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Gartner projects major IT spending increases for 2025

CIO

growth this year, with data center spending increasing by nearly 35% in 2024 in anticipation of generative AI infrastructure needs. Data center spending will increase again by 15.5% in 2025, but software spending — four times larger than the data center segment — will grow by 14% next year, to $1.24

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Pliops lands $100M for chips that accelerate analytics in data centers

TechCrunch

That’s why Uri Beitler launched Pliops , a startup developing what he calls “data processors” for enterprise and cloud data centers. “It became clear that today’s data needs are incompatible with yesterday’s data center architecture. Image Credits: Pliops.

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EnCharge AI emerges from stealth with $21.7M to develop AI accelerator hardware

TechCrunch

EnCharge AI , a company building hardware to accelerate AI processing at the edge , today emerged from stealth with $21.7 Speaking to TechCrunch via email, co-founder and CEO Naveen Verma said that the proceeds will be put toward hardware and software development as well as supporting new customer engagements.

Hardware 216
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Microsoft acquires Fungible, a maker of data processing units, to bolster Azure

TechCrunch

In December, reports suggested that Microsoft had acquired Fungible, a startup fabricating a type of data center hardware known as a data processing unit (DPU), for around $190 million. ” The Fungible team will join Microsoft’s data center infrastructure engineering teams, Bablani said. .

Azure 282
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The mainframe’s future in the age of AI

CIO

If there’s any doubt that mainframes will have a place in the AI future, many organizations running the hardware are already planning for it. Many organizations have their mission-critical data residing on mainframes, and it may make sense to run AI models where that data resides, Dyer says.

Survey 200
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The smart, strategic moves CIOs must make to leverage AI for business transformation

CIO

1] However, expanding AI within organizations comes with challenges, including high per-seat licensing costs, increased network loads from cloud-based services, environmental impacts from energy-intensive data centers, and the intrinsic difficulty of complex technology integrations. Fortunately, a solution is at hand.

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Navigating the future of national tech independence with sovereign AI

CIO

Core challenges for sovereign AI Resource constraints Developing and maintaining sovereign AI systems requires significant investments in infrastructure, including hardware (e.g., high-performance computing GPU), data centers, and energy. The user has full control of the input, output and the data that the model will train on.