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# Converged north-south networks: the critical path for AI success

**[Cisco](https://daily.dev/sources/cisco)** · 8 min read · 0 upvotes · 0 comments

## Summary

North-south network traffic—data ingestion, storage access, and inference serving—is increasingly a bottleneck for AI data center performance, not just east-west GPU fabric. Modern AI workloads generate bursty, high-bandwidth flows that can leave GPUs idle if the network can't keep up. A converged Ethernet fabric combining front-end and storage networks on high-density 400G/800G Cisco N9000 switches addresses this by unifying traffic paths and reducing cabling complexity. Cisco Silicon One ASICs paired with NX-OS deliver dynamic load balancing (DLB) in both per-packet and flowlet modes, adapting in real time to congestion and traffic type. QoS policies ensure inference, training, and storage traffic each receive appropriate treatment. Cisco Nexus Dashboard provides centralized provisioning, monitoring, and AI job observability across multi-fabric environments, while Cisco Validated Designs offer prescriptive blueprints for deploying these architectures.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://blogs.cisco.com/datacenter/converged-north-south-networks-the-critical-path-for-ai-success>

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Tags: [#gpu](https://daily.dev/tags/gpu), [#ai-infrastructure](https://daily.dev/tags/ai-infrastructure)

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