Nvidia tries to kill CUDA translation layers | Tom's Hardware
Nvidia tries to kill CUDA translation layers | Tom's Hardware
Translators in the crosshairs.
![Nvidia bans using translation layers for CUDA software — previously the prohibition was only listed in the online EULA, now included in installed files [Updated]](https://lemmy.ml/pictrs/image/6ffbf0eb-b115-4ea8-9849-429d02a5aa86.jpeg?format=webp&thumbnail=256)
Probably unenforceable, like so much of EULAs, but enough to deter the small guys who can't afford the lawyers to defend themselves. Bully tactics. Shame, because an open playing field would benefit everyone but nvidia, also a shame that AMD, who probably could defend themselves, dropped financial support for ZLUDA.
Also,
Translation layers are vital for preservation.
The malicious people in NVIDIA who are pushing for this are being very evil right now.
software licensing needs to die. This shit needs standardized licensing
Fuck you nvidia
Linus Torvalds
As AMD, Intel, Tenstorrent, and other companies develop better hardware, more software developers will be inclined to design for these platforms, and Nvidia's CUDA dominance could ease over time.
This seems a bit optimistic to me. CUDA is currently the de facto method of utilising a GPU's power efficiently. This makes them an easy choice for anyone with serious compute power needs. The other manufacturers are fighting an uphill battle trying to create an alternative that won't be used until it is definitively better.
This just seems like a catch 22 to me
It's not "optimistic", it's actually happening. Don't forget that GPU compute is a pretty vast field, and not every field/application has a hard-coded dependency on CUDA/nVidia.
For instance, both TensorFlow and PyTorch work fine with ROCm 6.0+ now, and this enables a lot of ML tasks such as running LLMs like Llama2. Stable Diffusion also works fine - I've tested 2.1 a while back and performance has been great on my Arch + 7800 XT setup. There's plenty more such examples where AMD is already a viable option. And don't forget ZLUDA too, which is being continuing to be improved.
I mean, look at this benchmark from Feb, that's not bad at all:
And ZLUDA has had many improvements since then, so this will only get better.
Of course, whether all this makes an actual dent in nVidia compute market share is a completely different story (thanks to enterprise $$$ + existing hw that's already out there), but the point is, at least for many people/projects - ROCm is already a viable alternative to CUDA for many scenarios. And this will only improve with time. Just within the last 6 months for instance there have been VAST improvements in both ROCm (like the 6.0 release) and compatibility with major projects (like PyTorch). 6.1 was released only a few weeks ago with improved SD performance, a new video decode component (rocDecode), much faster matrix calculations with the new EigenSolver etc. It's a very exiting space to be in to be honest.
So you'd have to be blind to not notice these rapid changes that's really happening. And yes, right now it's still very, very early days for AMD and they've got a lot of catching up to do, and there's a lot of scope for improvement too. But it's happening for sure, AMD + the community isn't sitting idle.
Unfortunately the article of the post directly contradicts your point about ZLUDA improving:
ZLUDA appears to be floundering now, with both AMD and Intel having passed on the opportunity to develop it further
Following the links and searching around, I found this: Andrzej "vosen" Janik, the lead dev, says in his FAQ:
What's the future of the project?
With neither Intel nor AMD interested, we've run out of GPU companies. I'm open though to any offers of that could move the project forward. Realistically, it's now abandoned and will only possibly receive updates to run workloads I am personally interested in (DLSS).
BRUH
