
BVQ° UNLOCKS CISCO NEXUS LAN FOR AI-OPTIMIZED INFRASTRUCTURE

The demands on modern data centers are increasing – especially due to AI workloads that process massive amounts of data in real time. With the new BVQ° platform integration for Cisco Nexus LAN under NX-OS, a crucial step toward AI-readiness is achieved: Layer 2 network data becomes visible, analyzable and embedded into the overall infrastructure context.
TECHNICAL FOUNDATION
BVQ° captures and correlates MAC addresses, VLANs, port utilization, CPU load and transceiver statistics directly from Cisco Nexus switches. The data is read under NX-OS and visualized in real time. This enables precise evaluation of network paths, redundancies and load distribution – essential for AI scenarios.
DETAILED AI USE CASES
- Distributed Deep Learning
Training data is exchanged between GPU nodes via RDMA or NVMe over TCP.
Benefit: BVQ° detects whether interconnects are overloaded or if the topology has bottlenecks that limit training throughput. - Realtime Inference für Edge-AI
Applications such as video analysis or speech processing require guaranteed latency.
Benefit: BVQ° identifies critical paths and enables prioritization of inference traffic via VLAN tagging and QoS mechanisms. - AI-Based Storage-Tiering Data is dynamically moved between fast NVMe and capacity-optimized storage solutions.
Benefit: BVQ° shows whether the network paths between storage and compute deliver the required performance – and where optimization potential exists. - Data Lake Ingestion für ML-Pipelines
Large volumes of data must be efficiently ingested from distributed sources.
Benefit: BVQ° detects whether ports or switches are overloaded, whether MAC-based connections are correctly established and whether load distribution is functioning. - AI-Powered IT Automation
Benefit: Monitoring data from BVQ° can be integrated into AI-driven decision processes – for example, to automatically respond to network congestion or dynamically adjust routing strategies.
CONCLUSION
With this integration, BVQ° becomes the central instance for analyzing and optimizing AI-relevant networks – making it a key component for high-performance, resilient and future-proof data centers.