By far the greatest challenge in supporting generative AI is a huge surge in power loads. Generative AI models use graphics processing unit (GPU) chips which require 10-15 times the energy of a traditional CPU. Many models have billions of parameters and require fast and efficient data pipelines in their training phase, which can take months to complete. ChatGPT 3.5, for instance, has 175 billion parameters and was trained on over 500 billion words of text. To train a ChatGPT 3.5 model requires 300-500 MW of power. Currently, a typical data center requires 30-50 MW of power.
The second AI-generated tsunami is at the back end; a stream of used equipment. AI is driving faster server innovation, particularly in chip design. While this refresh rate will be key to improving efficiency it will also - in tandem with the rise in capacity - increase the scale of e-waste. E-waste is one of the fastest-growing waste streams in the world. By 2030, annual e-waste production is on track to reach a staggering 75 million metric tons. Global e-waste is thought to hold roughly $60 billion-worth of raw materials such as gold, palladium, silver, and copper. However, just 17 percent of global e-waste is documented to be collected and properly recycled each year.
Watch what implications Generative AI has on sustainability commitments for the data center industry and how Iron Mountain Data Centers is positioned to overcome these challenges for its customers.
datacenter.iro...
7 сен 2024
