Network Bandwidth Considerations
Network Bandwidth Considerations for High-Throughput Server Architectures
This technical document details the specifications, performance metrics, recommended deployments, and maintenance requirements for a server configuration specifically optimized for demanding network I/O workloads. The primary focus of this architecture is maximizing sustained throughput and minimizing latency across the network fabric.
1. Hardware Specifications
The following section outlines the precise physical and logical components constituting the High-Throughput Network Optimized (HTNO) server platform. This configuration prioritizes PCIe lane availability, high-speed interconnects, and robust CPU capabilities necessary to feed the network interface controllers (NICs).
1.1 System Platform and Chassis
The foundation of this system is a 2U rackmount chassis designed for high-density component integration and superior airflow management, critical for sustaining high-speed network operations.
| Parameter | Specification |
|---|---|
| Chassis Model | Dell PowerEdge R760 or equivalent high-density 2U platform |
| Form Factor | 2U Rackmount |
| Motherboard Chipset | Intel C741 or AMD SP3/SP5 equivalent (supporting multiple x16 PCIe Gen5 slots) |
| Power Supply Units (PSUs) | 2x 2000W Platinum Rated, Hot-Swappable (Redundant configuration) |
| Cooling Solution | High-Static Pressure Fan Modules (N+1 redundancy) |
| Ambient Operating Temperature | 18°C to 27°C (Optimized for sustained 90% load) |
1.2 Central Processing Units (CPUs)
The CPU selection is critical to ensure that computational overhead associated with packet processing (e.g., checksum offloads, kernel bypass operations) does not become a bottleneck for the network throughput. We utilize dual-socket configurations with high core counts and substantial L3 cache to manage large data streams effectively.
| Parameter | Specification (Primary) | Specification (Alternative) |
|---|---|---|
| CPU Model | Intel Xeon Platinum 8592+ (Sapphire Rapids) | AMD EPYC 9654 (Genoa) |
| Core Count (Total) | 2 x 60 Cores (120 Total) | 2 x 96 Cores (192 Total) |
| Base Clock Frequency | 2.5 GHz | 2.4 GHz |
| Max Turbo Frequency | Up to 4.0 GHz (Single Core) | Up to 3.7 GHz (Single Core) |
| L3 Cache Size | 2 x 112.5 MB | 2 x 384 MB |
| TDP (Per Socket) | 350W | 360W |
The high core count is necessary for thread synchronization management in high-concurrency network applications, while the substantial L3 cache minimizes memory access latency during data staging operations before transmission or after reception.
1.3 Memory Subsystem (RAM)
The memory configuration is designed for high bandwidth and low latency, utilizing the maximum supported channels per CPU socket to feed the processors quickly, thus preventing starvation of the network processing threads.
| Parameter | Specification | |
|---|---|---|
| Total Capacity | 1024 GB (1 TB) DDR5 ECC RDIMM | |
| Configuration | 16 DIMMs per CPU (32 Total) | |
| Speed / Data Rate | 4800 MT/s (Tuning dependent on specific BIOS profile) | |
| Channel Configuration | 8-Channel per CPU (Fully Populated) | |
| Latency Profile | Optimized for JEDEC standard timings (tCL 36-40) |
For applications leveraging RDMA or DPDK, memory configuration must ensure NUMA node alignment with the associated physical NICs to maximize NUMA locality.
1.4 Storage Subsystem
While the primary focus is network bandwidth, the local storage must be capable of feeding data to the network stack without introducing I/O bottlenecks. This configuration utilizes high-speed NVMe storage configured in a RAID 10 array for redundancy and sequential read/write performance.
| Component | Specification |
|---|---|
| Boot Drive | 2 x 960GB M.2 NVMe SSD (RAID 1) |
| Data/Cache Drives | 8 x 7.68TB U.2 NVMe SSDs |
| RAID Controller | Hardware RAID, supporting NVMe passthrough for specific workloads (e.g., Ceph, vSAN) |
| Aggregate Sequential Read (Theoretical) | >30 GB/s |
| Aggregate Sequential Write (Theoretical) | >25 GB/s |
1.5 Network Interface Controllers (NICs)
The network interface hardware is the most critical component for achieving high bandwidth targets. This configuration mandates dual, high-speed, low-latency adapters.
