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Why Purpose-Built Infrastructure is the Best Option for Scaling AI Model Development

CIO Business Intelligence

“In an early phase, you might submit a job to the cloud where a training run would execute and the AI model would converge quickly,” says Tony Paikeday, senior director of AI systems at NVIDIA. Developers find that a training job now takes many hours or even days, and in the case of some language models, it could take many weeks.

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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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6 key considerations for selecting an AI systems vendor

CIO Business Intelligence

IT leaders attending NVIDIA’s GTC 2024 AI developer conference on March 18-21, 2024, in San Jose, CA, can explore these capabilities with ASUS, one of the global leaders in high-performance AI servers based on NVIDIA’s MGX server reference architecture. Maximize data storage AI workloads demand vast amounts of data.

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Use cloud computing to accelerate GenAI adoption in healthcare with Microsoft AI

CIO Business Intelligence

Scalability: Computational power and massive data storage are critical for training and hosting GenAI large language models. Healthcare organizations can leverage large and diverse cloud data sets and pre-trained, state-of-the- art model catalogs, in turn simplifying GenAI deployment. Optimize resources. Competency enablement.

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IT staff shortages damage the bottom line: IDC report

CIO Business Intelligence

Training can be an answer — if done right The report shed light on two parallel causes: the IT talent shortage itself, and resistance from employees in being retrained for different tasks. But it does require specialized training. Smith and others advise developing a culture of learning as among the best ways to achieve this goal.

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Leveraging Microsoft AI: A game changer for manufacturing

CIO Business Intelligence

To deploy and optimize AI on the edge, manufacturing IT leaders should: Segment the plant/assets: Grouping the plant/assets appropriately ensures balanced edge architecture. It provides the ability to scale the architecture for future requirements and it keeps costs in check.

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The blueprint for success: Demystifying enterprise architecture

Dataconomy

What is enterprise architecture? This includes its business processes, technology infrastructure, and data architecture. This includes its business processes, technology infrastructure, and data architecture. This helps ensure that technology investments are focused on achieving business objectives.