Altman recently shared a concrete determine for the vitality and water consumption of ChatGPT queries. Based on his weblog put up, every question to ChatGPT consumes about 0.34 Wh of electrical energy (0.00034 KWh) and about 0.000085 gallons of water. The equal to what a high-efficiency lightbulb makes use of in a few minutes and roughly one-fifteenth of a teaspoon.
That is the primary time OpenAI has publicly shared such information, and it provides an vital information level to ongoing debates concerning the environmental impression of enormous AI methods. The announcement sparked widespread dialogue – each supportive and skeptical. On this put up I analyze the declare and unpack reactions on social media to have a look at the arguments on each side.
What Helps the 0.34 Wh Declare?
Let’s have a look at the arguments that lend credibility to OpenAI’s quantity.
1. Unbiased estimates align with OpenAI’s quantity
A key motive some think about the determine credible is that it aligns intently with earlier third-party estimates. In 2025, analysis institute Epoch.AI estimated {that a} single question to GPT-4o consumes roughly 0.0003 KWh of vitality – intently aligning with OpenAI’s personal estimate. This assumes GPT-4o makes use of a mixture-of-experts structure with 100 billion energetic parameters and a typical response size of 500 tokens. Nevertheless, they don’t account for different components than the vitality consumption by the GPU servers and they don’t incorporate energy utilization effectiveness (PUE) as is in any other case customary.
A current tutorial research by Jehham et al (2025) estimates that GPT-4.1 nano makes use of 0.000454 KWh, o3 makes use of 0.0039 KWh and GPT-4.5 makes use of 0.030 KWh for lengthy prompts (roughly 7,000 phrases of enter and 1,000 phrases of output).
The settlement between the estimates and OpenAI’s information level means that OpenAI’s determine falls inside an affordable vary, at the least when focusing solely on the stage the place the mannequin responds to a immediate (known as “inference”).
2. OpenAI’s quantity could be believable on the {hardware} stage
It’s been reported that OpenAI servers 1 billion queries per day. Let’s think about the maths behind how ChatGPT may serve that quantity of queries per day. If that is true, and the vitality per question is 0.34 Wh, then the entire day by day vitality might be round 340 megawatt-hours, in keeping with an industry expert. He speculates that this may imply OpenAI may help ChatGPT with about 3,200 servers (assuming Nvidia DGX A100). If 3,200 servers need to deal with 1 billion day by day queries, then every server must deal with round 4.5 prompts per second. If we assume one occasion of ChatGPT’s underlying LLM is deployed on every server, and that the common immediate leads to 500 output tokens (roughly 375 phrases, in keeping with OpenAI’s rule of thumb), then the servers would wish to generate 2,250 tokens per second. Is that life like?
Stojkovic et al (2024) have been capable of obtain a throughput of 6,000 tokens per second from Llama-2–70b on an Nvidia DGX H100 server with 8 H100 GPUs.
Nevertheless, Jegham et al (2025) have discovered that three completely different OpenAI fashions generated between 75 and 200 tokens per second on common. It’s, nonetheless, unclear how they arrived at this.
So evidently we can not reject the concept that 3,200 servers may be capable to deal with 1 billion day by day queries.
Why some specialists are skeptical
Regardless of the supporting proof, many stay cautious or important of the 0.34 Wh determine, elevating a number of key issues. Let’s check out these.
1. OpenAI’s quantity may omit main components of the system
I think the quantity solely contains the vitality utilized by the GPU servers themselves, and never the remainder of the infrastructure – similar to information storage, cooling methods, networking gear, firewalls, electrical energy conversion loss, or backup methods. This can be a widespread limitation in vitality reporting throughout tech firms.
For example, Meta has additionally reported GPU-only vitality numbers prior to now. However in real-world information facilities, GPU energy is just a part of the total image.
2. Server estimates appear low in comparison with trade experiences
Some commentators, similar to GreenOps advocate Mark Butcher, argue that 3,200 GPU servers appears far too low to help all of ChatGPT’s customers, particularly should you think about international utilization, excessive availability, and different functions past informal chat (like coding or picture evaluation).
Different experiences recommend that OpenAI makes use of tens and even tons of of hundreds of GPUs for inference. If that’s true, the entire vitality use might be a lot increased than what the 0.34 Wh/question quantity implies.
3. Lack of element raises questions
Critics, eg David Mytton, additionally level out that OpenAI’s assertion lacks fundamental context. For example:
- What precisely is an “common” question? A single query, or a full dialog?
- Does this determine apply to only one mannequin (e.g., GPT-3.5, GPT-4o) or a mean throughout a number of?
- Does it embrace newer, extra advanced duties like multimodal enter (e.g., analyzing PDFs or producing pictures)?
- Is the water utilization quantity direct (used for cooling servers) or oblique (from electrical energy sources like hydro energy)?
- What about carbon emissions? That relies upon closely on the placement and vitality combine.
With out solutions to those questions, it’s laborious to understand how a lot belief to put within the quantity or methods to examine it to different AI methods.
Views
Are huge tech lastly listening to our prayers?
OpenAI’s disclosure comes within the wake of Nvidia’s release of knowledge concerning the embodided emissions of the GPU’s, and Google’s blog post concerning the life cycle emissions of their TPU {hardware}. This might recommend that the firms are lastly responding to the numerous calls which have been made for extra transparency. Are we witnessing the daybreak of a brand new period? Or is Sam Altman simply taking part in tips on us as a result of it’s in his monetary pursuits to downplay the local weather impression of his firm? I’ll go away that query as a thought experiment for the reader.
Inference vs coaching
Traditionally, the numbers that now we have seen estimated and reported about AI’s vitality consumption has associated to the vitality use of coaching AI fashions. And whereas the coaching stage might be very vitality intensive, over time, serving billions of queries (inference) can really use extra whole vitality than coaching the mannequin within the first place. My very own estimates suggest that coaching GPT-4 might have used round 50–60 million KWh of electrical energy. With 0.34 Wh per question and 1 billion day by day queries, the vitality used to reply person queries would surpass the vitality use of the coaching stage after 150-200 days. This lends credibility to the concept that inference vitality is price measuring intently.
Conclusion: A welcome first step, however removed from the total image
Simply as we thought the controversy about OpenAI’s vitality use had gotten previous, the notoriously closed firm stirs it up with their disclosure of this determine. Many are enthusiastic about the truth that OpenAI has now entered the controversy concerning the vitality and water use of their merchandise and hope that this is step one in the direction of higher transparency concerning the ressource draw and local weather impression of massive tech. Alternatively, many are skeptical of OpenAI’s determine. And for good motive. It was disclosed as a parenthesis in a weblog put up about an a completely completely different matter, and no context was given in any respect as detailed above.
Regardless that we could be witnessing a shift in the direction of extra transparency, we nonetheless want loads of info from OpenAI so as to have the ability to critically assess their 0.34 Wh determine. Till then, it must be taken not simply with a grain of salt, however with a handful.
That’s it! I hope you loved the story. Let me know what you suppose!
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