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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsSometimes, but not as a general rule. The often-quoted phone-charge comparison comes from a 2024 study’s least-efficient tested image-generation model: it used 11.49 watt-hours (Wh) for an inference, close to that paper’s 12 Wh phone-charge benchmark. A later International Energy Agency (IEA) experiment measured about 1.7 Wh of GPU electricity for a specified image-generation task, compared with about 15 Wh to charge a phone. The answer depends on the model and what the measurement counts.
Where the phone-charge comparison comes from
In a 2024 ACM FAccT study, Sasha Luccioni, Yacine Jernite, and Emma Strubell measured energy use across machine-learning tasks and models. For image generation, the paper reports a mean of 2.907 kilowatt-hours (kWh) per 1,000 inferences—about 2.907 Wh per inference when divided by 1,000. That is an average across the evaluated image-generation cases, not a claim that each image always uses that amount.
The striking phone comparison refers to a different result in the same study: its least-efficient evaluated image-generation model used 11.49 Wh per inference. The paper compared that with a 12 Wh smartphone charge, an assumption based on its EPA-related comparison. That result is close to one such charge, but it describes the least-efficient model in the study, not a universal image-generation request. The study also reports a median of 1.35 kWh per 1,000 inferences when comparing task medians; a median and a mean describe different things and should not be conflated. Read the 2024 ACM FAccT paper.
For scale, 1 kWh equals 1,000 Wh. So 2.907 kWh per 1,000 inferences is not 2.907 kWh for one image; it works out to about 2.907 Wh per inference as an average in that study.
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What a later IEA test measured
The IEA’s 2025 Energy and AI report gives a separate, controlled estimate: about 1.7 Wh of GPU electricity for an SD-XL 1.0-base image-generation task, alongside about 15 Wh for charging a mobile phone. Under those stated figures, the measured AI task uses substantially less electricity than the phone-charge comparison.
The boundary matters: the IEA figure covers GPU electricity in its H100 experiments, not the complete energy footprint of a hosted image-generation service. The report cautions that “The results presented in Figure 1.16 and Figure 1.17 should be seen as highly controlled experimental results – real-world implementations are likely to differ.” Its result and the 2024 benchmark cannot be treated as a direct apples-to-apples test because they use different methods, samples, and scopes. See the IEA’s Energy and AI report.
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Why energy use varies
Model choice
A 2025 preprint tested 17 diffusion models and found substantial variation. Its abstract reports an energy difference of up to 46-fold between models; its conclusion says 47-fold. Because the paper gives these two figures in different sections, the precise maximum is not consistent within the preprint. The important point is that results from one model should not be generalized to every image generator. Read the 2025 preprint.
Image resolution
In that same experiment, doubling image resolution increased energy use by between 1.3 and 4.7 times, depending on the model. The range is specific to the models and test conditions in the preprint, not a universal multiplier for all generators.
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Prompt length and content
The preprint found no statistically significant effect from prompt length or content in its tested setup. That does not prove prompts can never affect a service’s energy use; it means this experiment did not find a significant effect under its conditions.
How to interpret an energy figure
Before comparing a reported AI energy figure with a phone charge—or with another AI result—check what the number actually describes:
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- Model and version: Different image-generation models can have very different measured energy use.
- Amount of work: A figure may be per image, per task, or an average across many inferences. Check whether it is a mean, median, or result for a particular high-use model.
- Image settings: Resolution can change energy use; the 2025 preprint’s measured increase varied by model.
- System boundary: GPU-only electricity, inference energy, and a whole hosted service’s energy use are not interchangeable. The IEA’s named test is GPU-only; it does not establish the full footprint of a commercial cloud service.
- Phone benchmark: A phone comparison depends on the assumed energy for a charge. The 2024 paper used 12 Wh; the IEA report uses about 15 Wh.
These studies measure electricity use, not the resulting greenhouse-gas emissions. Emissions also depend on how the electricity is generated, so a watt-hour comparison alone cannot establish an image’s carbon footprint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.So, does making an AI image use as much energy as charging a phone?
It can approach that comparison in a particular case: the least-efficient model in the 2024 study used 11.49 Wh against that paper’s 12 Wh phone-charge benchmark. But the study’s average was much lower, and the IEA later measured about 1.7 Wh of GPU electricity for its specified image-generation task against about 15 Wh for a phone charge. Neither study establishes the whole-service energy use of every current hosted generator. The accurate answer is therefore model- and measurement-dependent—not that every AI image costs a phone charge.
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