Right now, the market for GPUs for use in machine learning is essentially a market of one: Nvidia.AMD, the only other major discrete GPU vendor of consequence, holds around 30 percent of the market for total GPU sales compared to Nvidiarsquo;s 70 percent.
For machine-learning work, though, Nvidiarsquo;s lead is near-total. Not just because all the major clouds with GPU support are overwhelmingly Nvidia-powered, but because the GPU middleware used in machine learning is by and large Nvidiarsquo;s own CUDA.[ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Get a digest of the dayrsquo;s top tech stories in the InfoWorld Daily newsletter. ]AMD has long had plans to fight back. Itrsquo;s been prepping hardware that can compete with Nividia on performance and price, but itrsquo;s also ginning up a platform for vendor-neutral GPU programming resourcesnbsp;— a way for developers to freely choose AMD when putting together a GPU-powered solution without worrying about software support.To read this article in full or to leave a comment, please click here