North-south traffic is becoming a gating factor for AI performance—moving data in and results out fast enough to keep GPUs highly utilized. See how a converged Ethernet fabric with traffic-aware load balancing and centralized operations can help your AI data center deliver predictable performance at scale.

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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.

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

Why north-south traffic now matters most for AI at scale

Understanding the AI cluster: a multi-network architecture

How north-south connectivity impacts GPU efficiency

Simplifying AI infrastructure with converged front-end and storage networks

Optimizing AI traffic with Cisco Silicon One and Cisco NX-OS

Scaling AI operations using Cisco Nexus One with Nexus Dashboard

Accelerating AI deployment with Cisco Validated Designs

Future-proofing your AI strategy with a resilient network foundation