Post
2252
๐ง๐ต๐ฒ ๐ต๐๐ด๐ฒ ๐ฐ๐ผ๐๐ ๐ผ๐ณ ๐ฟ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต ๐ผ๐ป ๐ณ๐ฟ๐ผ๐ป๐๐ถ๐ฒ๐ฟ ๐๐๐ ๐ ๐ธ
Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs.
One engineer decided to quantify the price of such a large-scale experiment.
๐ฌ And the bill is hefty: ~13M USD
This exact number is to take with a grain of salt because many approximations were necessary to get the final result.
โ๏ธ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.
This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves!
Read the full post that quantifies the paper's cost ๐ https://152334h.github.io/blog/scaling-exponents/
Google DeepMind recently released a great paper that shows optimal hyperparameters to train across different regimes: Scaling Exponents Across Parameterizations and Optimizers, with data from 10,000 training runs.
One engineer decided to quantify the price of such a large-scale experiment.
๐ฌ And the bill is hefty: ~13M USD
This exact number is to take with a grain of salt because many approximations were necessary to get the final result.
โ๏ธ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.
This means that open-sourcing research is more important than ever, to put everyone in the ecosystem on a roughly equal footing. Don't let OpenAI run first, they'll keep everything for themselves!
Read the full post that quantifies the paper's cost ๐ https://152334h.github.io/blog/scaling-exponents/
