N9K-C9508-FM-E2=: How Does Cisco’s High-Cap
N9K-C9508-FM-E2= Overview: Scaling Hyperscale Net...
The DS-C9124V-8IK9= is an 8-port activation license for Cisco MDS 9124 series multilayer fabric switches, designed to enable scalable 4/8/16/32G Fibre Channel connectivity in storage area networks (SANs). This license allows incremental expansion from base configurations to 24 fully operational ports, supporting non-blocking 64Gbps switching capacity and <500ns latency for mission-critical storage workloads.
1. Adaptive Performance Architecture
2. Enterprise-Class Reliability
3. Advanced Diagnostics
Metric | Base Model (Unlicensed) | DS-C9124V-8IK9= Activated |
---|---|---|
Active Ports | 8 (fixed) | 24 ports (+200% density) |
Throughput Capacity | 32Gbps | 64Gbps non-blocking |
Latency | 750ns | <500ns (-33% reduction) |
Energy Efficiency | 85W/chassis | 28W/port optimized cooling |
Compliance | FC-PI-5 | FC-PI-7 with NPIV support |
Q: How does it integrate with Cisco UCS C4800 ML servers?
A: The switch operates as a fabric edge node, using Cisco DCNM to synchronize QoS policies across UCS domains. For VMware vSAN deployments, it maintains <2ms latency through FCoE gateway functionality.
Q: What maintenance is required for optimal performance?
A:
This activation license exemplifies Cisco’s pay-as-you-grow SAN strategy, reducing initial CAPEX by 40% compared to full-port chassis configurations. Its adaptive zoning capabilities automatically quarantine misconfigured storage arrays, critical for maintaining HIPAA-compliant healthcare data environments. For compatibility verification with EMC PowerStore or NetApp FAS systems, consult certified experts through the Cisco hardware marketplace.
The DS-C9124V-8IK9= redefines hyperconverged infrastructure economics, enabling seamless transition from 16G FC to 32G NVMe-oF architectures without hardware replacement. Organizations implementing AI/ML training clusters should prioritize this model for its 1:1 oversubscription ratio and Fabric Performance Impact Notification (FPIN) integration, which reduces storage-related training errors by 27% in TensorFlow environments.