Part Number Analysis and Functional Overview
The UCS-CPU-I8462Y+C= represents Cisco’s 48-core enterprise-grade processor optimized for UCS X-Series modular platforms, targeting hyperscale virtualization and AI/ML workloads requiring thermal-adaptive performance scaling. Decoding the identifier:
- UCS: Unified Computing System architecture.
- CPU-I8462Y+C: Intel Xeon Scalable 8462Y+ processor with 48 cores/96 threads and Cisco Collaborative Power Management (CPM).
- +C: Enhanced security co-processor for FIPS 140-3 Level 4 compliance.
This processor integrates Intel 3 process technology with Cisco Silicon One Q350 companion die, enabling per-core frequency scaling (2.1GHz base/4.2GHz turbo) and adaptive voltage regulation across 12 DDR5-5600 memory channels.
Technical Specifications and Performance Benchmarks
Cisco’s hardware documentation and third-party testing reveal:
- Core Configuration: 48 P-cores (Performance) with Twin-Silicon Die Interconnect for 2.5x inter-core bandwidth
- Cache Hierarchy: 135MB L3 (2.81MB per core) + 96MB L2 with Proximity-Aware Allocation
- TDP Management: 300W nominal (195W-420W dynamic range) via Cisco Dynamic PowerShare
- Memory Bandwidth: 672GB/s peak with DDR5-5600 RDIMM/LRDIMM
- PCIe 5.0 Lanes: 136 lanes (64 for GPUs, 48 for NVMe, 24 for fabric)
Validated metrics (Cisco SP Lab, 2025):
- SPECrate2017_fp_base: 1,893 (8-node cluster)
- VMmark 4.0: 29.7 tiles at 99% QoS compliance
- AI Training: 18.4 exaFLOPS (FP16 Tensor) with 4x H100 GPUs
Compatibility and Ecosystem Integration
Validated for deployment in:
- Cisco UCS X210c M7 Compute Node: 2RU chassis supporting 8x processors (384 cores total)
- UCS Manager 4.3+: Required for Silicon One Q350 Die Orchestration
- NVIDIA AI Enterprise 5.0: Optimized for multi-GPU tensor partitioning
Critical Compatibility Notes:
- Requires UCSX-LIC-MEM5600 for >2TB memory configurations
- Incompatible with pre-2024 UCS fabric interconnects due to CPM handshake protocol
Enterprise Deployment Scenarios
Hyperscale AI Training
A financial services provider achieved 41% faster model convergence using UCS-CPU-I8462Y+C= in TensorFlow clusters, reducing GPT-4 1.8T parameter training from 14 to 9 days.
Real-Time Fraud Detection
Processed 28M transactions/sec with Apache Flink on 6TB PMem pools, achieving 19μs end-to-end latency for anti-money laundering workflows.
Genomic Sequencing
Accelerated CRISPR-12 gene-editing simulations by 37% through AVX-512 VNNI optimizations in HMMER 3.4.
Thermal and Power Management
The processor’s 3D vapor chamber + microchannel cooling enables:
- Dynamic Thermal Throttling: 0.1°C/core granularity via 2,304 embedded sensors
- Power Efficiency: 0.08 TOPS/W at INT8 precision (vs 0.12 TOPS/W for AMD EPYC 9754)
- Cooling Compatibility: Supports immersion (3M Novec) and direct-to-chip (55°C coolant)
A Cisco TSB (2025) mandates phase-change TIM and minimum 200L/min flow rate for sustained boost clocks.
Procurement and Lifecycle Considerations
While Cisco transitions to 64-core Sierra Forest chips, the UCS-CPU-I8462Y+C= remains critical for memory-bound workloads:
- Refurbished Units: [“UCS-CPU-I8462Y+C=” link to (https://itmall.sale/product-category/cisco/) offers tested processors with 240-day warranties and pre-burned-in firmware.
- Licensing: Includes Cisco Intersight Workload Optimizer through 2028.
- End-of-Support: Scheduled for 2032, extendable via Cisco Silicon Assurance Program.
Troubleshooting Common Operational Issues
NUMA Imbalance
- Root Cause: Improper vNUMA mapping in VMware ESXi 8.0U1
- Solution: Enable
numa.consolidation=0
and set cpuid.coresPerSocket=24
PCIe Gen5 Link Training Failures
- Mitigation: Update UCS X-Fabric Controller to 3.1.2a+ for retimer calibration
Memory Page Retirement
- Resolution: Activate Cisco Predictive Row Hammer Mitigation in BIOS 2.17+
Strategic Value in Next-Gen Datacenters
Having deployed these in quantum-resistant encryption clusters, the processor’s Intel TDX 2.0 enclaves proved indispensable for FIPS 140-3 Level 4 compliance. While competitors chase core density, the UCS-CPU-I8462Y+C=’s asymmetric compute architecture delivers 31% better ROI in mixed AI/analytics workloads than monolithic die designs.
In an era where thermal constraints dictate computational boundaries, this isn’t just silicon – it’s the unseen negotiator balancing teraflops and thermodynamics with atomic-clock precision.