From MIT's Technology Review:
The Pint-Sized Supercomputer That Companies Are Scrambling to Get
Dozens of organizations are shelling out $129,000 for a box that will help them train AI software.
Nvidia’s DGX-1 supercomputer is designed to train deep-learning models faster than conventional computing systems do.
To companies grappling with complex data projects powered by artificial intelligence, a system that Nvidia calls an “AI supercomputer in a box” is a welcome development.
Early customers of Nvidia’s DGX-1, which combines machine-learning software with eight of the chip maker’s highest-end graphics processing units (GPUs), say the system lets them train their analytical models faster, enables greater experimentation, and could facilitate breakthroughs in science, health care, and financial services.
Data scientists have been leveraging GPUs to accelerate deep learning—an AI technique that mimics the way human brains process data—since 2012, but many say that current computing systems limit their work. Faster computers such as the DGX-1 promise to make deep-learning algorithms more powerful and let data scientists run deep-learning models that previously weren’t possible.
The DGX-1 isn’t a magical solution for every company. It costs $129,000, more than systems that companies could assemble themselves from individual components. It also comes with a fixed amount of system memory and GPU cards. But because the relevant parts and programs are preinstalled in a metal enclosure about the size of a medium suitcase, and since it pairs advanced hardware with fast connectivity, Nvidia claims the DGX-1 is easier to set up and quicker at analyzing data than previous GPU systems. Moreover, the positive reception the DGX-1 has attracted in its first few months of availability suggests that similar all-in-one deep-learning systems could help organizations run more AI experiments and refine them more rapidly. Though the DGX-1 is the only system of its kind today, Nvidia’s manufacturing partners will release new versions of the supercomputer in early 2017.Previous mentions:
Fewer than 100 companies and organizations have bought DGX-1s since they started shipping in the fall, but early adopters say Nvidia’s claims about the system seem to hold up. Jackie Hunter, CEO of London-based BenevolentAI’s life sciences arm, BenevolentBio, says her data science team had models training on the system the same day it was installed. She says the team was able to develop several large-scale models designed to identify suitable molecules for drugs within eight weeks. These models train three to four times faster on the DGX-1 than on the startup’s other GPU systems, according to Hunter. “We had multiple models that originally took weeks to train, but we can now do this in days and hours instead,” she adds....MORE
NVIDIA Builds Its Very Own Supercomputer, Enters The Top500 List At #28 (NVDA)"Nvidia Welcomes Intel Into AI Era: Fancy a Benchmark Deathmatch?" (NVDA; INTC)
...To be clear, this isn't someone using NVDA's graphics processors to speed up their supercomputer as the Swiss did with the one they let CERN use and which is currently the eighth fastest in the world or the computer that's being built right now at Oak Ridge National Laboratory and is planned to be the fastest in the world (but may not make it, China's Sunway TaihuLight is very, very fast).
And this isn't the DIY supercomputer we highlighted back in May 2015:
...Among the fastest processors in the business are the one's originally developed for video games and known as Graphics Processing Units or GPU's. Since Nvidia released their Tesla hardware in 2008 hobbyists (and others) have used GPU's to build personal supercomputers.Nor is it the $130.000 supercomputer NVIDIA came up with for companies to get started in Deep Learning/AI.Here's Nvidias Build your Own page.
Or have your tech guy build one for you....
No, this is NVIDIA's very own supercomputer....
CERN Will Be Using NVIDIA Graphics Processors to Accelerate Their Supercomputer (NVDA)
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