"Blackstone has estimated that data centers in the US alone will need $1trn in investment by the end of the decade just to keep pace with demand. "Bigger and better AI requires compute - a lot of it," Mike Bushong, vice president of data center at Nokia, tells ITPro. "And, for every megawatt of data center capacity deployed today, networking is the second-largest budget item behind the AI systems themselves.""
"There's a good reason for this. Generative AI models rely on thousands of graphics processing units (GPUs) in a single data center to share information across nodes and racks in real-time. If GPUs are brain cells, then a network is the nervous system connecting them all by sending signals. Unlike traditional workloads, high-, low- fabrics - these are unified networks of switches - are critical for data to be sent along this nervous system in real-time."
AI-driven workloads require vastly more compute and networking capacity, with Blackstone estimating $1tn in US data center investment needed by decade-end. Generative AI depends on thousands of GPUs communicating in real time, making network performance and reliability critical to overall AI returns. Networking now represents one of the largest data center budget items behind AI systems themselves. Underinvesting in interconnect bandwidth can cause communication bottlenecks that idle expensive compute resources and produce diminishing returns. Key networking technologies include Ethernet, InfiniBand, NVLink, and Ultra Accelerator Link, with InfiniBand historically favored for low latency and high bandwidth.
Read at IT Pro
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