| Parameter | Specification |
|---|---|
| Primary Interface Type | Dual Port 200 Gigabit Ethernet (200GbE) Adapter |
| Adapter Model Example | NVIDIA ConnectX-7 or Intel Columbiaville (e.g., E810-XXV) |
| Physical Interface | QSFP-DD (Direct Attach Copper or Optical) |
| PCIe Interface Required | PCIe Gen5 x16 slot (Mandatory for full bandwidth utilization) |
| Offload Capabilities | TCP Segmentation Offload (TSO), Large Send Offload (LSO), Checksum Offload, VXLAN/Geneve Offload |
| Advanced Features | Support for RoCEv2 (RDMA over Converged Ethernet) and SR-IOV |
The utilization of PCIe Gen5 x16 provides a theoretical maximum bidirectional bandwidth of 128 GB/s (1024 Gbps) to the host CPU complex, which is significantly higher than the 400 Gbps aggregate network capacity, ensuring the PCIe bus is not the limiting factor. This is crucial for minimizing PCIe saturation.
2. Performance Characteristics
The performance of this HTNO configuration is measured by its ability to sustain high throughput while maintaining predictable latency, particularly under heavy load conditions typical of distributed storage or high-frequency trading environments.
2.1 Throughput Benchmarks
Testing utilized the iPerf3 toolset against a known, non-congested peer, measuring sustained unidirectional throughput.
| Workload Type | Configuration Setting | Achieved Throughput (Gbps) | Utilization (%) |
|---|---|---|---|
| Standard TCP (Jumbo Frames: 9000 bytes) | Kernel Stack, Standard OS Tuning | 385 Gbps | 96.25% |
| Kernel Bypass (DPDK/Solarflare OpenOnload) | Optimized User-Space Polling | 398 Gbps | 99.5% |
| RDMA (RoCEv2) | Kernel Bypass, Zero-Copy Enabled | 399.5 Gbps | 99.875% |
The slight deviation from the theoretical 400 Gbps aggregate is attributed to inherent protocol overhead (e.g., TCP/IP headers, physical layer framing). In optimal RDMA scenarios, throughput approaches the wire speed limit.
2.2 Latency Analysis
Network latency is often more critical than raw throughput for interactive and transactional workloads. Measurements below reflect the round-trip time (RTT) from the host application layer to the remote peer's application layer.
Latency Testing Environment: Measured using `ping` utility on Linux systems (ICMP echo), and specialized network stack latency tools (e.g., `sockperf` for TCP/UDP).
| Protocol/Stack | Average Latency (μs) | 99th Percentile Latency (μs) |
|---|---|---|
| Standard TCP/IP (Kernel) | 4.5 μs | 12.1 μs |
| UDP (Kernel) | 3.8 μs | 9.5 μs |
| DPDK (User Space Poll Mode Driver) | 1.2 μs | 2.5 μs |
| RoCEv2 (Kernel Bypass) | 0.9 μs | 1.8 μs |
The dramatic reduction in latency when utilizing kernel bypass techniques (DPDK/RoCEv2) highlights the overhead associated with OS kernel context switching and interrupt handling inherent in traditional network stacks. For ultra-low latency applications, kernel bypass is mandatory.
2.3 CPU Utilization Mapping
Understanding how CPU resources are consumed by network processing is vital for capacity planning. The following table shows CPU utilization breakdown during sustained 390 Gbps transmission.
| Component | CPU Core Allocation (Example: 120 Cores Total) | Percentage of Total CPU Cycles Consumed |
|---|---|---|
| Network Interrupt/Polling Threads (Kernel) | 32 Cores (Dedicated RSS Queue Handlers) | 26.7% |
| Application Processing Threads (Data Manipulation) | 60 Cores (Shared with DPDK/RDMA processing) | 50.0% |
| System Overhead (OS, Management) | 8 Cores | 6.7% |
| Idle/Headroom | 20 Cores | 16.6% |
This configuration demonstrates that even at near-maximum utilization, significant headroom remains (16.6%) due to the effectiveness of hardware offloads and the high core count available.
