Why Research Computing Leaders Need to Rethink the Data Architecture Behind Modern HPC For decades, research computing organizations have built infrastructure to support demanding modeling, simulation, and scientific computing workloads.…
Solution Benefits Unify Active Data and Archive Data in a Global Namespace Many organizations already have massive archives of unstructured data stored on tape. However, these archives are often operationally…
Run traditional simulation, AI, and GPU workloads on a single high-performance data platform spanning node-local NVMe, SSD, and HDD storage – across any data center or cloud Benefits The Need…
The Critical Barrier: Timely Delivery of AI- Ready Data Over the past year, the attention of the AI market has moved from extreme scale training AI to using AI to…
Make at Scale Data AI-Ready, Fast and Safe, Across On-Premises and Cloud Benefits The Challenge: Unstructured Data Sprawl in the Age of AI Unstructured data is everywhere: across edge sites,…
Introduction Most organizations already have more storage than they realize. What they lack is a practical way to unify data stored across different storage types, apply consistent policies, and do…
Benefits AI feeds off data – massive amounts of unstructured data that is stored in multiple locations and must be efficiently and quickly moved to GPUs as needed to enable…
Benefits Turn GPU Server-Local NVMe Into a New Tier of Ultra High Performance Shared Storage AI training, checkpointing, inferencing, and agentic AI demand high throughput, low-latency access to large volumes…
Benefits Introduction AI initiatives increasingly depend on data distributed across data centers, storage platforms, and cloud environments. Preparing that data for AI training and inference often requires complex migration projects,…
