Developer - Nvidia Corporation
nvidia.com/en-us/data-center/dgx-platform
Nvidia DGX on Wikipedia
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Developer - Nvidia Corporation
nvidia.com/en-us/data-center/dgx-platform
Nvidia DGX on Wikipedia
https://youtu.be/Bpb4T37sy8o
Announcing DGX Systems
Published on May 15, 2017
Quote:
NVIDIA DGX Systems are designed to give data scientists the most powerful tools for AI exploration-tools that go from your desk to the data center to the cloud.
https://youtu.be/1BZXJ6Hy_po
Nvidia DGX Station, world's most powerful desktop, a Supercomputer at the office
Published on Nov 28, 2017
Quote:
Nvidia DGX Station is the world’s first and fastest personal supercomputer for leading-edge AI development at Supercomputing developers desk, it has the computing capacity of four server racks in a desk-friendly package, using less than one twentieth the power. It’s the only personal supercomputer with four Nvidia Tesla V100 GPUs, next generation Nvidia NVLink, and new Tensor Core architecture. DGX Station delivers 3X the training performance of today’s fastest workstations, with 480 TFLOPS of water cooled performance (3X Faster Than the Fastest Workstations) and FP16 precision. It's designed to be whisper quiet at one tenth the noise of other deep learning workstations, it’s designed for easy experimentation at the office.
https://youtu.be/5x06avDdUgg
NVIDIA's largest ever GPU for Artificial Intelligence - DGX-2 512GB, 2 PetaFLOPS
Published on Mar 27, 2018
Quote:
Recorded: March 27th, 2018
The largest GPU ever built
NVIDIA DGX-2
2 PetaFLOPS
10kW
350lbs
Presented by CEO Jensen Huang at GTC 2018
https://youtu.be/OTOGw0BRqK0
Explore the world’s largest GPU: NVIDIA DGX-2
Published on Mar 28, 2018
Quote:
Learn how we’ve created the first 2 petaFLOPS deep learning system, using NVIDIA NVSwitch to combine the power of 16 V100 GPUs for 10X the deep learning performance.
https://youtu.be/oMqmgxnLuhk
The making of the NVIDIA DGX Station
Published on Jun 4, 2018
Quote:
What inspired our team to build the DGX Station? Get an inside look into how this deep learning workstation was designed for developers and researchers to effortlessly bring their deep-learning initiatives to the office. We packed this portable and whisper-quiet system with twice the performance of the most powerful workstations, so AI researchers can carry out their life’s work from their desks.
https://youtu.be/s6qQLoonalo
NVIDIA DGX Station personal AI supercomputer
May 25, 2020
Quote:
The personal supercomputer for leading-edge
Your data science team depends on computing performance to gain insights, and
innovate faster through the power of deep learning and data analytics. Until now,
AI supercomputing was confined to the data center, limiting the experimentation
needed to develop and test deep neural networks prior to training at scale. Now
there’s a solution, offering the power to experiment with deep learning while
bringing AI supercomputing performance within arm’s reach.
Groundbreaking AI, at Your Desk
Now you can get the computing capacity of 400 CPU's, in a workstation that
conveniently fits under your desk, drawing less than 1/20th the power. NVIDIA®
DGX Station™ delivers incredible deep learning and analytics performance,
designed for the office and whisper quiet with only 1/10th the noise of other
workstations. Data scientists and AI researchers can instantly boost their
productivity with a workstation that includes access to optimized deep learning
software and runs popular analytics software.
Get Started in Deep Learning, Faster
DGX Station breaks through the limitations of building your own deep learning
platform. You could spend a month or longer, procuring, integrating, and testing
hardware and software. Then additional expertise and effort are needed to optimize
frameworks, libraries, and drivers. That’s valuable time and money spent on
systems integration and software engineering that could be spent training and
experimenting.
NVIDIA DGX Station is designed to kickstart your AI initiative, with a streamlined
plug-in and power-up experience that can have you training deep neural networks
in just one day.