3. Recommended Use Cases
The HTNO server configuration is specifically engineered for environments where network I/O is the primary performance constraint. Deployments requiring high sustained data movement across the network fabric are ideal candidates.
3.1 High-Performance Computing (HPC) Clusters
In tightly coupled HPC environments, inter-node communication often relies on high-speed interconnects like InfiniBand or high-speed Ethernet utilizing RDMA.
- **MPI Traffic:** Applications using Message Passing Interface (MPI) benefit immensely from the low latency provided by RoCEv2, minimizing synchronization waits between compute nodes.
- **Distributed File Systems:** Deployments of Lustre or BeeGFS benefit from the high aggregate bandwidth, especially during checkpointing or large file transfers between compute nodes and storage targets.
For more detail on cluster interconnect standards, see HPC Interconnect Standards.
3.2 Software-Defined Storage (SDS)
SDS solutions require massive east-west traffic capacity to replicate, rebalance, and distribute data across the cluster nodes.
- **Ceph/GlusterFS:** These systems thrive on high-throughput NICs. A single storage node can easily saturate 100GbE links when handling replication traffic (e.g., 3x replication factor). The 200GbE ports allow for significant future scaling or lower latency consistency.
- **NVMe-oF Targets:** When serving NVMe namespaces over TCP or RDMA, the server must handle both the storage processing and the network transmission simultaneously. The HTNO configuration ensures the network path does not introduce stalls into the storage path. Refer to NVMe over Fabrics Implementation.
3.3 Real-Time Data Ingestion and Streaming
Environments processing massive, continuous streams of data, such as financial market data feeds or large-scale IoT telemetry, require predictable, low-jitter bandwidth.
- **Financial Trading Platforms:** Low-latency market data distribution and order execution require the sub-2 microsecond latency achievable via kernel bypass networking.
- **Log Aggregation:** Centralized log collection systems (e.g., Kafka brokers, Elasticsearch ingestion nodes) benefit from the ability to absorb bursts of data without dropping packets, thanks to the large buffer capacity of modern 200GbE NICs. See Data Streaming Architectures.
3.4 Virtualization and Cloud Infrastructure
In modern virtualized environments, the hypervisor itself must manage significant network traffic for VM-to-VM communication, live migration, and storage backhaul.
- **VM Density:** High-density VM hosts can leverage the 400Gbps aggregate bandwidth to ensure that even highly active VMs do not contend for network resources on the physical host.
- **Live Migration:** Large memory pages (e.g., 2GB HugePages) used in live migration require rapid, high-bandwidth transfer. This configuration can migrate a 1TB memory state in mere seconds, minimizing downtime. Discussed further in Virtual Machine Live Migration Protocols.
4. Comparison with Similar Configurations
To justify the investment in 200GbE infrastructure, it is essential to compare this HTNO configuration against more standard or lower-tier deployments. We establish two key comparison points: a Standard Enterprise Server (SES) equipped with 25GbE, and a Performance-Optimized Server (POS) equipped with 100GbE.
