DDN®, a leader in artificial intelligence (AI) and multi-cloud data management solutions, announced its next generation of reference architectures for NVIDIA DGX™ BasePOD and NVIDIA DGX SuperPOD. These new AI-enabled data storage solutions enhance DDN’s position as the leader for enterprise digital transformation at scale, while simplifying by 10X the deployment and management of systems of all sizes, from proof of concept to production and expansion.
DDN deployed more than 2.5 exabytes of AI storage in 2021 and is now supporting thousands of NVIDIA DGX systems deployed around the world. DDN systems are performing in the most demanding production AI environments, from autonomous vehicles to finance, natural language processing, climate modeling, and drug discovery at massive scale. In addition, DDN has enabled smaller enterprise IT organizations to achieve significant success in their AI digital transformation initiatives.
“Our close technical and business collaboration with NVIDIA is enabling enterprises worldwide to maximize the performance of AI applications and simplify deployment for all,” said Dr. James Coomer, vice president of products, DDN. “With this next generation of reference architectures, which include DDN’s A3I AI400X2, we’re delivering significant value to customers, accelerating enterprise digital transformation programs, and providing ease of management for the most demanding data-intensive workloads.”
“Organizations modernizing their business with AI need flexible, easy-to-deploy infrastructure to address their enterprise AI challenges at any scale,” said Tony Paikeday, senior director of AI systems, NVIDIA. “Expanding on NVIDIA’s long collaboration with DDN, the next generation of DDN’s A3I built on NVIDIA DGX BasePOD provides customers with integrated AI solutions to power the success of their most innovative work.”
DDN’s A3I powered by NVIDIA DGX BasePOD is an evolution of what was previously known as NVIDIA DGX POD configurations. These new configurations increase flexibility and customer choice, while maintaining the users’ abilities to start deployments at small scales and grow their DGX cluster over time. Additionally, DDN is collaborating with NVIDIA on vertical-specific DGX BasePOD solutions tailored specifically to financial services, healthcare and life sciences, and natural language processing. Customers using these DGX BasePOD configurations will not only get integrated deployment and management, but also software tools including the NVIDIA AI Enterprise software suite, tuned for their specific applications in order to speed up developer success.
DDN’s A3I AI400X2 is an all-NVMe appliance designed to help customers extract the most value from their AI and analytics data sources, is proven in production at the largest scale and is the world’s most performant and efficient building block for AI infrastructures. Configurable as all-flash or hybrid, customers can build efficient scale-out AI data pools tuned to their exact performance and capacity needs. Recent enhancements to the EXAScaler Management Framework have cut deployment times for appliances from 8 minutes to under 50 seconds. This approach enables organizations to have better resource allocation predictions and allows for faster and simpler scaling, which lends to better return on investment (ROI).
The DGX BasePOD reference architectures provide customers with a proven formula for quickly acquiring, deploying and scaling AI infrastructure. With an environment optimized for AI workloads, organizations see faster time to insights and quicker ROI.
Standard configurations start as small as two NVIDIA DGX A100 systems and a single DDN AI400X2 system — and can be as large as hundreds of DGX systems and 10 AI400X2 systems. Organizations can enter at any size, and the extensible model lets them scale as needed with simple building blocks. Backed by DDN, the leader in AI data management, along with NVIDIA technology, extensive integration and performance testing, customers can rest assured that they will get the fastest path to AI innovation.
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