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𝗧𝗵𝗲 𝗵𝘂𝗴𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗟𝗟𝗠𝘀 💸
𝗧𝗵𝗲 𝗵𝘂𝗴𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗟𝗟𝗠𝘀 💸
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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.
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.
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One engineer decided to quantify the price of such a large-scale experiment.
One engineer decided to quantify the price of such a large-scale experiment.
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😬 And the bill is hefty: ~13M USD 
😬 And the bill is hefty: ~13M USD 
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This exact number is to take with a grain of salt because many approximations were necessary to get the final result.
This exact number is to take with a grain of salt because many approximations were necessary to get the final result.
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⛔️ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.
⛔️ But still this ballpark means that for this sole experiment, the price is way over what most startups or research labs could afford.
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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!
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!
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Read the full post that quantifies the paper's cost 👉 
Read the full post that quantifies the paper's cost 👉 
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https://152334h.github.io/blog/scaling-exponents/
https://152334h.github.io/blog/scaling-exponents/
𝗧𝗵𝗲 𝗵𝘂𝗴𝗲 𝗰𝗼𝘀𝘁 𝗼𝗳 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗼𝗻 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗟𝗟𝗠𝘀 💸

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.

😬 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.

⛔️ 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 👉 https://152334h.github.io/blog/scaling-exponents/
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