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Getting infrastructure right for generative AI

CIO

But while the payback promised by many genAI projects is nebulous, the costs of the infrastructure to run them is finite, and too often, unacceptably high. Infrastructure-intensive or not, generative AI is on the march. IDC research finds roughly half of worldwide genAI expenditures in 2024 will go toward digital infrastructure.

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Seekr finds the AI computing power it needs in Intel’s cloud

CIO

Seekr’s main business is building and training AIs that are transparent to enterprise and other users. We really began last year looking at what it would really take in terms of hardware to scale our business,” Clark says. “We We were looking for like-minded, leading-edge, AI-focused hardware at the same time.”

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Gen AI without the risks

CIO

On top of that, Gen AI, and the large language models (LLMs) that power it, are super-computing workloads that devour electricity.Estimates vary, but Dr. Sajjad Moazeni of the University of Washington calculates that training an LLM with 175 billion+ parameters takes a year’s worth of energy for 1,000 US households. Not at all.

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The AI revolution is here. How do you make sense of it?

CIO

AI should be trained within the parameters of our morality, beliefs, our laws, and humanity. We take the example of Siemens , which is digitally transforming healthcare facilities by introducing smart infrastructures to hospitals. This improves speed to innovate, saving significant amounts of time and cost.

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

CIO

“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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Private cloud makes its comeback, thanks to AI

CIO

Controlling escalating cloud and AI costs and preventing data leakage are the top reasons why enterprises are eying hybrid infrastructure as their target AI solution. Private clouds provide more predictability because the infrastructure is dedicated.” billion in 2024, and more than double by 2027. billion in 2024 and grow to $66.4

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The AI continuum

CIO

Retrain and fine-tune an existing model Retraining proprietary or open-source models on specific datasets creates smaller, more refined models that can produce accurate results with lower-cost cloud instances or local hardware. What’s the right infrastructure for AI? Pick the right AI for your needs. Use compute resources wisely.