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Latest revision as of 21:21, 2 October 2025
Server Configuration Selection Guide: The High-Density Compute Node (HDCN-4000 Series)
This document provides a comprehensive technical overview and selection guide for the High-Density Compute Node (HDCN-4000 Series) server configuration. This configuration is specifically engineered for workloads requiring extreme core density, high-speed, low-latency memory access, and scalable NVMe storage, targeting modern virtualization, AI/ML inference, and large-scale database environments.
1. Hardware Specifications
The HDCN-4000 is built upon a 2U rackmount chassis, optimized for airflow and power efficiency within high-density data center racks. The core design emphasizes maximizing compute capabilities per watt and per rack unit (U).
1.1 Chassis and Platform
The foundation is a vendor-agnostic, specification-driven platform designed for maximum component density and serviceability.
| Parameter | Specification |
|---|---|
| Form Factor | 2U Rackmount |
| Motherboard Chipset | Dual-Socket Intel C741 / AMD SP5 (Configurable) |
| Maximum Power Delivery (PSU) | 2x 2400W Redundant (Titanium Efficiency) |
| Cooling Solution | High-Static Pressure Redundant Fans (N+1 Configuration) |
| Chassis Dimensions (H x W x D) | 87.9 mm x 448 mm x 998 mm |
| Maximum Thermal Design Power (TDP) Support | Up to 1200W total system power draw |
1.2 Central Processing Units (CPUs)
The HDCN-4000 is optimized for the latest generation of high-core-count processors, supporting dual-socket configurations to maximize parallel processing capabilities.
| Parameter | Specification (Recommended Configuration) |
|---|---|
| CPU Support | Dual Socket (e.g., Intel Xeon Scalable 4th Gen or newer) |
| Maximum Cores per Socket | 64 Cores (Total 128 Cores) |
| Base Clock Frequency | 2.4 GHz Minimum |
| Max Boost Frequency (Single Thread) | 4.0 GHz |
| L3 Cache (Total) | 128 MB per socket (256 MB Aggregate) |
| Thermal Design Power (TDP) per CPU | 350W |
| Interconnect (UPI/Infinity Fabric) Speed | 11.2 GT/s per link |
CPU Architecture significantly dictates performance profiles; the selection here favors throughput over absolute single-thread frequency.
1.3 Memory Subsystem (RAM)
Memory capacity and speed are critical for in-memory databases and large datasets. The HDCN-4000 supports high-density, high-speed DDR5 RDIMMs.
| Parameter | Specification |
|---|---|
| Memory Type | DDR5 Registered DIMM (RDIMM) ECC |
| Total DIMM Slots | 32 (16 per CPU socket) |
| Maximum Capacity (Per Slot) | 128 GB (Using 128GB LRDIMMs) |
| Maximum Total System Memory | 4096 GB (4 TB) |
| Memory Speed (Effective) | 4800 MT/s (JEDEC Standard for this configuration) |
| Memory Channels | 8 Channels per CPU (16 total) |
| Memory Bandwidth (Aggregate Theoretical Peak) | ~1.2 TB/s |
Support for ECC Memory is mandatory for enterprise stability. The high channel count ensures the CPUs are not memory-bandwidth bound under heavy load.
1.4 Storage Configuration
The configuration prioritizes high-speed, non-volatile storage access, utilizing the PCIe Gen 5 lanes for maximum NVMe throughput.
| Slot Type | Quantity | Interface | Total Capacity (Example) |
|---|---|---|---|
| Front Accessible NVMe Bays (U.2/M.2 Hybrid) | 16 | PCIe 5.0 x4 | 64 TB (Using 4TB U.2 drives) |
| Internal Boot/OS Drives (M.2) | 4 | PCIe 4.0 x4 | 8 TB (Redundant pair for OS, 2 spares) |
| SATA/SAS Drive Bays (Optional Secondary Storage) | 4 (Mixed Use) | SATA 6Gbps / SAS 12Gbps | 16 TB (If HDDs are deployed) |
The storage topology utilizes a dedicated NVMe-oF controller or direct PCIe attachment to maximize IOPS and minimize latency, crucial for transactional workloads.
