“The resources used to train the model can be repurposed to run millions of instances of it (this matches projected cluster sizes by ~2027), and the model can absorb information and generate actions at roughly 10x-100x human speed. … We could summarize this as a ‘country of geniuses in a datacenter’.”

Dario Amodei, CEO of Anthropic, Machines of Loving Grace

“Let’s say each copy of GPT-4 is producing 10 words per second. It turns out they would be able to run something like 300,000 copies of GPT-4 in parallel. And by the time they are training GPT-5 it will be a more extreme situation where just using the computer chips they used to train GPT-5, using them to kind of run copies of GPT-5 in parallel, you know, again, each producing 10 words per second, they’d be able to run 3 million copies of GPT-5 in parallel. And for GPT-6, it’ll just increase again, there’ll be another factor of 10 at play, and so it’ll be 30 million copies in parallel.”

Tom Davidson, researcher at OpenPhil, Future of Life Institute interview

“Once we get to AGI, we won’t just have one AGI. … given inference GPU fleets by then, we’ll likely be able to run many millions of them (perhaps 100 million human-equivalents, and soon after at 10x+ human speed).”