Will We Run Out of Data? Limits of LLM Scaling Based on Human-Generated Data

"If trends continue, language models will fully utilize this stock between 2026 and 2032" - that will require data centers with their own nuclear reactors (or other power plants) as hinted at by Marc Zuckerberg?

If you take Llama-3-400B, and 30x its data (hitting the data ceiling, AFAICT), 30x its size to match, and the hardware improves by, say, 3x, then you'll use up about a year's worth of energy from a typical nuclear power plant.

If its for training a new foundation model it is not that bad. It's still only a fraction of the energy compared to many human industries. I did rough math some time ago and found that that training llama-3-70B used the equivalent energy to 1/30 of a full loaded container ship going from China to the US. Even scaled up 100x and trained 10x longer, its seems like the energy consumption is relatively small compared to other industries. The fact that people are considering nuclear power for AI training is an advantage not a downside, imo. It should have a much lower CO2 footprint.

You always have to compare the cost to the value it generates. A year of power from a nuclear plant might be used in more productive ways.