1.5 Networking and I/O
I/O density is achieved via PCIe bifurcation and dedicated management controllers.
| Component | Specification |
|---|---|
| PCIe Slots (Total Count) | 8 (6 x PCIe 5.0 x16 physical, 2 x PCIe 5.0 x8 physical) |
| Baseboard Management Controller (BMC) | Dedicated IPMI 2.0 / Redfish Compatible |
| Onboard Network Interface (LOM) | 2x 10GbE (Management/Base Traffic) |
| Expansion Network Adapter Slots (Primary) | 3x Full Height, Full Length slots capable of 400GbE NICs |
| USB Ports | 4x USB 3.2 Gen 2 |
The extensive PCIe 5.0 lane availability (up to 128 lanes provided by the dual-socket platform) allows for significant expansion of high-bandwidth accelerators or specialized networking gear.
2. Performance Characteristics
The HDCN-4000 configuration yields exceptional performance across compute-bound and I/O-bound workloads due to its dense core count and high-speed fabric interconnects.
2.1 Compute Throughput Analysis
Performance is measured using standardized synthetic benchmarks that stress the CPU's floating-point and integer capabilities, particularly relevant for HPC and scientific simulations.
| Benchmark Suite | Metric | HDCN-4000 Result (Dual 64-Core, 4TB RAM) |
|---|---|---|
| SPECrate 2017 Integer | Base Rate Score | ~ 1,850 |
| SPECrate 2017 Floating Point | Base Rate Score | ~ 2,100 |
| Linpack (HPL) | Theoretical Peak GFLOPS (FP64) | ~ 12.5 TFLOPS (CPU Only) |
| Memory Bandwidth (Aggregate) | Read/Write Peak | > 1.2 TB/s |
The high SPECrate scores confirm the suitability for environments where many tasks run concurrently, such as large-scale containerized microservices or batch processing jobs.
2.2 Storage Latency and IOPS
The primary performance differentiator for this configuration is the storage subsystem. Utilizing direct PCIe 5.0 attachment for 16 NVMe drives provides massive bandwidth and low latency.
Testing Methodology: 128KB sequential read/write, 4KB random read/write using FIO against a 75% utilized RAID-0 array of 16x 3.84TB PCIe 5.0 U.2 drives.
| Workload Type | Queue Depth (QD) | Latency (99th Percentile Microseconds) | IOPS (Aggregate) |
|---|---|---|---|
| Sequential Read (128K) | 32 | 12 µs | 55 GB/s |
| Sequential Write (128K) | 32 | 15 µs | 48 GB/s |
| Random Read (4K) | 128 | 28 µs | 3.2 Million IOPS |
| Random Write (4K) | 128 | 35 µs | 2.8 Million IOPS |
These metrics demonstrate that the HDCN-4000 can sustain extremely high transactional loads without being bottlenecked by storage I/O, a common failure point in older server generations.
2.3 Virtualization Density
When hosting virtual machines (VMs), the high core count and ample memory capacity directly translate to high VM density.
Assuming a standard allocation profile of 8 vCPUs and 64 GB RAM per tenant VM: $$ \text{Maximum Tenant VMs} = \min \left( \frac{\text{Total Physical Cores}}{\text{vCPUs per VM}}, \frac{\text{Total RAM}}{\text{RAM per VM}} \right) $$ $$ \text{Maximum Tenant VMs} = \min \left( \frac{128}{8}, \frac{4096 \text{ GB}}{64 \text{ GB}} \right) = \min(16, 64) = 16 \text{ VMs} $$
While the calculation suggests 16 VMs, in production environments utilizing CPU oversubscription (typical for web servers or non-critical workloads), this density can often reach 32-40 VMs, provided the workload remains balanced and avoids peak contention on the shared Non-Uniform Memory Access (NUMA) nodes.
3. Recommended Use Cases
The HDCN-4000 configuration is purpose-built for environments demanding high transactional integrity, massive parallelism, and low-latency storage access.
3.1 Enterprise Databases (OLTP and OLAP)
The combination of high memory capacity (4TB) and extremely fast NVMe I/O makes this platform ideal for high-throughput Relational Databases (e.g., SQL Server, Oracle) and NoSQL stores (e.g., Cassandra, MongoDB) that rely on keeping hot datasets in memory.
- **OLTP (Online Transaction Processing):** The 2.8M Random Write IOPS capacity ensures rapid transaction commit rates, minimizing user wait times.
