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 AI400X2 Turbo Appliance Accelerates Gen AI and Inference for Data Center and Cloud by 10x
March 20, 2024 by Leave a Comment
DDN Simplifies Enterprise Digital Transformation with New NVIDIA DGX BasePOD and DGX SuperPOD Reference Architectures
September 26, 2022 by Leave a Comment
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.
insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 3
October 1, 2019 by Leave a Comment
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, insideBIGDATA 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.
insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads – Part 2
September 24, 2019 by Leave a Comment
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, insideBIGDATA 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.
insideBIGDATA Guide to Optimized Storage for AI and Deep Learning Workloads
September 17, 2019 by Leave a Comment
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, insideBIGDATA 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.
insideBIGDATA 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 Leave a Comment
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.
Five Data Platform Considerations When Thinking About Your Deep Learning Future
March 12, 2018 by Leave a Comment
With the current maturation of Artificial Intelligence applications and Deep Learning algorithms, many organizations are spinning up initiatives to figure out how they will extract competitive differentiation from their data. This guest article comes from DDN Storage, a provider of high performance, high capacity big data storage systems, processing solutions and services to data-intensive, global organizations.
Parallel Storage Solutions for Better Performance
August 24, 2017 by Leave a Comment
Using high performance parallel storage solutions, geologists and researchers can now incorporate larger data sets and execute more seismic and reservoir simulations faster than ever before, enabling higher fidelity geological analysis and significantly reduced exploration risk. To lean more download this white paper.
Parallel File System Delivers Better Strategies, Faster
August 24, 2017 by Leave a Comment
A parallel file system offers several advantages over a single direct attached file system. By using fast, scalable, external disk systems with massively parallel access to data, researchers can perform analysis against much larger datasets than they can by batching large datasets through memory. To Learn More about the Parallel File Systems download this guide
