A bushel.

Yes, but you don’t have Meta’s purchasing power to rent 10,000 GPUs for a month. Economies of scale, my friend!

I’ll reply to myself!

It’s not just about GPU expense. You need a small team of ML data scientists. You need access to (or a way to scrape/generate) a mind-bogglingly broad dataset. You need to clean, normalize, and prepare the dataset. All of this takes a huge amount of expertise, time and money. I wouldn’t be at all surprised if the auxiliary costs surpassed the GPU rental cost.

So the main answer to your question “Why is no one releasing 70b models?” is: it’s really, really, really expensive. Other parts of the answer are: lack of expertise, difficulty of generating a good dataset, and probably a hundred things I haven’t thought of.

But mainly it just comes down to cost. I bet you wouldn’t see any change from $5,000,000 if you wanted to make your own new 70b base model.

It took 3,311,616 hours of training for the llama2 70b base model. At $1/hour for an A100 GPU you’d spend just over $3M and it would take approximately 380 years to train the model.

Scale that across 10,000 GPUs and you’re looking at 2 weeks and a couple of million dollars.

Fine tune training is much, much faster and cheaper.