[jstx1]

> Train your LLM at scale on our infrastructure

Is it really their infrastructure or are they using a cloud provider and this wraps it up and provides convenience for a price?

[brucethemoose2]

Azure and such get such massive scaling cost benefits from scaling that HF's own GPUs would probably be more expensive anyway, even if they go AMD/Intel.

It does seem like they should run their own storage nodes, with the sheer quantity of models they host...

[fxtentacle]

Everyone claims that, yet I have never seen it happen.

Typically, small companies get rebates on NVIDIA GPUs, but big established ones do not. So I would expect a startup with 100 GPUs to pay less per GPU than Azure.

[jsemrau]

I'd think "infrastructur" includes the nice front end and Python API that they have proven to be capable to pull off already.

[perfmode]

What’s the difference?

[melx]

You end up paying more in the latter instance.

[marcinzm]

Not counting the cost of learning how to cluster together 500 GPUs, the cost of learning how to train models efficiently on 500 GPUs, the cost of convincing a cloud provider to let you get 500 GPUs, the cost of trying to find a cloud provider that actually has 500 GPUs you can book, etc, etc.