AI uses energy in different ways, depending on the application and infrastructure. Here are the main ways that AI “eats” energy:

1. Training phase

  • High computing power: Training complex AI models, such as deep learning algorithms, requires enormous amounts of computing power. This usually happens on powerful computers with GPUs (graphics processors) or TPUs (Tensor Processing Units), which are intensive consumers of energy.
  • Data centers: AI training is often conducted in large data centers, which consume a lot of energy for both calculations and cooling to prevent hardware overheating.

2. Deployment phase (inference)

  • Once trained, an AI model is used to make predictions or decisions (inference). Although this is less energy intensive than training, it can still take a lot of energy if the model is used frequently or has to respond in real time.

3. Storage and data processing

  • AI needs massive amounts of data to learn. Storing, retrieving and processing this data in data centers contributes significantly to energy consumption.

4. Network usage

  • Cloud-based AI systems continuously send data back and forth across networks. This data traffic also increases energy consumption, especially when large amounts of data are processed.

5. Hardware production

  • Manufacturing specialized hardware, such as GPUs and TPUs, requires raw materials and energy. This indirect energy consumption is often overlooked.

Why is this a problem?

AI's energy consumption has a significant environmental footprint, especially when electricity is generated from fossil fuels. Research shows that some AI models can emit hundreds of tons of CO₂ over their lifecycle. This makes energy efficiency and green energy important for sustainable AI development.

Possible solutions

  • More efficient algorithms: New algorithms that require less computing power.
  • Green energy: Data centers that run on renewable energy sources.
  • Edge computing: Perform processing closer to the user to reduce energy-intensive data traffic.
  • Hardware innovation: Energy-saving hardware designs.

AI offers many benefits, but reducing energy costs is essential to use this technology in a sustainable way

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