[hedgehog]

Last I checked they see deep learning training as a niche market, their strategy is to try to win big contracts (HPC etc) and then supply software specifically for that. Then "the community" will supply software. Having spent a bunch of time beating my head on this and related walls it's not clear to me that they're entirely wrong from an economic standpoint. Remember that 2/3 public cloud providers have their own chips as well as NVIDIA's so it would be tough to negotiate a good deal. As a user it's super irritating to be stuck on NVIDIA especially when Jensen gets up on stage to say "haha, Moore's law is over, stop expecting our products to get cheaper."

[marcyb5st]

I hope they change their minds. At least now that generative models are becoming somewhat popular. I'd love to be able to get an AMD card to run generative models, but to the best of my knowledge, they only run on Nvidia hardware

[nl]

No personal experience, but you can actually get Stable Diffusion to run on AMD cards.

It uses DirectML on Windows: https://gist.github.com/averad/256c507baa3dcc9464203dc14610d... This is thanks to Microsoft, not AMD.

On Linux you can use ROCm: https://www.videogames.ai/2022/11/06/Stable-Diffusion-AMD-GP...

The horrible install processes and what a mess this is is all down to AMD.

[hedgehog]

I don't have any experience with DirectML but it sounds promising.

[hedgehog]

I wouldn't hold my breath, and anyway at this point NVIDIA has faster chips and more supported software all the way down the stack. My previous startup tried to solve some of these problems and we built what is as far as I know still the only reasonably complete device-portable deep learning framework. Today something like an RTX 3070 is a good budget option for small experiments and you can always lean on a cloud provider if you need more compute temporarily. Hard to beat a TPU pod when you're in a hurry.