- **In-Memory Analytics:** The 4TB RAM capacity allows for substantial data warehousing cubes or large in-memory query processing sets to reside entirely on the server, bypassing slower disk access during analytics runs.
3.2 High-Performance Virtualization Hosts
For Tier-0 and Tier-1 workloads where VM density must be maximized without sacrificing performance isolation, the HDCN-4000 excels. It can host demanding workloads such as: 1. Virtual VDI broker servers. 2. High-traffic application servers consolidated onto a single platform. 3. Hypervisors running specialized hardware pass-through (e.g., for GPU acceleration or specialized NICs) due to the abundance of PCIe lanes.
3.3 AI/ML Inference and Edge Computing
While training large Deep Learning Models typically requires specialized GPU servers (like the HDGN-8000 series), the HDCN-4000 is perfectly suited for inference serving.
- **Model Serving:** The high core count efficiently handles the CPU-based preprocessing and post-processing required by inference pipelines.
- **Data Preprocessing:** Large ETL jobs required to feed data streams to GPU accelerators benefit immensely from the 128 processing cores and fast local storage for temporary staging.
3.4 Large-Scale Caching Layers
For applications utilizing distributed caches like Redis or Memcached, the HDCN-4000 offers superior density. A single node can host several terabytes of cached key-value pairs, utilizing the fast memory channels to serve requests with sub-millisecond latency.
4. Comparison with Similar Configurations
To justify the premium associated with PCIe 5.0 components and high-density RAM, it is essential to compare the HDCN-4000 against two common alternatives: the High-Density Storage (HDS-2000) and the GPU-Optimized Compute (GOC-6000) configurations.
4.1 Comparative Analysis Table
| Feature | HDCN-4000 (High-Density Compute) | HDS-2000 (High-Density Storage) | GOC-6000 (GPU Optimized) |
|---|---|---|---|
| Chassis Size | 2U | 4U | |
| Max Cores (Aggregate) | 128 | 96 (Lower TDP CPUs) | |
| Max RAM | 4 TB DDR5 | 2 TB DDR4 | |
| Primary Storage Interface | PCIe 5.0 NVMe (16 Bays) | PCIe 4.0 SATA/SAS (48 Bays) | |
| Max PCIe Lanes for Expansion | ~128 (Gen 5) | ~80 (Gen 4) | |
| Primary Workload Focus | Compute, Low-Latency I/O, Virtualization | Bulk Storage, Archival, Scale-out File Systems | |
| Peak Storage IOPS (4K Random) | ~3.2 Million | ~1.5 Million (Slower backend) | |
| GPU Support | 2x Full Height (Limited by power/airflow) | None (Dedicated to storage controllers) | 4-8x Double-Width PCIe 5.0 |
4.2 Decision Rationale
- **Versus HDS-2000:** If the primary bottleneck is disk I/O latency or the need to store massive amounts of hot, active data (e.g., transactional logs), the HDCN-4000's NVMe performance justifies its selection, despite the HDS-2000 offering more raw drive bays for cold storage. The HDCN-4000 is compute-first; HDS-2000 is storage-first. SAN integration often favors the compute node for faster access.
- **Versus GOC-6000:** The GOC-6000 is specialized for parallel processing acceleration via GPUs. If the workload is heavily reliant on CUDA/ROCm libraries (e.g., model training, complex fluid dynamics), the GOC-6000 is superior. The HDCN-4000 is preferred for general-purpose, high-core-count tasks that do not scale effectively onto GPU architectures, such as database coordination or hypervisor management.
5. Maintenance Considerations
Deploying a high-density, high-power configuration like the HDCN-4000 requires rigorous attention to environmental controls and serviceability protocols.
5.1 Power Requirements and Density
The system's peak operational power draw can reach 2000W under full load (CPUs at 350W TDP each, plus 16 NVMe drives, and memory).
- **Rack Power Density:** A standard 42U rack populated entirely with HDCN-4000 units (assuming 42 units, 1.5U footprint per server) results in a sustained power draw of approximately $42 \times 1.8 \text{ kW} \approx 75.6 \text{ kW}$ per rack. This necessitates high-amperage Power Distribution Units (PDUs) and A/B power feeds rated for at least 80A per rack cabinet in 208V/240V environments. PDU selection must account for the 2400W redundant PSUs.
