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 Nvidia’s 70 percent.

For machine-learning work, though, Nvidia’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 Nvidia’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 day’s top tech stories in the InfoWorld Daily newsletter. ]
AMD has long had plans to fight back.
It’s been prepping hardware that can compete with Nividia on performance and price, but it’s also ginning up a platform for vendor-neutral GPU programming resources — 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

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