4.1 Comparative Analysis Table
This table summarizes the key differences that impact network bandwidth performance.
| Feature | Standard Enterprise (SES - 25GbE) | Performance Optimized (POS - 100GbE) | High-Throughput Optimized (HTNO - 200GbE) |
|---|---|---|---|
| Aggregate Network Speed (Theoretical) | 50 Gbps (2x 25GbE) | 200 Gbps (2x 100GbE) | 400 Gbps (2x 200GbE) |
| PCIe Generation | Gen4 x16 | Gen4 x16 | Gen5 x16 |
| CPU Core Count (Example) | 24 Cores | 64 Cores | 120+ Cores |
| Memory Bandwidth | Moderate (DDR4) | High (DDR5) | Very High (DDR5, Max Channels) |
| Latency Profile (RoCEv2 capable) | ~5 μs | ~2 μs | <2 μs |
| Cost Index (Relative) | 1.0x | 1.8x | 2.8x |
4.2 Bandwidth Scaling Limitations
The primary limitation in the SES and POS configurations is the ability of the CPU and PCIe bus to keep pace with the NICs.
1. **SES (25GbE):** The CPU is often the bottleneck, as traditional kernel stacks consume significant CPU cycles handling 25Gbps traffic, leaving only 50% of the CPU available for the application. Furthermore, 25GbE NICs often rely on PCIe Gen3 or early Gen4, limiting the total available I/O bandwidth to the CPU complex. See PCIe Generation Impact on I/O. 2. **POS (100GbE):** While 100GbE offers a significant leap, saturation often occurs at 80-90 Gbps sustained throughput when using standard TCP/IP stacks, due to interrupt coalescing and context switching overhead reducing the effective application processing time. The HTNO configuration overcomes this via Offload Engine Utilization.
The HTNO's use of PCIe Gen5 is crucial. A 200GbE link requires approximately 25 GB/s of unidirectional bandwidth. PCIe Gen4 x16 provides ~32 GB/s, which is sufficient but leaves little room for other peripherals (e.g., storage controllers). PCIe Gen5 x16 provides ~64 GB/s, offering ample headroom for dual 200GbE adapters and high-speed NVMe storage concurrently.
4.3 Latency Trade-offs
As shown in Section 2.2, the latency improvement scales non-linearly with hardware investment. Moving from Kernel to DPDK/RoCEv2 provides the most significant latency reduction (a factor of 3-5x improvement), regardless of the absolute NIC speed (100GbE vs 200GbE). However, the 200GbE NICs typically offer superior hardware offload engines and lower internal queue latencies compared to older 100GbE generations, compounding the performance benefit.
5. Maintenance Considerations
Deploying high-density, high-power components necessitates stringent maintenance protocols focusing on thermal management, power delivery, and firmware integrity.
5.1 Thermal Management and Airflow
Sustaining 400 Gbps across two NICs, coupled with dual high-TDP CPUs (approx. 700W total CPU TDP), generates substantial heat.
- **Airflow Requirements:** The server rack must maintain a front-to-back airflow velocity of at least 200 Linear Feet Per Minute (LFM) at the server intake, measured at 24°C ambient temperature. Failure to meet this risks thermal throttling of the CPUs, which directly impacts the rate at which data can be prepared for network transmission. See Data Center Cooling Standards.
- **NIC Thermal Profiles:** Modern 200GbE NICs often have thermal limits. Monitoring the physical temperature sensor on the NIC ASIC (via BMC/IPMI) is essential. Sustained temperatures above 85°C may trigger throttling mechanisms within the adapter firmware, reducing effective throughput below the rated 200Gbps per port.
5.2 Power Consumption and Redundancy
The HTNO configuration is power-hungry. Accurate power budgeting is non-negotiable.
| Component | Estimated Peak Draw (Watts) |
|---|---|
| Dual CPUs (350W+350W TDP) | 750 W (Accounting for turbo headroom) |
| Memory (1TB DDR5) | 150 W |
| Dual 200GbE NICs | 70 W (Combined, including optical modules) |
| Storage (8x NVMe SSDs) | 80 W |
| Chassis, Fans, Motherboard | 200 W |
| **Total Estimated Peak System Draw** | **~1250 W** |
The 2x 2000W Platinum PSUs provide necessary redundancy (N+1 capability) and efficiency margin. However, the Power Distribution Unit (PDU) in the rack must be rated to handle the aggregated load of multiple such servers. A typical 42U rack populated with 20 HTNO servers could draw over 25 kW, requiring high-amperage 3-phase power infrastructure. See Rack Power Density Calculation.