- **Thermal Output:** The significant power consumption translates directly into high heat output ($\approx 75 \text{ kW}$ of heat rejection). Data center cooling infrastructure must be provisioned to handle this thermal load effectively, often requiring closer spacing or higher flow rates for the hot/cold aisle containment strategy.
5.2 Thermal Management and Airflow
The 2U form factor forces tight component spacing, making airflow critical.
- **Fan Configuration:** The system relies on high-static pressure, variable-speed fans. Monitoring the BMC fan speed readings is crucial. Any deviation leading to reduced RPMs under heavy load (e.g., BIOS setting changes or firmware issues) can rapidly lead to thermal throttling, especially on the high-TDP CPUs.
- **Component Placement:** Due to the dual-socket layout, ensuring proper airflow pathing over the CPU heatsinks, the PCIe riser cards, and the rear I/O area is non-negotiable. Improperly seated riser cards can obstruct airflow to the memory DIMMs closest to the chassis center.
5.3 Serviceability and Component Life Cycle
The high density impacts Mean Time To Repair (MTTR).
- **Hot-Swappable Components:** PSUs, cooling modules (fans), and all 16 front NVMe drives are hot-swappable. This minimizes downtime during component failure.
- **Memory Replacement:** Replacing or upgrading RAM requires accessing the motherboard, which usually involves sliding the chassis out of the rack and potentially removing the top cover. Due to the high channel count (16 DIMMs per CPU access area), careful labeling and tracking of memory placement relative to the NUMA topology are essential for restoring peak performance post-maintenance.
- **Firmware Management:** Due to the complexity of the PCIe 5.0 interconnects and memory controllers, maintaining the latest validated firmware for the BIOS, BMC, and RAID/Storage controllers is a continuous operational requirement to ensure stability and performance consistency. Firmware updates should be scheduled quarterly or immediately following critical security patches.
5.4 Licensing Implications
The high core count (128 physical cores) significantly impacts software licensing models based on physical or virtual cores (e.g., Oracle Database, Microsoft SQL Server Enterprise Edition). Administrators must budget appropriately for software costs, as this configuration pushes the limits of per-core licensing tiers. Licensing strategy should be finalized before hardware procurement.
--- This in-depth analysis confirms the HDCN-4000 series as a leading choice for extreme compute density requirements in modern enterprise infrastructure.
Intel-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Core i7-6700K/7700 Server | 64 GB DDR4, NVMe SSD 2 x 512 GB | CPU Benchmark: 8046 |
| Core i7-8700 Server | 64 GB DDR4, NVMe SSD 2x1 TB | CPU Benchmark: 13124 |
| Core i9-9900K Server | 128 GB DDR4, NVMe SSD 2 x 1 TB | CPU Benchmark: 49969 |
| Core i9-13900 Server (64GB) | 64 GB RAM, 2x2 TB NVMe SSD | |
| Core i9-13900 Server (128GB) | 128 GB RAM, 2x2 TB NVMe SSD | |
| Core i5-13500 Server (64GB) | 64 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Server (128GB) | 128 GB RAM, 2x500 GB NVMe SSD | |
| Core i5-13500 Workstation | 64 GB DDR5 RAM, 2 NVMe SSD, NVIDIA RTX 4000 |
AMD-Based Server Configurations
| Configuration | Specifications | Benchmark |
|---|---|---|
| Ryzen 5 3600 Server | 64 GB RAM, 2x480 GB NVMe | CPU Benchmark: 17849 |
| Ryzen 7 7700 Server | 64 GB DDR5 RAM, 2x1 TB NVMe | CPU Benchmark: 35224 |
| Ryzen 9 5950X Server | 128 GB RAM, 2x4 TB NVMe | CPU Benchmark: 46045 |
| Ryzen 9 7950X Server | 128 GB DDR5 ECC, 2x2 TB NVMe | CPU Benchmark: 63561 |
| EPYC 7502P Server (128GB/1TB) | 128 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/2TB) | 128 GB RAM, 2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (128GB/4TB) | 128 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/1TB) | 256 GB RAM, 1 TB NVMe | CPU Benchmark: 48021 |
| EPYC 7502P Server (256GB/4TB) | 256 GB RAM, 2x2 TB NVMe | CPU Benchmark: 48021 |
| EPYC 9454P Server | 256 GB RAM, 2x2 TB NVMe |
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⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️