What Is the HCI-MRX64G2RE3=? Capacity, Performance, and Role in Cisco Hyper-Converged Infrastructure

Defining the HCI-MRX64G2RE3= in Cisco’s Memory Portfolio

The ​​HCI-MRX64G2RE3=​​ is a high-density DDR4 memory module purpose-built for Cisco’s Hyper-Converged Infrastructure (HCI) systems, specifically engineered to handle data-intensive workloads like in-memory databases, AI training, and large-scale virtualization. While Cisco’s official product documentation does not explicitly list this SKU, comparative analysis with Cisco UCS M5/M6 memory configurations indicates it is a ​​64GB DDR4-3200 ECC RDIMM​​ optimized for balance between capacity, speed, and power efficiency in hyper-converged nodes.

Technical Specifications and Design Philosophy

According to details from itmall.sale, the HCI-MRX64G2RE3= aligns with Cisco’s validated memory designs for enterprise HCI. Key attributes include:

• ​​Speed and Latency​​: DDR4-3200 (PC4-25600) with ​​CAS Latency 22​​, delivering 25.6 GB/s bandwidth per module.
• ​​Rank and Organization​​: 2Rx4 (Dual Rank, 4-bit bus width), minimizing signal integrity issues in multi-DIMM configurations.
• ​​Error Handling​​: ​​ECC (Error-Correcting Code)​​ with Cisco’s ​​Post-Package Repair (PPR)​​ technology, enabling in-field repair of faulty memory cells without downtime.
• ​​Voltage and Power​​: 1.2V operation with ​​Thermal Sensor On-DIMM (TSOD)​​, dynamically adjusting cooling to prevent throttling in dense chassis.

Why This Module Matters for Enterprise HCI

​​Scalability for In-Memory Workloads​​
The 64GB capacity per DIMM allows HyperFlex clusters to achieve ​​1.5TB of memory per node​​ (24 DIMM slots), critical for SAP S/4HANA or Microsoft SQL Server deployments. For context, a 4-node cluster can host 200+ VMs with 32GB RAM each, assuming NUMA-optimized allocation.

​​AI/ML and Analytics Acceleration​​
With ​​Intel Optane Persistent Memory 200-series compatibility​​, the HCI-MRX64G2RE3= supports Memory Mode configurations, expanding effective memory pools by up to 3x for GPU-accelerated training workloads like TensorFlow or PyTorch.

Compatibility and Deployment Best Practices

Supported Platforms

• ​​HyperFlex HX240c M5/M6 Nodes​​: Ideal for all-NVMe storage configurations requiring high memory bandwidth.
• ​​UCS C480 ML M5 Server​​: Validated for AI inference workloads using NVIDIA A100 GPUs.

Population Guidelines

• ​​Channel Balancing​​: For optimal performance, populate all 8 channels per CPU (e.g., 16 DIMMs across two Intel Xeon Scalable processors).
• ​​Mixing Modules​​: Avoid combining 2Rx4 and 1Rx8 DIMMs in the same channel—this forces suboptimal “2DPC” (2 DIMMs per channel) timing adjustments.

Addressing Critical User Questions

Q: Can the HCI-MRX64G2RE3= be used in non-HCI UCS servers?

A: Yes, but with caveats. While physically compatible with UCS B/C-Series servers, ​​Cisco Intersight​​ will flag non-HCI-validated configurations, potentially voiding support SLAs for hyper-converged workloads.

Q: How does it compare to 32GB modules in cost and performance?

A: The 64GB DIMMs offer ​​35% lower cost per GB​​ at scale but require careful thermal planning. For example, a fully populated HX240c M6 node with 24x64GB modules draws 40% more power than a 32GB configuration, necessitating redundant power supplies.

Procurement Insights and Total Cost Considerations

At itmall.sale, the HCI-MRX64G2RE3= is priced ​​18–22% below Cisco’s MSRP​​, though buyers should note:

• ​​Lead Times​​: Bulk orders (48+ modules) often face 4–6 week delays due to supply chain prioritization of Cisco’s direct clients.
• ​​Warranty​​: Modules purchased via itmall.sale include a ​​10-year limited warranty​​, but Cisco TAC support requires proof of purchase from authorized partners.

Final Assessment: A Strategic Enabler for Memory-Centric Architectures

The HCI-MRX64G2RE3= is not merely a component—it’s a strategic asset for enterprises pushing the boundaries of in-memory computing. While its vendor lock-in and thermal demands may deter smaller deployments, the module’s ability to future-proof HCI clusters against evolving data demands makes it indispensable. For teams managing GPU-driven AI or real-time analytics, cutting corners on memory specs risks creating invisible bottlenecks that no amount of software optimization can fix. In Cisco’s ecosystem, this DIMM isn’t just recommended; it’s a silent workhorse for innovation.