Rail-Optimized Networking for AI Training Workloads
Clos-based leaf-spine architectures have dominated data center networking for their scalability and integration capabilities, but large-scale AI training reveals their limitations.
MAIN POINTS
- Clos-based architectures offer predictable latency and horizontal scalability.
- Integration with BGP EVPN/VXLAN overlays is seamless in these designs.
- They remain suitable for most enterprise and cloud workloads.
- Large-scale AI training highlights the limitations of these architectures.
TAKEAWAYS
- Leaf-spine designs have been the default for a decade in data centers.
- These architectures are effective for typical enterprise and cloud tasks.
- New demands from AI training challenge existing network designs.
- Future network designs may need to adapt to AI-specific requirements.