Dedicated Server Hosting
Technical Documentation: Dedicated Server Hosting Configuration (Platform Titan-D1)
This document provides a comprehensive technical overview of the standard Dedicated Server Hosting configuration, designated internally as the Platform Titan-D1. This configuration is optimized for high-throughput, low-latency enterprise workloads requiring guaranteed resource allocation and predictable performance profiles.
1. Hardware Specifications
The Titan-D1 platform is engineered around dual-socket, high-core-count server architecture, prioritizing I/O bandwidth and memory capacity over maximum clock speed in specific workloads. All components are enterprise-grade, selected for 24/7 operation under sustained load.
1.1 Central Processing Units (CPUs)
The system utilizes dual Intel Xeon Scalable processors (Cascade Lake/Ice Lake generation, depending on current procurement cycle, details below). Thermal Design Power (TDP) is managed to ensure sustained turbo clock speeds under heavy multi-threaded load.
| Parameter | Specification (Cascade Lake Baseline) | Specification (Ice Lake Refresh) |
|---|---|---|
| Processor Model | 2x Intel Xeon Gold 6248R | 2x Intel Xeon Gold 6342 |
| Core Count (Total) | 48 Cores (24 per socket) | 48 Cores (24 per socket) |
| Thread Count (Total) | 96 Threads | 96 Threads |
| Base Clock Frequency | 3.0 GHz | 2.8 GHz |
| Max Turbo Frequency (Single Thread) | Up to 3.9 GHz | Up to 3.5 GHz |
| L3 Cache (Total) | 71.5 MB (35.75 MB per socket) | 72 MB (36 MB per socket) |
| TDP (Per Socket) | 205W | 205W |
| Processor Interconnect | UPI Link Speed: 10.4 GT/s | UPI Link Speed: 11.2 GT/s |
The choice between generations often balances raw frequency (Cascade Lake) against Instruction Set Architecture (ISA) advancements and memory bandwidth improvements (Ice Lake). For most computational fluid dynamics (CFD) and compilation tasks, the higher cache capacity and UPI speed of the newer generation are preferred.
1.2 Random Access Memory (RAM)
The Titan-D1 supports up to 2TB of DDR4 ECC Registered DIMMs (RDIMMs). The standard deployed configuration emphasizes capacity and speed for memory-intensive applications like in-memory databases and large virtualization hosts.
| Parameter | Specification |
|---|---|
| Total Capacity (Standard Deployment) | 384 GiB |
| Module Type | DDR4 ECC RDIMM |
| Module Density | 16 x 32 GiB DIMMs |
| Speed Grade | 2933 MHz (JEDEC Standard for Gold series) or 3200 MHz (Ice Lake) |
| Configuration | Fully populated across 12 memory channels (6 per CPU) for optimal interleaving. |
| Memory Error Correction | ECC (Error-Correcting Code) mandatory. |
Note: Proper Memory Channel Interleaving is crucial for maximizing memory bandwidth utilization, especially when operating near the memory topology limits of the platform.
1.3 Storage Subsystem
The storage architecture is designed for a balance of high-speed transactional access and significant bulk storage capacity. It employs a tiered approach utilizing NVMe for primary operations and SATA SSDs for secondary, high-capacity logging or archival data.
1.3.1 Primary Boot and OS Volume
This volume is typically configured as a hardware RAID-1 array for redundancy and fast boot times.
| Parameter | Specification | |
|---|---|---|
| Drive Type | Enterprise NVMe U.2 SSDs | |
| Capacity (Total Raw) | 2 x 3.84 TB | |
| RAID Level | RAID 1 (Mirroring) | |
| Usable Capacity | 3.84 TB | |
| Controller | Broadcom MegaRAID SAS 9460-16i (or equivalent integrated NVMe controller) |
1.3.2 Data Volumes
For high-I/O workloads, such as database transaction logs or real-time analytics, additional NVMe drives are provisioned, often configured in RAID 10 for the best balance of redundancy and write performance.
| Parameter | Specification | |
|---|---|---|
| Drive Type | Enterprise NVMe PCIe Gen4 SSDs (if Ice Lake platform used) | |
| Quantity | 4 x 1.92 TB | |
| RAID Level | RAID 10 (Stripe of Mirrors) | |
| Usable Capacity | 3.84 TB (50% overhead) | |
| Theoretical I/O Operations Per Second (IOPS) | > 1,500,000 Read/Write (Mixed Queue Depth 1) |
1.4 Network Interface Controllers (NICs)
Network connectivity is a critical bottleneck in dedicated environments. The Titan-D1 employs dual, redundant, high-speed fabric interfaces.
