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    Home»Data Science»AI Data Center Workaround? Startups Pursue Networked Aggregation of Idle GPUs
    Data Science

    AI Data Center Workaround? Startups Pursue Networked Aggregation of Idle GPUs

    Team_AIBS NewsBy Team_AIBS NewsFebruary 21, 2025No Comments3 Mins Read
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    Issues related to GPU deployment – interminable order wait occasions, excessive costs and, significantly, dire want – are resulting in new GPU entry methods.

    An article in today’s Wall Street Journal, “Your Gaming PC Might Assist Prepare AI Fashions,” studies that underused GPUs “encourage startups to sew collectively digital ‘distributed’ networks to compete with AI information facilities.”

    The article cites various firm who’re “amongst a burgeoning group of founders who say they imagine success in AI lies find pockets of underused GPUs world wide and stitching them collectively in digital ‘distributed’ networks over the web,” said the Journal. “These chips could be anyplace—in a college lab or a hedge fund’s workplace or a gaming PC in a teen’s bed room. If it really works, the setup would enable AI builders to bypass the biggest tech firms and compete towards OpenAI or Google at far decrease price.”

    This recollects the Folding@Residence phenomenon (and comparable efforts) that grew to become broadly used quickly after the 2020 COVID-19 outbreak, wherein scientists accessed idle distributed computing assets, beginning with PCs and workstations that, in mixture, delivered HPC-class compute for illness analysis.

    One of many entrepreneurs cited within the article, Alex Cheema, co-founder of EXO Labs, said that organizations world wide have tens and a whole bunch of GPUs that always will not be getting used – akin to throughout non-business hours – that taken collectively have extra GPU compute energy than massive AI information facilities powered by a whole bunch of 1000’s of Nvidia GPUs.

    The article notes that up to now, digital networks of GPUs have been scaled solely to a couple hundred chips, and that many technical and enterprise obstacles exist. Amongst them: community latency, information safety, figuring out contributors of idle GPUs, and the chance averseness of builders of expensive AI fashions.

    Nonetheless, sidestepping present high-cost GPU enterprise fashions, be they on-premises, in a colo or within the cloud, will at all times be a focus for IT planners.

    The Journal quoted Paul Hainsworth, CEO of decentralized AI firm Berkeley Compute, who mentioned he’s working a method of investing in GPUs as a monetary asset that may be rented out. “I’m making an enormous guess that the massive tech firms are incorrect that the entire worth shall be accreted to a centralized place,” mentioned Hainsworth, whose dwelling web page makes this provide: “House owners buy GPUs that get put in and managed in skilled datacenter(s), incomes passive earnings via rental charges without having any technical experience.”





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