5.3 Firmware and Driver Management
Network performance is highly sensitive to software layers. Consistent firmware management is crucial for stability and performance guarantees.
- **BIOS/UEFI:** Ensure the BIOS is updated to the latest stable version supporting optimal PCIe Gen5 initialization sequencing and memory training profiles. Specific settings for PCIe ASPM (Active State Power Management) must often be disabled to prevent latency spikes during low-utilization periods.
- **NIC Firmware:** NIC firmware updates are released frequently to improve offload engine efficiency, enhance RoCEv2 stability, and fix security vulnerabilities. A strict patch cycle (e.g., quarterly review) must be implemented.
- **Driver Stack:** For high-performance networking, proprietary vendor drivers (e.g., Mellanox OFED stack, Intel E800 series drivers) must be installed *instead* of the generic in-box operating system drivers to unlock full hardware acceleration features like DPDK support and RDMA capabilities. See Driver Versioning Best Practices.
5.4 Network Cabling and Optics
The physical layer connection must match the performance characteristics of the NICs.
- **Cabling:** For in-rack (0.5m to 3m) connections, Direct Attach Copper (DAC) cables are preferred due to their low attenuation and cost. For connections spanning the top-of-rack (ToR) switch, Active Optical Cables (AOC) or pluggable optics (QSFP-DD SR4/DR4) are required.
- **Switch Interconnect:** The ToR switch must support 200GbE uplinks (or utilize breakout cables, e.g., 4x 50GbE or 2x 100GbE) and possess a non-blocking backplane architecture capable of handling aggregate traffic from all connected servers simultaneously. A switch with a total fabric capacity significantly exceeding the sum of connected ports is required to prevent switch buffer exhaustion.
6. Advanced Configuration Topics for Network Optimization
Achieving maximum performance requires tuning beyond basic hardware installation. This section covers essential software and operating system configurations.
6.1 Interrupt Management and RSS/RPS
For standard kernel networking, efficient distribution of incoming packets across multiple CPU cores is vital to prevent any single core from becoming a bottleneck handling network interrupts.
- **Receive Side Scaling (RSS):** RSS maps incoming network flows (based on source/destination IP/Port tuples) to specific CPU queues. In the HTNO setup, we must ensure the number of RSS queues configured on the NIC matches or exceeds the number of available physical cores dedicated to network processing.
* *Tuning:* Set `ethtool -d <interface>` to verify supported RSS queues. Adjust queue count using `ethtool -L <interface> combined <N>`.
- **Receive Packet Steering (RPS):** While RSS distributes hardware interrupts, RPS allows the kernel to steer software processing (the packet handling *after* the interrupt) to different cores. This is crucial when using large data structures that benefit from NUMA locality. Improper RPS configuration can lead to unnecessary cross-socket traffic.
6.2 TCP Stack Tuning Parameters
Even when using kernel bypass for critical paths, the standard TCP stack is often used for management, file transfers, and non-latency-sensitive traffic. Tuning the TCP buffers is essential for high-bandwidth links.
- **TCP Window Scaling:** Must be fully enabled (it is standard on modern OSes) to allow the receiver to advertise large receive windows, preventing the sender from stalling due to insufficient buffer space over high-latency links.
- **Buffer Sizes:** System-wide and per-socket buffer limits must be increased substantially.
| Parameter | Default Value (Example) | HTNO Recommended Value |
|---|---|---|
| `net.core.rmem_max` | 26214400 (25 MB) | 1073741824 (1 GB) |
| `net.core.wmem_max` | 26214400 (25 MB) | 1073741824 (1 GB) |
| `net.ipv4.tcp_rmem` (Min/Default/Max) | 4096/87380/26214400 | 4096/1048576/2147483647 (Max possible) |
| `net.ipv4.tcp_wmem` (Min/Default/Max) | 4096/65536/26214400 | 4096/1048576/2147483647 (Max possible) |
- Note: Setting maximums to the theoretical maximum (2^31-1) is common in high-speed environments, though specific application requirements may dictate smaller, more predictable limits.* Refer to TCP Buffer Optimization.
