Altman recently shared a concrete determine for the vitality and water consumption of ChatGPT queries. In line with 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 essential knowledge level to ongoing debates in regards to the environmental influence of huge 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 take a look at the arguments on either side.
What Helps the 0.34 Wh Declare?
Let’s take a look at the arguments that lend credibility to OpenAI’s quantity.
1. Impartial estimates align with OpenAI’s quantity
A key purpose some take into account the determine credible is that it aligns carefully 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 – carefully 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 educational examine 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, a minimum of when focusing solely on the stage the place the mannequin responds to a immediate (referred to as “inference”).
2. OpenAI’s quantity may be believable on the {hardware} degree
It’s been reported that OpenAI servers 1 billion queries per day. Let’s take into account 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 full each day vitality could possibly be round 340 megawatt-hours, in keeping with an industry expert. He speculates that this could imply OpenAI may help ChatGPT with about 3,200 servers (assuming Nvidia DGX A100). If 3,200 servers need to deal with 1 billion each 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 typical immediate ends in 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 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 plainly we can’t reject the concept that 3,200 servers may be capable to deal with 1 billion each day queries.
Why some specialists are skeptical
Regardless of the supporting proof, many stay cautious or essential 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 believe the quantity solely contains the vitality utilized by the GPU servers themselves, and never the remainder of the infrastructure – equivalent to knowledge storage, cooling programs, networking gear, firewalls, electrical energy conversion loss, or backup programs. This can be a widespread limitation in vitality reporting throughout tech firms.
As an example, Meta has additionally reported GPU-only vitality numbers up to now. However in real-world knowledge facilities, GPU energy is barely a part of the complete image.
2. Server estimates appear low in comparison with trade studies
Some commentators, equivalent 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 studies recommend that OpenAI makes use of tens and even lots of of hundreds of GPUs for inference. If that’s true, the full vitality use could possibly be a lot larger 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. As an 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 embody newer, extra complicated duties like multimodal enter (e.g., analyzing PDFs or producing photographs)?
- 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 onerous to understand how a lot belief to position within the quantity or how you can evaluate 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 knowledge 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 firms 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 methods 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’ve seen estimated and reported about AI’s vitality consumption has associated to the vitality use of coaching AI fashions. And whereas the coaching stage will 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 might have used round 50–60 million KWh of electrical energy. With 0.34 Wh per question and 1 billion each 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 carefully.
Conclusion: A welcome first step, however removed from the complete 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 better transparency in regards to the ressource draw and local weather influence of massive tech. Then again, many are skeptical of OpenAI’s determine. And for good purpose. It was disclosed as a parenthesis in a weblog submit about an an entirely completely different subject, and no context was given in any way as detailed above.
Though we may be witnessing a shift in direction of extra transparency, we nonetheless want a variety 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 assume!
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