 |
VOOZH | about |
|
VOOZH | about |
LLM inference prices
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.
Compute stock growth
The total computing power of the stock of AI chips is growing at a rate of 3.4×/year.
The total computing power of the stock of AI chips is doubling every 6.8 months.
The total computing power of the stock of AI chips is growing by 0.53 OOMs per year.
Training compute
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.
Software progress
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.
Largest AI data center
The largest known AI data center has a computing capacity equivalent to 800,000 NVIDIA H100 chips.
FLOP/s per dollar
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.
Trusted by leading publications and institutions
Open database of AI data centers using satellite and permit data to show compute, power use, and construction timelines.
Get the latest updates on our research and weekly commentary on AI news and developments.
Subscribe3,200+ ML models tracked, 1950–today
Model performance on key benchmarks
Open database of large AI data centers
Chip shipment volumes and revenue
Need deeper insights? Our team offers custom research and advisory services.
Book a consultation
