AI and data intelligence vendor DDN today announced the NVIDIA AI Data Platform reference design, built in collaboration with NVIDIA to support organizations’ generative AI strategies by simplifying how unstructured data is stored, accessed ….
DDN and Nebius Cloud Collaborate on AI Infrastructure for Enterprise
April 30, 2025 by
CHATSWORTH, CA – April 30, 2025 – AI and data management company DDN has partnered with AI cloud provider Nebius for enterprise AI workloads. By integrating DDN Infinia and EXAScaler into its AI cloud, Nebius offers enterprises a solution tailored to the demands of next-generation AI, the companies said. Nebius said that as an AI […]
DDN Achieves Unprecedented Performance in MLPerf™ Benchmarking, Empowering Transformative AI Business Outcomes
September 30, 2024 by
DDN®, provider of the data intelligence platform, proudly announces a groundbreaking achievement in the MLPerf™ Storage Benchmark, setting new standards for performance and efficiency. DDN’s A3I™ (Accelerated Any-scale AI) systems demonstrated unmatched capabilities in multi-node configurations, solidifying its role as essential drivers for high-demand machine learning (ML) workloads and transformative business outcomes. “Our MLPerf results emphatically showcase DDN’s […]
DDN AI400X2 Turbo Appliance Accelerates Gen AI and Inference for Data Center and Cloud by 10x
March 20, 2024 by
DDN®, a global leader in artificial intelligence (AI) and multi-cloud data management solutions, announced the latest addition to its powerful A3I® solutions, the DDN AI400X2 Turbo. 30% more powerful than the AI400X2, the previous industry performance leader, the AI400X2 Turbo boasts faster performance and expanded connectivity options.
DDN Simplifies Enterprise Digital Transformation with New NVIDIA DGX BasePOD and DGX SuperPOD Reference Architectures
September 26, 2022 by
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.
insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3
October 1, 2019 by
Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.
insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2
September 24, 2019 by
Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.
insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads
September 17, 2019 by
Artificial Intelligence (AI) and Deep Learning (DL) represent some of the most demanding workloads in modern computing history as they present unique challenges to compute, storage and network resources. In this technology guide, insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads, we’ll see how traditional file storage technologies and protocols like NFS restrict AI workloads of data, thus reducing the performance of applications and impeding business innovation. A state-of-the-art AI-enabled data center should work to concurrently and efficiently service the entire spectrum of activities involved in DL workflows, including data ingest, data transformation, training, inference, and model evaluation.
insideAI News Guide to Optimized Storage for AI and Deep Learning Workloads
September 10, 2019 by Leave a Comment
This new technology guide from DDN shows how optimized storage has a unique opportunity to become much more than a siloed repository for the deluge of data constantly generated in today’s hyper-connected world, but rather a platform that shares and delivers data to create competitive business value. The intended audience for this important new technology guide includes enterprise thought leaders (CIOs, director level IT, etc.), along with data scientists and data engineers who are a seeking guidance in terms of infrastructure for AI and DL in terms of specialized hardware. The emphasis of the guide is “real world” applications, workloads, and present day challenges.
How to Get to the Data-Enabled Data Center
March 11, 2019 by
Despite their many promising benefits, advancements in Artificial Intelligence (AI) and Deep Learning (DL) are creating some of the most challenging workloads in modern computing history and put significant strain on the underlying I/O, storage, compute and network. An AI-enabled data center must be able to concurrently and efficiently service the entire spectrum of activities involved in the AI and DL process, including data ingest, training and inference.
