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Nvidia Unveils Deep Learning Supercomputer

06 April 2016

Nvidia Corp. has launched what it claims to be the world’s first deep-learning supercomputer designed to up the stakes in developing artificial intelligence.

The Nvidia DGX-1 supercomputer delivers a throughput of 250 x 86 servers by combining a new generation of Nvidia GPU accelerators, deep-learning software and development tools. As a result, the supercomputer allows scientists and researchers to create a new class of intelligent machines that learn, see and perceive the world as humans do, Nvidia says.

The Nvidia DGX-1 supercomputer offers the performance of 250 CPU-based servers in one single box. Source: NvidiaThe Nvidia DGX-1 supercomputer offers the performance of 250 CPU-based servers in one single box. Source: NvidiaThe supercomputer is designed to drive a new kind of software created with massive amounts of data, which require considerably higher levels of computational performance. The Nvidia DGX-1 allows researchers to reduce time to train larger, more sophisticated deep neural networks, Nvidia says.

What’s Inside

The Nvidia DGX-1 deep-learning supercomputer is powered by Nvidia’s Tesla P100 GPUs, based on Nvidia’s new Pascal GPU architecture. These high-performance GPUs allow for the equivalent of 250 CPU-based servers, networking, cables and racks all inside one single box.

The supercomputer also includes four other features, including the NVLink high-speed interconnect for scalability; 16-nm FinFET fabrication technology for energy efficiency; Chip-on-Wafer-on-Substrate with HBM2 for big data workloads; and new half-precision instructions to deliver more than 21 teraflops of peak performance for deep learning.

Nvidia says these features allow the DGX-1 systems equipped with Tesla P100 GPUs to deliver more than 12x faster training than four-way Nvidia Maxwell architecture-based offerings Nvidia unveiled just one year ago. Nvidia has already sparked interest from Facebook’s AI Research division, Baidu and Microsoft Research.

To contact the author of this article, email Peter.Brown@ieeeglobalspec.com


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