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What Is NAS (Network Attached Storage)? Working, Features, and Use Cases

IT Toolbox

NAS refers to storage hardware connected to a local area network that lets all endpoints on the network access the files. The post What Is NAS (Network Attached Storage)? Working, Features, and Use Cases appeared first on Spiceworks.

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VAST Data allies with Nvidia on reference storage architecture for AI

Venture Beast

VAST Data collaborates with Nvidia to create reference architecture for attaching its storage systems to GPU servers. Read More.

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ASUS unveils powerful, cost-effective AI servers based on modular design

CIO Business Intelligence

That means hardware designed from the ground up for maximum performance, data center integration, AI development support, optimal cooling, and easy vertical and horizontal scaling. That architecture lets ASUS servers exploit the latest NVIDIA advances in GPUs, CPUs, NVME storage, and PCIe Gen5 interfaces.

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Mastering the art of storage automation for your enterprise

Dataconomy

Storage automation involves utilizing software and tools to automate storage tasks, which results in decreased manual labor and improved efficiency. In today’s era of data-driven business, managing storage infrastructure can be a complex and time-consuming process. What is storage automation?

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Datera, Fresh from Stealth, Moves Intent-Based Config to Data Storage

Data Center Knowledge

Data center architects familiar with software-defined infrastructure will recognize decoupling as referring to separating the resource requirements of software from the hardware that hosts it. Read More.

Storage 100
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The Future is Converged: How to Overcome Infrastructure Integration Challenges

Data Center Knowledge

Storage infrastructure will soon move to primarily pre-validated hardware and software systems, known as reference architectures, writes Radhika Krishnan of Nimble Storage. Industry Perspectives'

Storage 200
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Inferencing holds the clues to AI puzzles

CIO Business Intelligence

With the potential to incur high compute, storage, and data transfer fees running LLMs in a public cloud, the corporate datacenter has emerged as a sound option for controlling costs. Because LLMs consume significant computational resources as model parameters expand, consideration of where to allocate GenAI workloads is paramount.

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