| Parameter | Specification |
|---|---|
| Primary Interface (Data Plane) | 2 x 25 Gigabit Ethernet (25GbE) SFP28 |
| Secondary Interface (Management/Out-of-Band) | 1 x 1 Gigabit Ethernet (1GbE) RJ45 (Dedicated IPMI/BMC) |
| Offload Capabilities | Support for TCP Segmentation Offload (TSO), Large Send Offload (LSO), and RDMA (Remote Direct Memory Access) if configured with compatible InfiniBand adapters (optional). |
The 25GbE interfaces are typically bonded using Link Aggregation Control Protocol (LACP) or configured for active/passive failover, depending on the client's Virtual Local Area Network (VLAN) requirements.
1.5 Chassis and Power
The system resides in a 2U rack-mountable chassis, designed for high airflow density.
- **Chassis:** 2U Rackmount, Hot-Swappable Bays.
- **Power Supplies:** Dual Redundant (N+1) Platinum-rated 1600W PSU units.
- **Power Distribution:** Input must support dual independent Power Distribution Units (PDUs) for resilience against single power feed failure.
- **Cooling:** Optimized for front-to-back airflow, supporting ambient rack temperatures up to 35°C (95°F) while maintaining CPU junction temperatures below 90°C under sustained 90% utilization.
2. Performance Characteristics
The performance profile of the Titan-D1 is characterized by high parallel processing capability and exceptional I/O throughput, making it suitable for workloads that scale across many cores and require rapid data access.
2.1 CPU Performance Benchmarks
Performance is measured using industry-standard benchmarks focusing on sustained throughput rather than burst performance.
2.1.1 SPEC CPU 2017 Integer Rate
This benchmark measures sustained integer performance across all available cores.
| Configuration | Score | Notes |
|---|---|---|
| Titan-D1 (Cascade Lake) | ~1150 | Baseline reference score. |
| Titan-D1 (Ice Lake) | ~1280 | Higher score due to architectural improvements (e.g., AVX-512 enhancements). |
| High-Frequency Desktop CPU (8 Cores) | ~550 | Illustrates the advantage of core count over single-thread speed for this metric. |
2.1.2 Floating Point Performance (Linpack)
For scientific computing workloads, the Linpack benchmark is critical, heavily utilizing the FPU and Advanced Vector Extensions (AVX) capabilities.
The sustained GFLOPs performance is highly dependent on the thermal envelope, ensuring the CPUs remain within their all-core turbo limits (typically 3.1 GHz to 3.4 GHz sustained). We measure performance in TFLOPS (Tera Floating Point Operations Per Second).
- **Peak Theoretical Performance (FP64):** Approximately 5.5 TFLOPS (based on 48 cores * 2.8 GHz * 16 FLOPs/cycle/core @ AVX-512 utilization).
- **Sustained Performance (Observed):** Typically 70-80% of peak theoretical, achieving 3.85 to 4.4 TFLOPS under continuous load in optimized environments (e.g., using MPI frameworks).
2.2 Storage I/O Benchmarks
The storage subsystem is validated using FIO (Flexible I/O Tester) to simulate real-world database and file server loads.
2.2.1 Sequential Read/Write Throughput
This measures large block transfers, relevant for backup, media streaming, and large sequential data processing.
| Operation | Throughput (GB/s) | Notes |
|---|---|---|
| Sequential Read (Block Size 128K) | > 10.5 GB/s | Limited by PCIe lane saturation and RAID controller overhead. |
| Sequential Write (Block Size 128K) | > 8.0 GB/s | Write performance slightly reduced due to RAID 10 parity calculations. |
2.2.2 Random I/O Performance
Crucial for transactional databases (e.g., PostgreSQL, MySQL) and virtual machine disk operations.
| Operation | IOPS | Latency (Average) |
|---|---|---|
| Random Read (QD=32) | ~1,650,000 IOPS | < 150 microseconds (µs) |
| Random Write (QD=32) | ~1,400,000 IOPS | < 200 microseconds (µs) |
The latency figures are critical indicators of the quality of the Storage Area Network (SAN) equivalent provided by the local NVMe fabric. Low latency ensures fast transaction commits.
2.3 Network Latency and Throughput
Testing is performed using `iperf3` across the 25GbE fabric.
