Ever wondered how ChatGPT can answer almost any question or how those super-smart image generators can convert your image to a Ghibli one? These fascinating tools are examples of something called Generative AI, a topic I introduced in my previous blog. Think of Generative AI as the technology that powers giant digital brains capable of understanding and generating human-like content, whether it’s text, audio, or video. For today, let’s narrow our focus to the realm of text and language, which is specifically powered by Large Language Models (LLMs).
But how do these digital brains learn so much? The answer is model training. To give you a sense of scale, GPT-4 is trained on 4.9 trillion of words – that’s more text than you could read in several lifetimes! (For reference, 1 trillion is equal to 1,000,000 million) This vast sea of information needs to be processed, analyzed, and understood by the model.
Think of the training process as a student diligently studying countless textbooks and practicing numerous exercises. For an LLM, these “exercises” involve making predictions about the next word in a sentence or answering complex questions based on the patterns it has learned. Every time the model makes a prediction, it compares it to actual data. If it’s wrong, it doesn’t just get a red mark; it subtly adjusts millions, even billions, of internal “weights” and “biases” – the core of its understanding. This constant tweaking and refining happen over countless iterations.
This constant tweaking and refining over countless iterations require immense computational power, far beyond what a regular computer can handle. This is where special chips called Graphics Processing Units (GPUs) come in. Initially designed for gaming graphics, GPUs are incredibly efficient at handling the complex calculations needed to train an LLM. GPUs are like a large team of workers for LLMs, performing many calculations at the same time (a concept called parallel processing), making this intensive process feasible.
Consider the infrastructure needed to support this. These training processes don’t happen on a single computer. They require massive data centers – huge warehouses filled with thousands of powerful computers and, crucially, sophisticated cooling systems to prevent them from overheating due to the intense processing. It requires a substantial amount of energy to run the GPUs.
All this powerful hardware working for extended periods naturally demands a significant amount of energy. The total computational work required for training an AI model is often referred to as “compute.” This term describes the amount of computational power and time needed for the task. Training a large frontier AI model (such as GPT 4.5, DeepSeek- V3, Llama 4) can require an immense number of computations. According to estimates, GPT-4 required a training compute of about 22 trillion trillion (2.2e25) calculations. This high computing directly translates to high energy consumption. The longer the training period and the more complex the model, the more energy it will consume.
According to International Energy Agency (IEA) special report about Energy and AI published in April 2025, GPT-4 training reportedly consumed around 42.4 Gigawatt-hours (GWh) of energy. That’s a staggering amount, equivalent to the daily electricity consumption of around 70,500 households! Think about your city or town – the electricity used by tens of thousands of homes for an entire day, just to train one AI model. By 2030, AI’s energy usage could, according to some estimates, reach an astonishing level, potentially consuming about 10% of all electricity in the US.
While companies like OpenAI and Google have been at the forefront of this development, detailed energy consumption data has often been kept private, making it challenging to fully assess the environmental impact. This is why the recent move by Meta to report energy use and carbon emissions associated with its latest Llama models is a significant step towards greater transparency in the AI industry.
So, the next time you interact with a sophisticated AI, remember the vast amount of energy that went into making it so intelligent. It’s a reminder that while these digital tools are intangible, their creation has very tangible effects on our physical world and the resources we use. Understanding this is the first step towards a more sustainable future for AI.
Image source: https://chatgpt.com/
Prompt used: A hand-drawn, four-panel watercolor illustration titled “The Invisible Cost: Unraveling the Environmental Footprint of Large Language Models” follows a young AI researcher as he learns about the ecological impact of his LLM setup. In a Ghibli-inspired style, vibrant colors and expressive characters depict his journey from excitement about his new AI model to the realization of its environmental cost, culminating in an attempt to offset his carbon footprint with plants.
