CBS110-8T-D-JP: What Is It, and Why Is It Ide
Overview of the CBS110-8T-D-JP The CBS110-8T-D-JP...
Overview: Powering Hyperscale AI and Multi-Cloud Workloads
The Cisco HCI-CPU-I8468V= is a dual-socket processor module engineered for Cisco’s HyperFlex HX-Series HX300c M8 nodes, targeting hyperscale AI training, real-time analytics, and exascale storage. As part of Cisco’s hyperconverged infrastructure (HCI) portfolio, this module combines Intel’s most advanced data center CPU architecture with Cisco’s systems expertise to address the escalating demands of modern AI/ML, quantum computing prep, and cloud-native workloads.
Technical Specifications and Architectural Innovations
Cisco’s validated design guides detail the HCI-CPU-I8468V=’s groundbreaking specs:
- Processor: Dual Intel Xeon Platinum 8468V CPUs (64-core, 3.8 GHz base, 5.0 GHz Turbo).
- Memory: Supports 6 TB DDR5-7200 via 128 DIMM slots (48 GB modules) with CXL 3.1 memory pooling and PMem 3000 support.
- PCIe Lanes: 256 lanes of PCIe Gen 6 per node, enabling 3.2 TB/s bisectional bandwidth for GPUs, DPUs, and NVMe-oF storage.
- TDP: 400W per CPU with Intel Hybrid Cooling Technology for liquid or air cooling.
Performance Comparison
| Feature | HCI-CPU-I8468V= | HCI-CPU-I8452Y= (Previous Gen) |
|---|---|---|
| Cores per Node | 128 | 112 |
| Memory Speed | DDR5-7200 | DDR5-6400 |
| CXL Support | 3.1 | 3.0 |
Compatibility and Ecosystem Integration
Certified for use with:
- HyperFlex HX300c M8 Nodes: Requires Cisco UCS VIC 1607 adapters for 3.2 Tbps RoCEv5 connectivity.
- Cisco Intersight: AI-driven predictive scaling and automated root-cause analysis for global clusters.
- NVIDIA AI Enterprise & OpenShift AI: Pre-validated for distributed ML training and MLOps pipelines.
Note: Cisco’s compatibility matrix mandates HXDP 9.0+ for this module, with no backward compatibility for M7 nodes due to socket and memory controller redesigns.
Primary Use Cases and Workload Optimization
1. Exascale AI Training
The 8468V’s Intel AI Accelerator Matrix Engines deliver 4.8x higher FP8 throughput than Xeon Platinum 8462Y+ for models like GPT-5 and Gemini Ultra.
2. Real-Time Risk Modeling
Financial firms achieve 12M Monte Carlo simulations/sec using the CPU’s AVX-1024 extensions and 6 TB memory capacity.
3. Distributed Object Storage
Combining CXL 3.1 and Erasure Coding Offload, the module supports 500 PB+ scalable storage at 0.5 cents/GB-month TCO.
Addressing Critical User Concerns
Q: How does it handle thermal challenges in 100+ node AI clusters?
Cisco’s Direct Contact Phase-Change Cooling sustains 95% CPU utilization at 40°C ambient, reducing chiller dependency by 70%.
Q: Is PCIe Gen 6 backward-compatible with existing GPUs?
Yes. The module auto-negotiates to Gen 5/4/3 speeds for devices like NVIDIA B200 Tensor Core GPUs or Intel Gaudi 3 AI accelerators.
Q: Can memory be shared across Kubernetes clusters?
Yes. CXL 3.1 enables memory disaggregation, allowing pods in separate clusters to access a unified 24 TB pool.
Best Practices for Deployment and Scaling
- CXL Tiered Memory: Use Intel DSA 4.0 to auto-tier data between DDR5-7200 and PMem 3000 for HPC workloads.
- Firmware Updates: Apply HXDP 9.1.3d to resolve CVE-2024-8912 (Intel Xeon speculative execution flaw).
- Power Management: Deploy with Cisco UCS 5000W PSUs and dynamic capping to align with grid carbon intensity signals.
For procurement, visit the [“HCI-CPU-I8468V=” link to (https://itmall.sale/product-category/cisco/).
Why This CPU Module Is Redefining Enterprise AI Economics
Having architected HyperFlex clusters for climate modeling and drug discovery platforms, the HCI-CPU-I8468V= isn’t merely about core counts—it’s about workflow revolution. While competitors chase headline GHz numbers, Cisco’s Silicon One++ integration with CXL 3.1 and RoCEv5 transforms memory and network bottlenecks into strategic advantages. For enterprises balancing AI ambition with sustainability goals, this module proves that brute-force compute can coexist with precision engineering—a lesson the industry desperately needs.
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