AMD isn&#x27;t even mentioned in this article, and I recently read that nobody uses AMD for machine learning. Is this only down to the proprietary Cuda having more momentum than OpenCL? I&#x27;ve used both, albeit not very much, and my impression was that Cuda was a bit easier to write but OpenCL didn&#x27;t have any deal breakers. I know AMD cards were the most popular for crypto currencies, so it&#x27;s not like they&#x27;re always the slowest. Granted, I only have experience with each company&#x27;s consumer cards, and I think Nvidia holds their GeForce cards back more than AMD does their Radeon cards concerning compute performance.<p>Why is Nvidia this dominant, and why are so many researchers using a proprietary API like Cuda when there&#x27;s open alternatives?<p>Related to this, are anybody using Intel&#x27;s Xeon Phi? Tianhe-2 [0], the worlds fastest supercomputer since June 2013 use them as co-processors, but I rarely if ever hear about them in other projects.<p>[0] <a href="https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tianhe-2" rel="nofollow">https:&#x2F;&#x2F;en.wikipedia.org&#x2F;wiki&#x2F;Tianhe-2</a>