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> these people we're talking about the majority of open source developers (I'm one of them). if researchers don't get access to hardware X, they write their pa

> these people we're talking about the majority of open source developers (I'm one of them). if researchers don't get access to hardware X, they write their paper using hardware Y (Nvidia). AMD isn't doing fine because most low level research on AI is done purely on CUDA.

qualifiedeephd 1y ago
Serious researchers use HPC clusters not desktop workstations. Currently the biggest HPC cluster in North America has AMD GPUs. I think it'll be fine.
- pjmlp 1y ago
  Before they became serious researchers they were once upon a time students learning with what their laptops were capable of.
Certhas 1y ago
So nVidia has a huge software lead because of open source developers like you? Or because people employed by nVidia write closed source high performance drivers and kernels? Or because the people employed by Meta and Google that wrote Torch and Tensorflow built it on nVidia? I am really sympathetic to the complaints. It would just be incredibly useful to have competition and options further down the food chain. But the argument that this is a core strategic mistake makes no sense to me.
- imtringued 1y ago
  You don't have to beg for good software quality from Nvidia. Everything else is built on top of that foundation. That's all there is to it.
- regularfry 1y ago
  Nit: just writing Torch/TF isn't what made the difference. Having them adopted by a huge audience outside those orgs is, and that's bottlenecked on the hardware platform choice. AMD has demonstrably not even acknowledged that they needed to play catch-up for a significant chunk of the last 20 years. The mistake isn't a recent one.
- wisty 1y ago
  Also theano, keras ... nvidia made it easy to develop on so that's what people use.
motorest 1y ago
> if researchers don't get access to hardware X, they write their paper using hardware Y (Nvidia). There are plenty of research institutions that can easily spend >$250k on computational resources. Many routinely spend multiples of that volume. They'll be fine.
- impossiblefork 1y ago
  Many institutions don't. Look at China. A couple of years ago people thought people in China weren't doing good AI research, but the thing is, there's good AI research from basically everywhere-- even South America. You can't assume that institutions can spend >$250k on computational resources. Many can, but many can't.

qualifiedeephd 1y ago

Serious researchers use HPC clusters not desktop workstations. Currently the biggest HPC cluster in North America has AMD GPUs. I think it'll be fine.

pjmlp 1y ago
Before they became serious researchers they were once upon a time students learning with what their laptops were capable of.

Certhas 1y ago

So nVidia has a huge software lead because of open source developers like you? Or because people employed by nVidia write closed source high performance drivers and kernels? Or because the people employed by Meta and Google that wrote Torch and Tensorflow built it on nVidia? I am really sympathetic to the complaints. It would just be incredibly useful to have competition and options further down the food chain. But the argument that this is a core strategic mistake makes no sense to me.

imtringued 1y ago
You don't have to beg for good software quality from Nvidia. Everything else is built on top of that foundation. That's all there is to it.
regularfry 1y ago
Nit: just writing Torch/TF isn't what made the difference. Having them adopted by a huge audience outside those orgs is, and that's bottlenecked on the hardware platform choice. AMD has demonstrably not even acknowledged that they needed to play catch-up for a significant chunk of the last 20 years. The mistake isn't a recent one.
wisty 1y ago
Also theano, keras ... nvidia made it easy to develop on so that's what people use.

motorest 1y ago

> if researchers don't get access to hardware X, they write their paper using hardware Y (Nvidia). There are plenty of research institutions that can easily spend >$250k on computational resources. Many routinely spend multiples of that volume. They'll be fine.

impossiblefork 1y ago
Many institutions don't. Look at China. A couple of years ago people thought people in China weren't doing good AI research, but the thing is, there's good AI research from basically everywhere-- even South America. You can't assume that institutions can spend >$250k on computational resources. Many can, but many can't.