Altman recently shared a concrete determine for the vitality and water consumption of ChatGPT queries. In response to his weblog submit, 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 knowledge, and it provides an vital knowledge level to ongoing debates in regards to the environmental influence of enormous AI programs. The announcement sparked widespread dialogue – each supportive and skeptical. On this submit I analyze the declare and unpack reactions on social media to have a look at the arguments on either side.
What Helps the 0.34 Wh Declare?
Let’s have a look at the arguments that lend credibility to OpenAI’s quantity.
1. Impartial estimates align with OpenAI’s quantity
A key motive some take into account 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. Nonetheless, they don’t account for different elements 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 latest 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 knowledge level means that OpenAI’s determine falls inside an inexpensive vary, at the very 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 take into account the maths behind how ChatGPT might serve that quantity of queries per day. If that is true, and the vitality per question is 0.34 Wh, then the overall day by day vitality might be round 340 megawatt-hours, in keeping with an industry expert. He speculates that this is able to imply OpenAI might help ChatGPT with about 3,200 servers (assuming Nvidia DGX A100). If 3,200 servers must 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 want to generate 2,250 tokens per second. Is that reasonable?
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.
Nonetheless, 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 might have the ability to deal with 1 billion day by day queries.
Why some consultants are skeptical
Regardless of the supporting proof, many stay cautious or vital of the 0.34 Wh determine, elevating a number of key considerations. Let’s check out these.
1. OpenAI’s quantity may omit main elements of the system
I think the quantity solely consists of the vitality utilized by the GPU servers themselves, and never the remainder of the infrastructure – akin to knowledge storage, cooling programs, networking tools, firewalls, electrical energy conversion loss, or backup programs. This can be a widespread limitation in vitality reporting throughout tech corporations.
As an illustration, Meta has additionally reported GPU-only vitality numbers previously. However in real-world knowledge facilities, GPU energy is simply a part of the total image.
2. Server estimates appear low in comparison with trade stories
Some commentators, akin to GreenOps advocate Mark Butcher, argue that 3,200 GPU servers appears far too low to help all of ChatGPT’s customers, particularly when you take into account international utilization, excessive availability, and different functions past informal chat (like coding or picture evaluation).
Different stories recommend that OpenAI makes use of tens and even a whole bunch of 1000’s of GPUs for inference. If that’s true, the overall 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 primary context. As an illustration:
- 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 complicated duties like multimodal enter (e.g., analyzing PDFs or producing photos)?
- 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 exhausting to know the way a lot belief to put within the quantity or how you can examine it to different AI programs.
Views
Are huge tech lastly listening to our prayers?
OpenAI’s disclosure comes within the wake of Nvidia’s release of information in regards to the embodided emissions of the GPU’s, and Google’s blog post in regards to the life cycle emissions of their TPU {hardware}. This might recommend that the companies are lastly responding to the various calls which were made for extra transparency. Are we witnessing the daybreak of a brand new period? Or is Sam Altman simply enjoying tips on us as a result of it’s in his monetary pursuits to downplay the local weather influence of his firm? I’ll go away that query as a thought experiment for the reader.
Inference vs coaching
Traditionally, the numbers that we have now seen estimated and reported about AI’s vitality consumption has associated to the vitality use of coaching AI fashions. And whereas the coaching stage could be very vitality intensive, over time, serving billions of queries (inference) can truly use extra whole vitality than coaching the mannequin within the first place. My very own estimates suggest that coaching GPT-4 could 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 consumer 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 talk 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 talk in regards to the vitality and water use of their merchandise and hope that this is step one in direction of larger transparency in regards to the ressource draw and local weather influence of huge tech. However, many are skeptical of OpenAI’s determine. And for good motive. It was disclosed as a parenthesis in a weblog submit about an an entirely completely different matter, and no context was given by any means as detailed above.
Although we could be witnessing a shift in direction of extra transparency, we nonetheless want a variety of data from OpenAI so as to have the ability to critically assess their 0.34 Wh determine. Till then, it ought to 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 assume!
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