GPU Networking

AI models are outgrowing network infrastructure because the hard part is no longer just training a model once. The hard part is repeatedly moving 140 GB to 1 TB artifacts, placing them on the right GPU nodes, and serving them with predictable latency during bursty inference demand. According to the CNCF Annual Cloud Native Survey (2026), 66% of organizations already use Kubernetes to host generative AI workloads, yet only 7% deploy models daily, which tells us the bottleneck is operational delivery, not lack of interest. ...

Meta is spending up to $135 billion on AI infrastructure in 2026 — the largest single-company technology investment in history — and the networking layer that ties it all together runs on Nvidia Spectrum-X Ethernet, not InfiniBand. This multiyear partnership covers millions of Nvidia Blackwell and next-generation Rubin GPUs, and the deliberate choice of Ethernet over InfiniBand sends a clear signal: the future of AI-scale networking is open, Ethernet-based, and built on the same fabric principles that CCIE-level engineers have been mastering for years. ...