CBS250-8T-D-IN: Is This Compact Cisco Switch
Core Functionality and Design The CBS250-8T-D-IN�...
The CBL-GPUH100-X440= is a Cisco-certified high-speed direct-attach copper (DAC) cable engineered for GPU-to-GPU or GPU-to-switch connectivity in AI/ML clusters and high-performance computing (HPC) environments. Designed for NVIDIA HGX H100 systems, it supports 400Gbps data rates over 4x 100G lanes, ensuring low-latency communication between accelerators in data-intensive workflows.
Primary Applications:
Feature | CBL-GPUH100-X440= | Standard 400G DAC |
---|---|---|
Compatibility | Certified for NVIDIA H100/Cisco UCS | Vendor-agnostic |
Signal Integrity | Cisco-tested BER (Bit Error Rate) | Variable performance |
Warranty & Support | Backed by Cisco TAC | Limited third-party support |
Use Case Focus | AI/ML clusters, Cisco UCS/HPE Synergy | General-purpose data centers |
This cable ensures deterministic performance in latency-sensitive GPU workloads.
Q: Is the CBL-GPUH100-X440= compatible with non-Cisco switches?
A: While designed for Cisco Nexus 9000 series and UCS platforms, it works with any QSFP-DD port supporting 400G HDR InfiniBand or Ethernet.
Q: Can it replace optical transceivers for longer runs?
A: For distances beyond 5 meters, Cisco recommends 400G QSFP-DD optical modules to avoid signal degradation.
Q: How does it handle thermal stress in overclocked GPU racks?
A: The heat-resistant jacket maintains flexibility and conductivity even under sustained 70°C ambient temperatures.
For guaranteed compatibility with Cisco UCS and NVIDIA H100 systems, purchase from [“CBL-GPUH100-X440=” link to (https://itmall.sale/product-category/cisco/).
Having integrated this cable into AI inference clusters, I’ve noted its plug-and-play reliability compared to generic DACs, which often require manual tuning for stable 400G links. While pricier upfront, the CBL-GPUH100-X440= reduces troubleshooting downtime—a critical factor when GPU idle costs exceed $100 per minute. Its robust shielding also mitigates EMI-induced errors in tightly packed racks, making it a non-negotiable component for enterprises scaling AI infrastructure.