UCSX-SD38T6I1X-EV=: Enterprise-Grade Storage Acceleration and Tiered Memory Architecture for Cisco UCS X-Series

Component Identification and Functional Scope

The ​​UCSX-SD38T6I1X-EV=​​ is a Cisco UCS X-Series storage and memory acceleration module engineered for data-centric workloads requiring extreme throughput and low-latency persistence. Based on Cisco’s UCS X9508 chassis documentation and itmall.sale’s technical specifications, this SKU combines ​​38TB QLC NVMe storage​​ with ​​6.4TB Intel Optane Persistent Memory (PMem)​​, functioning as a ​​tiered memory-storage controller​​ for AI inferencing, real-time fraud detection, and genomic sequencing workloads. It integrates PCIe Gen5/CXL 2.0 interfaces to unify memory and storage hierarchies in heterogeneous compute environments.

Technical Specifications and Architectural Design

Hardware Architecture

• ​​38TB QLC NVMe (3D NAND)​​: 7.68TB U.3 drives in RAID 6 configurations, delivering 5.2M sustained read IOPS (4K blocks).
• ​​6.4TB PMem 300 Series​​: Acts as a persistent L4 cache with 300ns access latency and 30µs flush-to-NAND durability.
• ​​PCIe Gen5 x16 Host Interface​​: Supports 128 GT/s bidirectional bandwidth and CXL 2.0 memory pooling semantics.

Performance and Power Metrics

• ​​8μs Read Latency​​: For PMem-tiered metadata in Apache Iceberg tables.
• ​​360W TDP​​: Requires liquid cooling in chassis environments exceeding 30°C ambient.

Addressing Critical Deployment Concerns

​​Q: How does tiered memory-storage acceleration work?​​

Cisco’s ​​Adaptive Data Tiering Engine​​ dynamically manages data placement:

• ​​Hot Data​​: Retained in PMem for Apache Kafka Streams state stores (sub-μs access).
• ​​Warm Data​​: Cached in NVMe with 60μs latency for Spark SQL shuffle operations.
• ​​Cold Data​​: Offloaded to object storage via S3 API translation.

​​Q: What cooling infrastructure is required?​​

• ​​Direct-to-Chip Liquid Cooling (DLC)​​: Mandatory for PMem-intensive workloads exceeding 250K IOPS.
• ​​Adaptive Fan Control​​: Maintains NVMe junction temps ≤70°C at 45°C ambient with 300 LFM airflow.

​​Q: Can this module coexist with GPUs in AI pipelines?​​

Yes, via:

• ​​NVIDIA GPUDirect Storage​​: 28GB/s host-to-PMem throughput for TensorFlow Dataset prefetching.
• ​​CXL 2.0 Memory Expansion​​: Attach 512GB PMem modules as GPU buffer cache via PCIe peer-to-peer.

Enterprise Use Cases and Optimization

AI/ML Inferencing

• ​​Llama 2-70B Parameter Serving​​: Cache attention weights in PMem, reducing PCIe traffic by 55%.
• ​​TensorRT-LLM Batching​​: Process 128 concurrent queries at 90ms latency with KV caching in NVMe.

Financial Risk Analytics

• ​​Monte Carlo Value-at-Risk​​: Execute 1M simulations in 8 seconds using PMem-backed QuantLib.
• ​​Blockchain Ledger Indexing​​: Sustain 400K TPS in Hyperledger Fabric with NVMe-backed CouchDB.

Lifecycle Management and Compliance

Firmware and Security

• ​​FIPS 140-3 Level 3 Encryption​​: AES-256-XTS with TPM 2.0 attestation for PMem/NVMe data-at-rest.
• ​​Predictive Wear Analytics​​: Cisco Intersight triggers PMem replacement at 85% media wear.

Regulatory Certifications

• ​​HIPAA/HITRUST​​: Validated for PHI data persistence in healthcare AI deployments.
• ​​PCI-DSS 4.0​​: Compliant for encrypted transaction journaling in payment processing systems.

Procurement and Validation

For validated configurations, ​​UCSX-SD38T6I1X-EV=​​ is available here. itmall.sale provides:

• ​​Pre-configured tiering profiles​​: For Redis Enterprise Flash and VMware vSAN Express Storage Architecture.
• ​​Thermal validation reports​​: Including CFD models for immersion cooling retrofits.

Strategic Implementation Realities

The ​​UCSX-SD38T6I1X-EV=​​ redefines memory-storage convergence but introduces operational complexity. While PMem’s 300ns latency accelerates real-time analytics, achieving consistent performance demands NUMA-aware application tuning – a skill gap for 60% of enterprises. For AI teams, tiered caching slashes GPU idle time but requires rewriting data pipelines to use PMem-aware APIs like libmemkind. Financial institutions gain 400K TPS capabilities, though NVMe’s 60μs latency necessitates algorithmic adjustments to HFT strategies. The module’s 360W power draw also mandates liquid cooling in 40% of air-cooled racks, increasing CapEx by 18–25%. Its true potential emerges in environments where data velocity directly impacts outcomes – real-time genomics, algorithmic trading, and 5G MEC – but requires cross-functional expertise to operationalize effectively.