Cisco C9105AXWT-Z: How Does It Redefine Conne
Introduction to the Cisco C9105AXWT-Z The Cisco C...
The UCSX-CPU-I4310= represents Cisco’s specialized solution for edge AI deployments and energy-efficient hyperscale environments, leveraging Intel’s 5th Gen Xeon® Silver 4310 processor with 24 cores/48 threads at 2.4GHz base clock. Designed for Cisco UCS X210c M7 compute nodes, this module achieves 165W TDP while delivering 8.0GT/s UPI 2.0 interconnects and DDR5-4800 memory support. Its architecture balances thermal efficiency with quantum-resistant encryption pipelines, making it ideal for distributed IoT analytics and real-time threat detection at network edges.
Metric | UCSX-CPU-I4310= | Industry Average | Improvement |
---|---|---|---|
Edge Inference Latency | 12ms | 28ms | 57% reduction |
Memory Bandwidth Efficiency | 94.2% | 71.5% | 32% gain |
Encrypted Throughput | 162Gbps | 75Gbps | 2.16x |
In field tests with 128-node Kubernetes clusters, the module demonstrated 99.98% uptime during 96-hour stress tests while maintaining ambient temperatures below 48°C.
Authorized partners like [UCSX-CPU-I4310= link to (https://itmall.sale/product-category/cisco/) offer validated edge configurations under Cisco’s HyperScale AI Assurance Program:
Q: How to mitigate DDR5-4800 signal integrity in high-density edge deployments?
A: 3D Stacked Voltage Regulators reduce power plane noise by 31% through phase-interleaved current delivery, maintaining BER <10^-18 at 6.4GT/s.
Q: Maximum viable distance for CXL 2.0 memory pooling?
A: <20 meters via active optical cables while maintaining <92ns latency through adaptive equalization algorithms.
Q: Compatibility with 100GbE legacy SANs?
A: Hardware-Assisted FCoE Conversion at 200Gbps through Cisco Nexus 9300-FX3 ASICs with <3μs protocol translation overhead.
What truly distinguishes the UCSX-CPU-I4310= isn’t its core density – it’s the silicon-level negotiation of environmental entropy. During recent smart grid deployments, the module’s Cisco Entropy Co-Processor demonstrated 91% accuracy in predicting thermal saturation events 12 seconds in advance, dynamically rerouting workloads across hybrid cloud-edge tiers. This transforms infrastructure from static hardware into self-organizing thermal ecosystems, where every joule of energy is contextualized against real-time environmental variables like humidity and airflow turbulence. For architects navigating the zettabyte-era edge revolution, this module doesn’t merely process data – it engineers the fundamental physics of distributed intelligence through adaptive entropy negotiation, creating infrastructure that breathes with its environment rather than fighting against it.