Two technology booms -- some people might call them frenzies -- are combining to turn a once-obscure type of microprocessor into a must-have but scarce commodity. Artificial-intelligence systems, made by companies ranging in size from Google to the Chinese startup Malong Technologies, rely heavily on a computer chip called a graphics processing unit, or GPU. The chips are also very useful in mining digital currencies like Ethereum, a bitcoin alternative riding the same wave of hype as its more famous cousin. With people and companies involved in the two surging tech niches buying up the same chips, GPUs have been in short supply over the past several months. Prices have increased by as much as 50 percent, according to some resellers and customers. "The chips are simply going out of stock," said Matt Scott, a technologist from the United States who founded Malong after leaving Microsoft's research lab in Beijing in 2014.

Pattern analytics, deep learning, and machine learning have fueled a rapid rise in interest in GPU computing, in addition to GPU computing applications in high performance computing (HPC) and cloud-based data analytics. As a high profile example, Facebook recently contributed its "Big Sur" design to the Open Compute Project (OCP), for use specifically in training neural networks and implementing artificial intelligence (AI) at scale. Facebook's announcement of Big Sur says "Big Sur was built with the Nvidia Tesla M40 in mind but is qualified to support a wide range of PCI-e cards," pointing out how pervasive Nvidia's Tesla platform has become for AI research. Big Sur is a 4U high chassis housing a two-processor (2P) board connected to a daughter card featuring eight full-height double-width PCIe Gen3 x16 300W accelerator card slots intended to house GPU or other PCIe-based compute accelerators. The processor board and daughter card are linked via one PCIe Gen3 x16 slot in the initial implementation.