The NVIDIA DGX POD™ is an optimised data centre rack containing up to nine DGX-1 servers, twelve storage servers, and three networking switches to support single and multi-node AI model training and inference using NVIDIA AI software.The NVIDIA DGX POD™is also designed to be compatible with leading storage and networking technology providers.
Please contact your local sales for availability Long-term storage of raw data can be located on a wide variety of storage devices outside of the NVIDIA DGX POD, either on-premises or in public clouds.This reference architecture*, is based on a single 35 kW high-density rack to provide the most efficient use of costly data center floorspace and to simplify network cabling. Workload: ResNet-50, BS=256, 90 epochs to solution | CPU: dual Xeon Platinum 8180 | GPU: 8X NVIDIA Tesla V100 32GBRed Hat Enterprise Linux is supported on NVIDIA DGX Systems.High-performance training accelerates your productivity, which means faster time to insight and faster time to market.Learn About Enterprise-Grade Support for NVIDIA DGX SystemsEssential Instrument for AI ResearchNVIDIA DGX-1 lets you quickly uncover patterns in large data sets leading to new knowledge and insights in hours or minutes.Simplified workflows and improved team collaboration make you more productive immediately. Delivered as fully integrated, ready-to-deploy offerings through our NVIDIA Partner Network, these solutions make your data center AI deployments simpler and faster for ITDownload the reference architectures available from each of our partners.Generic data center company that has been around since 1997 to offer companies of all sizes the perfect place to house their data. DGX-1 servers feature 8 GPUs based on the Pascal or Volta daughter cards with HBM 2 memory, connected by an NVLink mesh network.. Ths system is designed to run a number of NVIDIA-optimized deep learning framework applications packaged in Docker containers. The most recent addition to the DGX POD ecosystem is the Dell EMC Isilon with NVIDIA DGX-1.
All are enjoying the greatest generational performance leap in eight generations of NVIDIA GPUs.Also helping meet the strong demand for A100 are the world’s leading cloud providers, such as Amazon Web Services, Baidu Cloud, Microsoft Azure and Tencent Cloud, as well as dozens of major server makers, including Dell Technologies, Hewlett Packard Enterprise, Inspur and Supermicro. DGX-1 is designed to save you from the typical deep learning setup costs, which can be hundreds of thousands of dollars in software engineering hours, and months of delays for the open source software to stabilize.The Industry Standard for AI at Scale MLPerf is a consortium of AI leaders from academia, research labs, and industry whose mission is to “build fair and useful benchmarks” that provide unbiased evaluations of training and inference performance for hardware, software, and services—all conducted under prescribed conditions.
For the presentation of the cube system, Nvidia called as purchase price around 400,000 US dollar before taxes.
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