6.3 NUMA Awareness and Thread Pinning
The dual-socket architecture demands strict adherence to NUMA principles to avoid performance penalties associated with accessing memory across the inter-socket interconnect (e.g., Intel Ultra Path Interconnect - UPI).
1. **NIC Placement:** The 200GbE NICs must be placed in PCIe slots physically connected to the CPU socket whose memory banks they will primarily access. For example, NIC-A connects to CPU-0 lanes, and NIC-B connects to CPU-1 lanes. 2. **Thread Pinning:** Application threads responsible for processing packets arriving on NIC-A must be explicitly pinned (using `taskset` or equivalent scheduler affinity tools) to cores belonging to CPU-0. This ensures data processing occurs directly over the local memory channels.
* *Verification:* Use tools like `numastat` or `perf` to monitor cross-NUMA memory accesses. Ideally, cross-NUMA memory traffic related to network I/O should be near zero during peak load. See NUMA Memory Allocation Strategies.
6.4 Jumbo Frames and MTU Settings
For bulk data transfer, increasing the Maximum Transmission Unit (MTU) size significantly improves efficiency by reducing the per-packet overhead (headers) relative to the payload size.
- **Recommendation:** Set the MTU to 9000 bytes (Jumbo Frames) across the entire network path (Host NIC -> Switch Port -> Remote Host NIC).
- **Efficiency Gain:** A standard Ethernet frame (1500 bytes) requires 1.15 header bytes for every 6.8 payload bytes. A 9000-byte frame reduces this overhead ratio substantially, leading to higher effective application throughput for the same wire speed. See Jumbo Frames Implementation Guide.
7. Security Implications of High-Speed Networking
While performance is paramount, the increased data velocity introduces new security considerations, particularly regarding intrusion detection and denial-of-service (DoS) mitigation.
7.1 Intrusion Detection System (IDS) Bottlenecks
Traditional, software-based IDS/IPS solutions struggle to inspect traffic flowing at 200Gbps per link.
- **Solution:** Security processing must be shifted to hardware acceleration. Modern 200GbE NICs support features like cryptographic offloads and, more importantly, **Packet Filtering/Firewall Offloads** directly on the NIC ASIC.
- **Flow Steering:** Utilizing SR-IOV virtualization, specific virtual functions (VFs) can be steered directly to specialized security VMs (e.g., virtual firewalls) without traversing the main host kernel, allowing the security appliance to operate at near-line rate. See SR-IOV Network Virtualization.
7.2 DoS Mitigation
High-bandwidth links are prime targets for volumetric DoS attacks.
- **Rate Limiting:** Hardware rate limiting features on the NICs must be configured to drop excessive traffic destined for specific flows or IPs before it consumes host CPU cycles.
- **Flow Control:** Enabling IEEE 802.3x (Pause Frames) on the switch and NIC level can prevent congestion collapse in the physical layer, though this is generally less preferred than intelligent QoS marking in modern fabrics. See Network Congestion Control.
8. Conclusion and Deployment Strategy
The High-Throughput Network Optimized (HTNO) server configuration, featuring dual 200GbE PCIe Gen5 NICs, high-core count CPUs, and vast DDR5 memory, is an elite platform designed to eliminate network I/O as the primary performance constraint in enterprise and HPC workloads.
Successful deployment relies not just on the selection of these premium components, but critically on the meticulous tuning of the software stack—specifically embracing kernel bypass (DPDK/RoCEv2) and enforcing strict NUMA locality. Organizations must budget for the requisite power and cooling infrastructure capable of sustaining these peak thermal loads.
This architecture is the necessary foundation for next-generation distributed systems requiring sustained, sub-2 microsecond inter-node communication or multi-hundred-gigabit data movement.
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.* ⚠️