1) Support your graphics cards on linux using kernel drivers that you upstream. All of them. Not just a handful - all the ones you sell from say 18 months ago till today.
2) Make GPU acceleration actually work out of the box for pytorch and tensorflow. Not some special fork, patched version that you “maintain” on your website, the tip of the main branch for both of those libraries should just compile out of the box and give people gpu-accelerated ML.
This is table stakes but it blows my mind that they keep making press releases and promises like this that things are on the roadmap without doing thing one and unfucking the basic dev experience so people can actually use their GPUs for real work.
How it actually is: 1) Some cards work with rocm, some cards work with one of the other variations of BS libraries they have come up with over the years. Some cards work with amdgpu but many only work with proprietary kernel drivers which means if you don’t use precisely one of the distributions and kernel versions that they maintain you are sool.
2) Nothing whatsoever builds out of the box and when you get it to build almost nothing runs gpu accelerated. For me, pytorch requires a special downgrade, a python downgrade and a switch to a fork that AMD supposedly maintain although it doesn’t compile for me and when I managed to beat it into a shape where it compiled it wouldn’t run GPU accelerated even though games use the GPU just fine. I have a GPU that is supposedly current, so they are actively selling it, but can I use it? Can I bollocks. Ollama won’t talk to my GPU even though it supposedly works with ROCm. It only works with ROCm with some graphics cards. Tensorflow similar story when I last tried it although admittedly I didn’t try as hard as pytorch.
Just make your shit work so that people can use it. It really shouldn’t be that hard. The dev experience with NVidia is a million times better.
Because, there are tons of IP and trade secrets involved in driver development and optimization. Sometimes game related, sometimes for patching a rogue application which developers can't or don't fix, etc. etc.
GPU drivers are ought to be easy, but in reality, they are not. The open source drivers are "vanilla" drivers without all these case-dependent patching and optimization. Actually, they really work well out of the box for normal desktop applications. I don't think there are any cards which do (or will) not work with the open source kernel drivers as long as you use a sufficiently recent version.
...and you mention ROCm.
I'm not sure how ROCm's intellectual underpinnings are but, claiming lack of effort is a bit unfair to AMD. Yes, software was never their strong suit, but they're way better when compared to 20 years earlier. They have a proper open source driver which works, and a whole fleet of open source ROCm packages, which is very rigorously CI/CD tested by their maintainers now.
Do not forget that some of the world's most powerful supercomputers run on Instinct cards, and AMD is getting tons of experience from these big players. If you think the underpinnings of GPGPU libraries are easy, I can only say that the reality is very different. The simple things people do with PyTorch and other very high level libraries pull enormous tricks under the hood, and you're really pushing the boundaries of the hardware performance and capability-wise.
NVIDIA is not selling a tray full of switches and GPUs and require OEMs to integrate it as-is for no reason. On the other hand, the same NVIDIA acts very slowly to enable an open source ecosystem.
So, yes, AMD is not in an ideal position right now, but calling them incompetent doesn't help either.
P.S.: The company which fought for a completely open source HDMI 2.1 capable display driver is AMD, not NVIDIA.
janpmz•1h ago
Mountain_Skies•1h ago