Surging interest in artificial intelligence systems will add further strain to global electricity grids with the potential to rival the massive energy consumption of Bitcoin. Thankfully, the premier cryptocurrency has shown us a way to mitigate the impact.
A doubling of data-center revenue at Nvidia Corp. last quarter shows that demand for generative applications like ChatGPT hasn’t yet hit its peak. The US chipmaker is the key provider of shovels in this AI goldrush, but those processors are neither cheap nor lean. Its latest flagship, the GH200 Grace Hopper Superchip, which is the size of a postcard, draws up to 1,000 watts — equivalent to a portable heater.
Though most customers will be opting for something less fancy than the Superchip, they do buy them in bulk to connect together into a massive AI server and that’s where the hunger for electricity really kicks in. One study published last year looked at the energy consumption required to train a single large-language model used to output text in multiple languages.
BLOOM from startup HuggingFace drew on 176 billion parameters from 1.6 terabytes of data. It took a cluster of 384 Nvidia A100 graphical processors — GPUs — more than 118 days to crunch, according to the study’s authors. The electricity consumption from running so many GPUs for so long likely created 24.7 metric tons (54,000 pounds) of carbon dioxide, they estimated. But the true cost doubles to 50.5 tons when you take into account the network connections and idle time of the entire system.
Even then, training a model is just the start. According to one estimate from Amazon.com Inc., which runs its own AI servers, 90% of the expense from running artificial intelligence comes in the next phase when users query the model to get results — such as asking ChatGPT for chocolate-cake recipes. The energy expenditure from implementing the data, called inferencing, is hard to calculate, but it’s believed to be roughly in the order of 10 times that required in the first training phase — which means 500 tons of CO2. And a single generative AI query may have a carbon footprint four times larger than a Google search, according to one estimate.
Brute-force number crunching is built into Bitcoin’s design and helps explain why a wave of semiconductors and servers was rolled out around the world in the hope of mining digital gold. An ongoing study at the University of Cambridge estimates that Bitcoin is responsible for 72.5 million tons of carbon dioxide. That figure could be as low as 3 million tons if all Bitcoin mines were run on hydroelectricity.1 Compared to the wastefulness of cryptocurrency, 500 tons of carbon dioxide from a single round of training and deployment seems like nothing. Yet it’s still equal to driving one million miles in a gasoline-powered car, or 500 flights from New York to Frankfurt.
