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NVIDIA Announces the World’s Only Petascale Integrated AI Workgroup Server
November 19, 2020 News

 

NVIDIA announced the NVIDIA DGX Station™ A100 — the world’s only petascale workgroup server. The second generation of the groundbreaking AI system, DGX Station A100 accelerates demanding machine learning and data science workloads for teams working in corporate offices, research facilities, labs or home offices everywhere.

Delivering 2.5 petaflops of AI performance, DGX Station A100 is the only workgroup server with four of the latest NVIDIA® A100 Tensor Core GPUs fully interconnected with NVIDIA NVLink®, providing up to 320GB of GPU memory to speed breakthroughs in enterprise data science and AI.

DGX Station A100 is also the only workgroup server that supports NVIDIA’s multi-instance GPU (MIG) technology. With MIG, a single DGX Station A100 provides up to 28 separate GPU instances to run parallel jobs and support multiple users without impacting system performance.

“DGX Station A100 brings AI out of the data centre with a server-class system that can plug in anywhere,” said Charlie Boyle, vice president and general manager of DGX systems at NVIDIA. “Teams of data science and AI researchers can accelerate their work using the same software stack as NVIDIA DGX A100 systems, enabling them to easily scale from development to deployment.”

 

DGX Station Powers AI Innovation
Organisations around the world have adopted DGX Station to power AI and data science across industries such as education, financial services, government, healthcare and retail. These AI leaders include:

  • BMW Group Production is using NVIDIA DGX Station to explore insights faster as they develop and deploy AI models that improve operations.
  • DFKI, the German Research Center for Artificial Intelligence, is using DGX Station to build models that tackle critical challenges for society and industry, including computer vision systems that help emergency services respond rapidly to natural disasters.
  • Lockheed Martin is using DGX Station to develop AI models that use sensor data and service logs to predict the need for maintenance to improve manufacturing uptime, increase safety for workers, and reduce operational costs.
  • Pacific Northwest National Laboratory is using NVIDIA DGX Station to conduct federally funded research in support of national security. Focused on technological innovation in energy resiliency and national security, PNNL is a leading U.S. HPC centre for scientific discovery, energy resilience, chemistry, Earth science and data analytics.
  • NTT Docomo, Japan’s leading mobile operator with over 79 million subscribers, uses DGX Station to develop innovative AI-driven services such as its image recognition solution.

 

An AI Supercomputer Anywhere
While DGX Station A100 does not require data-centre-grade power or cooling, it is a server-class system that features the same remote management capabilities as NVIDIA DGX A100 data centre systems. System administrators can easily perform any management tasks over a remote connection when data scientists and researchers are working at home or in labs.

DGX Station A100 is available with four 80GB or 40GB NVIDIA A100 Tensor Core GPUs, providing options for data science and AI research teams to select a system according to their unique workloads and budgets.

DGX Station A100 is more than 4x faster than the previous generation DGX Station to power complex conversational AI models like BERT Large inference. It delivers nearly a 3x performance boost for BERT Large AI training.

 

Doubling Down on GPU Memory for AI, HPC
DGX A100 systems will be available with the new NVIDIA A100 80GB GPUs for advanced data centre workloads, doubling GPU memory capacity to 640GB per system to enable AI teams to boost accuracy with larger datasets and models.

The new NVIDIA DGX A100, 640GB systems, can also be integrated into the NVIDIA DGX SuperPOD™ Solution for Enterprise, allowing organisations to build, train and deploy massive AI models on turnkey AI supercomputers available in units of 20 DGX A100 systems.

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