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Accelerating AI Innovation for Datacentric Enterprises and HPC Workloads at Any Scale

NVIDIA pioneered accelerated computing to tackle challenges no one else can solve. Their work in AI and digital twins is transforming the world’s largest industries and profoundly impacting society.

By consolidating the power of an entire data center into a single platform, NVIDIA has revolutionized how complex machine learning workflows and AI models are developed and deployed in an enterprise. This computing marvel also led to the creation of Selene, one of the world’s fastest computers in total performance, and commercially available system – the first of its kind, NVIDIA DGX SuperPOD™ Solution. NVIDIA seeks to reduce the complexity of increasingly diverse AI models, including conversational AI, recommender systems, computer vision workloads and autonomous vehicles.

View Success Story
Success Story
NVIDIA and DDN Collaborate to Solve the World’s Greatest Challenges
Press Release
Groundbreaking NVIDIA Eos AI Supercomputer
Reference Architecture
NVIDIA DGX H100 SuperPOD with DDN A³I Storage

The Challenge

To reduce the time-to-solution challenge, NVIDIA needs to be able to distribute large computational problems across hundreds of systems operating in parallel using an integrated set of compute, network and storage building blocks. This approach requires extremely fast storage performance to support intensive processing demands. High degrees of parallel access to small and large files are critical, along with seamless horizontal scaling to ensure incremental expansion.​

Solution

More than 300 all-flash and hybrid storage appliances
Seamless deployment and optimization for on-premises and in the cloud​
Simple appliance model with powerful 2U building block

Benefits

Simple linear scaling of performance, optimized for AI/ML workloads​​
Easy access to DDN technical team for expedited collaboration​
A globally connected team allows for faster development times ​

QUOTE

What’s needed is data center-scale computing, so AI models and datasets can be processed across many systems in parallel, enabling applications to train in hours instead of weeks.

Tony Paikeday Senior Director of Product Marketing at NVIDIA