Since 2010, the length of training runs has increased by 1.2x per year among notable models, excluding those that are fine-tuned from base models. A continuation of this trend would ease hardware constraints, by increasing training compute without requiring more chips or power. However, longer training times face a tradeoff. For very long runs, waiting for future improvements to algorithms and hardware might outweigh the benefits of extended training.| Epoch AI
Progress in pretrained language model performance outpaces expectations, occurring at a pace equivalent to doubling computational power every 5 to 14 months.| Epoch AI
We investigate four constraints to scaling AI training: power, chip manufacturing, data, and latency. We predict 2e29 FLOP runs will be feasible by 2030.| Epoch AI