Cisco's Leaf-Spine Architecture for AI Workloads

Modern AI workloads demand exceptional network performance, low latency, and high reliability. Cisco's leaf-spine architecture provides an ideal foundation for AI and machine learning infrastructure, enabling high-bandwidth, low-latency communication between GPU clusters and storage systems.

Leaf-Spine Network Topology for AI/ML Workloads

The following diagram illustrates a Cisco-based leaf-spine architecture optimized for AI and machine learning workloads:

Key Components

Spine Layer

Cisco Nexus 9316D-GX Switches: Provide high-density 400G connectivity, enabling ultra-low latency and high-bandwidth connectivity between leaf switches
Non-blocking Architecture: Ensures any-to-any connectivity across the data center fabric

Leaf Layer

Cisco Nexus 93180YC-FX Switches: Offer high-density 10/25G connectivity to servers with 100G uplinks to the spine
VXLAN EVPN: Enables scalable Layer 2 and Layer 3 connectivity across the fabric

AI Compute

Cisco UCS X-Series with NVIDIA GPUs: Provides the computational power needed for large AI models
RoCE (RDMA over Converged Ethernet): Enables high-performance, low-latency GPU-to-GPU communication

Storage

High-Performance Flash Storage: Provides high IOPS and low latency for AI training data
Scale-Out NAS: Offers high throughput for large datasets

Benefits for AI Workloads

Predictable Latency: Every device is the same distance (number of hops) from any other device
Linear Scalability: Add leaf switches as compute or storage needs grow
High Bandwidth: Non-blocking fabric ensures maximum throughput for GPU-to-GPU communication
Redundancy: Dual-homed connections provide path diversity and high availability

Design Considerations

Oversubscription Ratio: Typically designed with 2:1 or 3:1 oversubscription for AI workloads
Traffic Engineering: QoS policies to prioritize AI training traffic
Telemetry: Cisco Nexus Insights provides real-time visibility into network performance
Automation: Cisco DCNM/Nexus Dashboard enables automated fabric deployment and management

This architecture provides the foundation for a high-performance infrastructure capable of supporting the most demanding AI and machine learning workloads, from training to inference.