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You are at:Home » ChatGpt may not be as hunger for power as it once was supposed to
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ChatGpt may not be as hunger for power as it once was supposed to

Adnan MaharBy Adnan MaharFebruary 11, 2025No Comments4 Mins Read0 Views
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Openai’s chatbot platform, ChatGpt, may not be as powerful as it once was supposed to. However, its appetite relies heavily on how ChatGpt is used, and the AI ​​model answering the queries, according to a new survey.

A recent analysis by Epoch AI, a non-profit AI research institute, attempted to calculate the amount of energy a typical ChatGPT consumes. A commonly cited statistic is that ChatGpt requires about 3 watts of power to answer a single question, i.e. 10 times more than Google searches.

Epoch believes it is an overrated one.

Using chatGPT’s latest default model of OpenAI, GPT-4o, as a reference, Epoch discovered that the average ChatGPT query consumes about 0.3 watt-hour consumption.

“Using energy is no big deal compared to using regular appliances, heating your home, driving a car or driving a car,” said Epoch, analysed. data analyst Joshua Yu told TechCrunch.

As AI companies are trying to quickly expand their infrastructure footprint, AI’s energy usage and its environmental impact are broadly the subject of debate. Last week, a group of over 100 organizations published an open letter to the AI ​​industry and regulators to ensure new AI data centers do not drain natural resources and make utilities rely on non-renewable energy sources. did.

He told TechCrunch that his analysis was spurred by what he characterized as an outdated previous study. For example, the authors of the report, which reached the 3-watt-era estimate, pointed out that Openai assumed that it used an old and inefficient chip to run the model.

Epoch AI ChatGpt Energy Consumption
Image credit: Epoch AI

“I have seen many public discourses that correctly recognize that AI will consume a lot of energy in the coming years, but in fact, the energy that is heading towards AI today is actually accurate. I didn’t explain it,” you said. “Also, some of my colleagues have found that the most widely reported estimates of 3 watts per query are based on rather old research, and are too high to be based on napkin mathematics. I realized that it seemed like that.”

Certainly, the 0.3 watt-hour value of the epoch is also approximate. Openai does not publish the details necessary to perform accurate calculations.

The analysis also does not take into account the additional energy costs caused by ChATGPT functions such as image generation and input processing. I’ve admitted that “long input” ChatGPT queries (for example, with long files attached) are more likely to consume more power in advance than a typical question.

However, he said he expects increased baseline ChatGpt power consumption.

“(The) AI is getting more advanced, and this AI training will probably require much more energy. This future AI will use ChatGpt today, with more tasks and more complex tasks. “It can handle tasks that are much more complicated than the way.”

While there have been significant breakthroughs in AI efficiency over the past few months, the scale of AI deployment is expected to drive the expansion of vast, power-hungry infrastructure. Over the next two years, AI data centers may need almost all of California’s 2022 power capacity (68 GW), according to a RAND report. By 2030, frontier model training could require power equivalent to the power of eight reactors (8 GW), the report predicted.

ChatGpt alone has reached a huge number of people growing, and server requests are in equal demand. Openai plans to spend billions of dollars on new AI data center projects over the next few years, along with several investment partners.

Openai’s attention, along with the rest of the AI ​​industry, has shifted to inference models. This is generally more capable in terms of tasks that can be accomplished, but requires more computing to be performed. In contrast to models like GPT-4O that responds almost instantly to queries, models infer “think” the process of sucking more computing, and thus the power sucking process for seconds to several minutes.

“Inference models increasingly take on tasks that older models can’t do, generate more (data) to do that, and both require more data centers,” you said.

Openai has begun releasing more power-efficient inference models like the O3-Mini. However, at least at this point, it appears unlikely that an increase in efficiency will offset the increased power demand due to the “thinking” process of inference models and the increased use of AI around the world.

You suggested that anyone concerned about the AI ​​energy footprint would use apps such as ChatGPT at all or choose a realistic model that would minimize the computing required.

“Try using a small AI model, such as the GPT-4o-Mini (Openai’s),” you said.



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Adnan Mahar
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Adnan is a passionate doctor from Pakistan with a keen interest in exploring the world of politics, sports, and international affairs. As an avid reader and lifelong learner, he is deeply committed to sharing insights, perspectives, and thought-provoking ideas. His journey combines a love for knowledge with an analytical approach to current events, aiming to inspire meaningful conversations and broaden understanding across a wide range of topics.

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