- **Throughput (TCP):** Sustained transfer rates of 23.5 Gbps between two Titan-D1 servers connected via a top-of-rack switch with >= 100Gbps uplink capacity.
- **Inter-Server Latency (Ping):** Typically 18 µs to 25 µs between servers on the same Top-of-Rack (ToR) switch, depending on the switch ASIC complexity (e.g., Broadcom Trident II vs. Tomahawk III). This low latency is essential for distributed computing frameworks like Apache Spark.
3. Recommended Use Cases
The Titan-D1 configuration is positioned as a high-density, high-performance compute node. Its resource profile is heavily skewed towards applications that benefit from high core counts, large memory footprints, and extremely fast local storage access.
3.1 High-Performance Computing (HPC) Workloads =
The combination of high core count (48c/96t) and fast memory bandwidth makes this ideal for tightly coupled scientific simulations.
- **Computational Fluid Dynamics (CFD):** Simulations requiring significant matrix operations benefit directly from the AVX-512 capability and high memory throughput.
- **Molecular Dynamics (MD):** Running complex force calculations where memory access patterns are large and sequential.
- **Finite Element Analysis (FEA):** Large-scale structural analysis where solving sparse linear systems dominates runtime.
3.2 Enterprise Database Hosting =
The storage subsystem is the primary differentiator here, allowing for massive transactional loads without I/O contention.
- **OLTP (Online Transaction Processing) Systems:** High-volume, small-block read/write operations, such as those found in large-scale e-commerce platforms or financial trading systems, thrive on the >1.5M random read IOPS capability.
- **In-Memory Databases (e.g., SAP HANA):** While the standard 384 GiB RAM is sufficient for many deployments, the platform supports upgrades up to 2TB, making it suitable for mid-to-large scale in-memory deployments.
3.3 Large-Scale Virtualization and Containerization =
When hosting a high density of virtual machines (VMs) or containers, resource isolation and predictable performance are paramount.
- **Hypervisor Host (e.g., VMware ESXi, KVM):** The dual-socket architecture provides excellent NUMA locality for guest operating systems. Hosting 30-50 lightweight VMs or 10-15 high-resource VMs is common.
- **Kubernetes Worker Nodes:** Used as powerful nodes for running CPU-intensive pods that require guaranteed CPU scheduling affinity (e.g., specialized machine learning serving pods).
3.4 Continuous Integration/Continuous Deployment (CI/CD) Pipelines =
For organizations with large codebases, compilation times can be a significant bottleneck.
- **Large Code Compilations:** The 96 threads allow for massive parallel compilation jobs (e.g., building Linux kernels or large C++ projects), drastically reducing build times compared to lower-core-count machines.
4. Comparison with Similar Configurations
To properly contextualize the Titan-D1, it must be compared against two common alternatives: the High-Frequency Workstation Equivalent (HFW) and the High-Density Compute Node (HDCN).
4.1 Comparison Matrix
| Feature | Titan-D1 (Dedicated Server) | High-Frequency Workstation (HFW - Single Socket) | High-Density Compute Node (HDCN - Lower Core Count) |
|---|---|---|---|
| CPU Configuration | 2x 24-Core Xeon (48 Total) | 1x 16-Core High-Clock Xeon W | |
| Max RAM Capacity | 2 TB (DDR4 ECC RDIMM) | 1 TB (DDR4 ECC UDIMM) | |
| Storage I/O (Random Read IOPS) | > 1.5 Million (NVMe RAID 10) | ~700,000 (Single NVMe PCIe Gen4) | |
| Network Fabric | 2x 25GbE Standard | 1x 10GbE Standard | |
| Redundancy (Power/Storage) | Full N+1 PSU, Hardware RAID | Single PSU, Software RAID often used | |
| Ideal Workload Focus | Balanced Compute/I/O/Memory | Single-threaded peak performance, light virtualization | |
| Cost Index (Relative) | 1.0 | 0.6 | 0.85 |
4.2 Analysis of Differences
- 48 Cores vs. High Clock Speed
The Titan-D1 (48 cores) excels in **scaling throughput**. An HFW configuration might achieve 4.5 GHz on 8 cores, resulting in superior performance for legacy applications or those that cannot utilize more than 8 threads effectively. However, when scaling to 48 threads, the Titan-D1's sustained throughput significantly surpasses the HFW, as the HFW often throttles its single CPU heavily under full load. The Titan-D1 leverages Non-Uniform Memory Access (NUMA) architecture effectively across its two sockets.
