Completely agree. Whenever use of AMD GPUs for ML comes up on HN I echo your points with the added personal experiences (PAIN) I've had trying to actually use

Completely agree. Whenever use of AMD GPUs for ML comes up on HN I echo your points with the added personal experiences (PAIN) I've had trying to actually use an AMD GPU for anything other than driving a display. In terms of the tech leadership at Nvidia, all you need to do is peek at the mind-blowing number of repos they have on Github - literally hundreds of component software pieces across every layer that at this point can do anything from drastic performance increases to completely unique (CUDA only of course) functionality. On HN especially the whole "proprietary driver on Linux desktop situation" has hurt Nvidia significantly in terms of hearts and minds. As I said, then you look at almost any other software provided by Nvidia and realize they're actually a huge champion and supporter of open source for just about every aspect of the ecosystem other than the driver itself - and they're working on open sourcing the driver as well. AMD occupies a weird space in GPU compute - there are massive HPC deployments of AMD GPUs. Presumably they only work because AMD is throwing a ton of essentially one-off support at them for deployment. On the other end you have their "support" for low to mid-range GPU compute. I say "support" with quotes because you realize very quickly it's absolutely pathetic to the point of useless and run back screaming to Nvidia/CUDA. I'm not an Nvidia fanboy but my opinion at this point is the hardware markups for Nvidia GPU essentially subsidize all of the incredible (largely open source) software and ecosystem support they provide. Yes, they engage in anti-competitive practices but please show me a large corporation that doesn't. Fact is Nvidia has invested a massive amount of resources for well over a decade to earn their dominance of GPU compute. Spending twice as much (somewhat factual but overblown popular opinion on HN) on Nvidia hardware becomes an obvious choice when you realize you're going to burn A TON of time (and still fail) trying to get AMD GPU hardware to actually do anything in ML.