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Comment by LoganDark | Hacker News Reader

PyTorch and CUDA solve completely different problems. CUDA is a general purpose programming environment. PyTorch is for machine learning. PyTorch won't ever displace CUDA because there are things other than machine learning models that GPUs are good at accelerating.

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Yeah, the amount of tunnel vision from AI/ML users thinking that Nvidia exists solely for their use is funny to watch. Try writing anything other than ML in pytorch. You can't? You can in CUDA. There's a much bigger world than ML out there.

> Try writing anything other than ML in pytorch. You can't?

Of course you can! It's a library of vectorized math operations. You don't need to do gradient descent on the graph either.

I tried that, tensorflow is actually better for general purpose compute, jax a lot better. Pytorch seems to omit most of the non-ml basic building blocks, while tf kind of gives you most of the XLA api.

Nvidia's stock price isn't at an all time price because of all the people writing fluid dynamics in CUDA.

Nor is it because of all the tensorflow models people are writing, to be honest.

Of course it's all of the mining, but that's not using pytorch either. It's using CUDA

GPU mining went waaay down since Ethereum went POS (Proof of Stake) almost 2 years ago. Does BTC even use GPU's for mining? I am pretty sure they use ASICS.

What is being mined using CUDA?

And similarly from people who consider Nvidia to be the "Gaming GPU" company, not understanding why it's so big now.

Pytorch and Jax have a numpy like API, so you can use PyTorch for other things too.

It was an example.

> With AMD, Intel, Tenstorrent, and several other AI chip specialists coming with pytorch compatibility, NVIDIA will eventually have to adapt.

I don't see how Nvidia has to do anything since PyTorch works just fine on their GPUs, thanks to CUDA. If anything, they're still one of the best platforms and that's definitely not because CUDA isn't competitive.

I hate stuff that only works on certain GPUs as much as the next person, but sadly competition has only really started to catch up to CUDA very recently.

> I don't see how Nvidia has to do anything since PyTorch works just fine on their GPUs

It was an example. The example was: competition from pytorch meant that tensorflow had to improve their DX to keep up.

That example was earlier in the comment and isn't what I quoted.

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