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Hyperconverged Supercomputers For the Enterprise Data Center

Last month NVIDIA, our favorite GPU vendor, dived into the converged appliance space. In fact we might call their new NVIDIA DGX-1 a hyperconverged supercomputer in a 4U box. Designed to support the application of GPU's to Deep Learning (i.e. compute intensive deeply layered neural networks that need to train and run in operational timeframes over big data), this beast has 8 new Tesla P100 GPUs inside on an embedded NVLink mesh, pre-integrated with flash SSDs, decent memory, and an optimized container-hosting deep learning software stack. The best part? The price is surprisingly affordable, and can replace the 250+ server cluster you might otherwise need for effective Deep Learning.

Despite the obvious opportunities, enterprises face a lot of obstacles in putting machine learning (and esp. Deep Learning) into production processes. One of them is standing up and hosting HPC level infrastructure in a production oriented data center. The only realistic option has been going to the large cloud providers for this kind of infrastructure on an on-demand basis, but there are issues with data flow, data governance, and even cost over time. This optimized on-premise appliance brings HPC technology not only within reach of enterprises (and smaller Service Providers), but also far down market for companies for example wanting to fully exploit new streams of IoT data. And it makes Deep Learning itself practical. 

If you are a F1000 company you'd be remiss not to already have at least one of these on order, just to see what you can do with it. For everyone else, you might just find this thing fits within a solid business case based on the potential return for a new Deep Learning application.  And if you think Deep Learning is just for learning to recognize photos, drive cars and win at Go, I'm happy to help brainstorm ideas for your business!

  • Premiered: 05/12/16
  • Author: Mike Matchett
Topic(s): NVIDIA hyperconvergence Big Data HPC


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