, meaning a model trained on 1 billion parameters could require ten times the energy of one trained on 100 million.

While OpenAI doesn’t offer ChatGPT in different sizes, smaller, task-specific models often consume significantly less energy. The same applies to mixture-of-experts models, which selectively activate only portions of the network (see our Slack discussion).

Finally, we did not talk about embodied carbon, i.e., the carbon footprint due to the production of the GPUs, CPUs and servers. Luccioni et al. estimated that for their small 9 Billion parameter model another 11.2 tons of CO2eq can be added.

Upcoming Lesson:

Saving Energy In Your Studies

How We Feel Today

References

Hintemann, R., Hinterholzer, S., 2022. Data centers 2021: Data center boom in Germany continues—Cloud computing drives the growth of the data center industry and its energy consumption. Tech. Rep., Borderstep Institute for Innovation and Sustainability. doi:10.13140/RG.2.2.31826.43207.
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Luccioni, S., Jernite, Y., Strubell, E., 2024. Power hungry processing: Watts driving the cost of AI deployment? Proc. ACM Conf. Fairness, Accountability, and Transparency (FAccT '24), 85–99. Assoc. Comput. Mach., New York, NY, USA. doi:10.1145/3630106.3658542.
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Patterson, D., Gonzalez, J., Le, Q., Liang, C., Munguia, L.-M., Rothchild, D., So, D., Texier, M., Dean, J., 2021. Carbon emissions and large neural network training. arXiv, 2104.10350. doi:10.48550/arXiv.2104.10350.
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de Vries, A., 2023. The growing energy footprint of artificial intelligence. Joule, 7(10), 2191–2194. doi:10.1016/j.joule.2023.09.004.
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Luccioni, A.S., Viguier, S., Ligozat, A.-L., 2023. Estimating the carbon footprint of BLOOM, a 176B parameter language model. J. Mach. Learn. Res., 24, 1–15.

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Edited by Patrick Penndorf
Connection@ReAdvance.com
Lutherstraße 159, 07743, Jena, Thuringia, Germany
​Data Protection & Impressum​
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