Over the past decade, Clos-based leaf-spine architectures have become the default design for data center networking. They deliver predictable latency, horizontal scalability, and clean integration with BGP EVPN/VXLAN overlays. For most enterprise and cloud workloads, they’re still the right architecture.
But large-scale AI training, particularly distributed training of large language models, has exposed the limits of a purely general-purpose network design. This isn’t because Clos is fundamentally flawed, but because the assumptions Clos designs make about traffic distribution no longer hold for certain workloads.
What we’re seeing now is not a replacement of Clos, but a shift toward workload-aligned network design where topology, traffic patterns, and application behavior are tightly coupled. Rail-optimized architectures are one of the clearest recent examples of that shift.
Continue reading “Rail-Optimized Networking for AI Training Workloads”
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