- Storage Performance Disparity
The most significant gap is in storage. The HFW relies on limited PCIe lanes connected to a single CPU, often bottlenecking the NVMe drives. The HDCN often uses SATA SSDs or fewer NVMe drives. The Titan-D1's dedicated RAID controller managing 6 high-speed NVMe drives in RAID 10 provides an I/O subsystem performance that is 2x to 5x greater than the alternatives, which is non-negotiable for high-transaction databases.
- Reliability and Uptime
Dedicated Server Hosting, as exemplified by the Titan-D1, mandates hardware redundancy (dual PSUs, ECC memory, hardware RAID). The HFW and often the HDCN utilize consumer-grade or workstation components that lack these features, leading to higher potential Mean Time Between Failures (MTBF) expectations.
5. Maintenance Considerations
While dedicated hosting shifts the physical maintenance burden to the provider, proper configuration and operational monitoring are essential for the client to maximize Service Level Agreement (SLA) adherence and performance stability.
5.1 Power Requirements and Density
The Titan-D1 is a power-hungry unit, especially under peak computation.
- **Power Draw:** Idle consumption is typically 350W. Peak sustained consumption can reach 1200W to 1400W (with all components running at high utilization).
- **Rack Density:** Due to the high TDP, careful consideration of rack power distribution and cooling capacity is necessary. A standard 42U rack populated solely with Titan-D1 units might require 15kW+ dedicated power feed capacity, demanding high-amperage Power Distribution Units (PDUs).
5.2 Thermal Management and Airflow
Server thermal management is a complex interplay between component TDP and ambient air temperature.
- **Ambient Temperature:** The environment must be maintained below the specified maximum inlet temperature (35°C or 95°F). Exceeding this forces the CPUs to downclock aggressively to prevent thermal throttling, immediately reducing performance below expected benchmarks.
- **Airflow Direction:** Strict adherence to front-to-back airflow cooling is mandatory. Obstructions in the cold aisle or hot aisle containment can lead to localized hot spots, stressing PSUs and memory modules, potentially leading to Uncorrectable Error (UECC) events.
5.3 Firmware and Driver Management
Maintaining optimal performance requires up-to-date firmware across the critical hardware layers.
- **BIOS/UEFI:** Updates often include critical microcode patches addressing security vulnerabilities (e.g., Spectre/Meltdown mitigation) and performance tuning for Intel Speed Select Technology (SST).
- **RAID Controller Firmware:** Crucial for NVMe stability and performance. Outdated firmware on the storage controller can lead to unexpected I/O latency spikes or data corruption under heavy queue depth loads.
- **BMC/IPMI:** The Baseboard Management Controller firmware must be current to ensure reliable remote management, power cycling, and sensor reporting via Redfish API or legacy IPMI commands.
5.4 Operating System Tuning
Effective utilization of the dual-socket architecture requires OS awareness of NUMA Topology.
- **NUMA Pinning:** For HPC applications, processes must be explicitly pinned to the CPU cores closest to the memory banks they primarily access (local memory access provides significantly lower latency than remote access across the UPI link). Tools like `numactl` are essential for this configuration.
- **I/O Scheduling:** For the storage array, schedulers like `mq-deadline` or `kyber` are often preferred over the default `cfq` for high-performance NVMe devices, though this is highly workload-dependent.
5.5 Disaster Recovery Preparedness
Although hardware redundancy is high, logical failures (software corruption, accidental deletion) require external planning.
- **Backup Strategy:** Given the high data ingestion rates possible (8 GB/s write throughput), backups must be staged to a network location capable of absorbing this rate, usually requiring a dedicated 10GbE or 25GbE backup network segment, or utilizing high-speed Storage Area Network (SAN) snapshots if available.
- **Recovery Time Objective (RTO):** Due to the complexity (RAID 10 rebuilds, large OS volumes), the RTO for complete bare-metal recovery is higher than for simple cloud instances. Pre-built OS images and configuration management scripts (e.g., Ansible) are necessary to meet aggressive RTO targets.
Conclusion
The Platform Titan-D1 Dedicated Server Hosting configuration delivers uncompromising performance suitable for mission-critical, resource-intensive applications. Its key strengths lie in its balanced high core count, massive RAM capacity, and superior, low-latency NVMe storage subsystem, positioning it as the premier choice for demanding HPC, large-scale database operations, and high-density virtualization environments where resource isolation and predictable performance SLAs are non-negotiable requirements. Continuous monitoring of thermal envelopes and firmware levels remains the client's responsibility to guarantee sustained peak performance.
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.* ⚠️