Not anymore. AMD/Mesa are already competitive enough, and are getting better fast. I was using Nvidia myself as well, until around the time Polaris cards came out, and Mesa was bumped to 17.x. That's when things really started to get good with AMD.
And in the overall integration sense, it's like night and day. Nvidia is never going to reach it with their blob approach. Seamless upgrades, proper framebuffer, proper KMS/DRM, no constant screen tearing, Wayland support, GALLIUM_HUD, you name it. I wouldn't go back to Nvidia now (and I do all my gaming on Linux).
There are compromises you can make, most of them easy. None of this is my problem as the maintainer of Sway.
So, in short, you're firing your Nvidia users? Sometimes it has to be done, but you'll have to be very clear that you don't want Nvidia users.
Passive-aggressive attacks in an indirectly linked blog post against the userbase and Nvidia themselves are not sufficient, you will have to just be explicit about it in your GitHub README.
Also, this article contains pretty forwardly agressive attacks, I think.
Upon thinking about this, it's kind of like listing dependencies. I think you should make it clear in the README. I don't think the blog post is relevant in this context, as people likely won't read your blog post before installing Sway (and how would they know to do so, anyway?).
Do you mean cuda as a language v/s the latest opencl spec as a language, or implementations ? No one other than Intel is even implementing and updating OpenCL really, and nvidia will always push CUDA over opencl for lock ins. Which is kind of the same thing as every other argument that you can make for nvidia.
That said, if I were Nvidia, and the post was an example of "cooperating" and "working" with the Wayland developers, I don't think I'd go out of my way to cooperate.
You can certainly make the swap and the sentence still makes sense and is factually correct, but the context matters.
>That said, if I were (the Wayland developers), and (witnessed how Nvidia is) "cooperating" and "working" with (us), I don't think I'd go out of my way to cooperate.
Is also a valid point.
>nu-uh, I quit!
It worked great for me, and given Nvidia deliberately de-rate their support for OpenCL to push CUDA, I feel it's important to signal with your money that given the choice between flexible and open standards vs. proprietary, I'll support openness.
Why is that? What prevents you from using open APIs like Vulkan for GPGPU? Unless you rely on some CUDA only middleware of course.
Using anything but Nvidia for deep learning is a great idea if you want to get noticed for not using Nvidia for deep learning, but it wouldn't be the smart thing to do if you want to actually get something done.
CUDA is not as dominant as it was at first now that there are serious alternatives, but Nvidia works hard (both carrot and stick) to keep you locked in. And their hardware and software tools genuinely do have great performance, even if they should be better if Nvidia was a bit less... well evil honestly.