The cost to inference an LLM at a fixed level of performance has fallen rapidly, but unevenly across tasks.

The cost to inference an LLM at a fixed level of performance has been halving every 2 months.

The cost to inference an LLM at a fixed level of performance has fallen by 2 OOMs per year.

The total computing power of the stock of NVIDIA chips is growing at a rate of 2.3×/year.

The total computing power of the stock of NVIDIA chips is doubling every 10 months.

The total computing power of the stock of NVIDIA chips is growing by 0.36 OOMs per year.

Training compute for frontier language models has been growing at 5× per year since 2020.

Training compute for frontier language models has been doubling every 5.2 months since 2020.

Training compute for frontier language models has been growing at 0.7 OOMs per year since 2020.

Pre-training compute efficiency is improving at roughly 3.0× per year.

Pre-training compute efficiency is doubling roughly every 7.6 months.

Pre-training compute efficiency is improving by roughly 0.5 OOMs per year.

The largest known AI data center has computing power equivalent to 600,000 NVIDIA H100 chips.

AI chip performance per dollar has improved by 37% per year.

AI chip performance per dollar has doubled every 2.2 years.

AI chip performance per dollar has improved by 0.14 OOMs per year.