Machine learning operations

# Machine Learning Operations Server Configuration

This article details the recommended server configuration for running Machine Learning Operations (MLOps) within our MediaWiki environment. It's geared towards system administrators and engineers responsible for deploying and maintaining the infrastructure supporting our machine learning workflows. This covers hardware, software, and networking considerations.

Overview

MLOps requires significant computational resources, especially for training and serving models. This guide outlines the specifications for three primary server roles: the Development Server, the Training Server, and the Inference Server. Each role has different requirements, and proper configuration is crucial for performance, scalability, and reliability. Understanding Resource allocation is paramount. We will also cover Monitoring and logging best practices.

Development Server Configuration

The Development Server is used by data scientists for prototyping, experimentation, and initial model development. It doesn't require the same level of horsepower as the Training or Inference Servers, but it needs sufficient resources to handle common development tasks. Consider using Docker containers for consistent environments.

Software installed on the Development Server should include: Python 3.9, Jupyter Notebook, TensorFlow, PyTorch, scikit-learn, and version control systems like Git. Proper security hardening is essential, even in a development environment.

Training Server Configuration

The Training Server is responsible for the computationally intensive task of training machine learning models. It requires powerful CPUs, a large amount of RAM, and, crucially, multiple high-end GPUs. Distributed training is a common strategy to accelerate training. We utilize Kubernetes for orchestration of these servers.

The Training Server software stack should include the same components as the Development Server, but with optimized libraries for distributed training (e.g., Horovod, DeepSpeed). Consider using GPU monitoring tools to track utilization and performance. Regular data backups are vital.

Inference Server Configuration

The Inference Server is responsible for deploying trained models and serving predictions in real-time. It needs to be optimized for low latency and high throughput. Model serving frameworks like TensorFlow Serving or TorchServe are recommended.

The Inference Server should be configured with a load balancer (e.g., HAProxy, Nginx) to distribute traffic across multiple instances for high availability and scalability. API monitoring is crucial to ensure predictions are being served correctly and efficiently. We also employ autoscaling to dynamically adjust the number of inference servers based on demand. Careful capacity planning is essential to meet peak loads.

Networking Considerations

All servers should be connected to a high-bandwidth, low-latency network. Consider using a dedicated VLAN for MLOps traffic. Implement appropriate firewall rules to secure the servers. Network segmentation is a key security practice.

Security Best Practices

• Regularly update all software packages.
• Implement strong authentication and authorization mechanisms.
• Encrypt sensitive data at rest and in transit.
• Monitor server logs for suspicious activity.
• Perform regular security audits. Vulnerability scanning should be automated.

Future Considerations

We are investigating the use of specialized hardware accelerators, such as TPUs, to further improve training performance. We are also exploring the integration of edge computing for low-latency inference.

Main Page Server Administration Data Science Machine Learning Deep Learning Infrastructure Deployment Scalability Performance Tuning Security Monitoring Logging Troubleshooting Kubernetes Docker Resource allocation Distributed training Model serving frameworks API monitoring Autoscaling Capacity planning GPU monitoring tools Data backups Security hardening Vulnerability scanning Network segmentation TPUs Edge computing HAProxy Nginx Git Python 3.9 Jupyter Notebook TensorFlow PyTorch scikit-learn CentOS Ubuntu Intel Xeon AMD EPYC NVIDIA NVLink RDMA Storage Operating System CPU RAM GPU Networking Firewall VLAN Load Balancer Encryption Authentication Authorization Security Audits Server Logs Docker containers Software Installation Hardware Specifications Infrastructure Design Deployment Strategies Monitoring Tools Logging Best Practices Troubleshooting Guide Kubernetes Configuration Docker Images Resource Management Distributed Systems Model Optimization API Design Scalability Solutions Performance Analysis Security Policies Monitoring Dashboards Log Analysis Troubleshooting Techniques Kubernetes Orchestration Docker Security Resource Allocation Strategies Distributed Training Frameworks Model Serving Architectures API Monitoring Metrics Autoscaling Policies Capacity Planning Methodologies GPU Utilization Monitoring Data Backup Procedures Security Hardening Steps Vulnerability Scanning Tools Network Segmentation Techniques TPU Integration Edge Computing Deployment HAProxy Configuration Nginx Configuration Git Version Control Python Package Management Jupyter Notebook Best Practices TensorFlow Optimization PyTorch Optimization scikit-learn Best Practices CentOS Security Ubuntu Security Intel Xeon Performance AMD EPYC Performance NVIDIA GPU Performance NVLink Technology RDMA Networking Storage Solutions Operating System Security CPU Architecture RAM Specifications GPU Memory Networking Protocols Firewall Rules VLAN Configuration Load Balancing Algorithms Encryption 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scikit-learn Power Optimization CentOS Power Management Ubuntu Power Optimization Intel Xeon Power Efficiency AMD EPYC Power Efficiency NVIDIA GPU Power Efficiency NVLink Power Consumption RDMA Power Management Storage Power Efficiency Operating System Power Consumption CPU Power Management RAM Power Efficiency GPU Power Consumption Networking Power Management Firewall Power Efficiency VLAN Power Consumption Load Balancer Power Efficiency Encryption Power Consumption Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Consumption Scalability Power Management Performance Tuning Power Consumption Security Power Efficiency Monitoring Dashboard Power Management Log Analysis Power Consumption Troubleshooting Power Efficiency Kubernetes Power Consumption Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Management GPU Performance Power Consumption Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Efficiency Network Intrusion Detection Power Management TPU Power Consumption Edge Computing Power Efficiency HAProxy Power Consumption Nginx Power Management Git Power Efficiency Python Power Consumption Jupyter Notebook Power Management TensorFlow Power Efficiency PyTorch Power Consumption scikit-learn Power Management CentOS Power Efficiency Ubuntu Power Consumption Intel Xeon Power Management AMD EPYC Power Consumption NVIDIA GPU Power Management NVLink Power Efficiency RDMA Power Consumption Storage Power Management Operating System Power Efficiency CPU Power Efficiency RAM Power Consumption GPU Power Management 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Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power Consumption Kubernetes Power Efficiency Docker Image Power Management Resource Allocation Automation Power Consumption Distributed Training Power Efficiency Model Serving Power Management API Monitoring Power Consumption Autoscaling Power Efficiency Capacity Planning Power Consumption GPU Performance Power Management Data Backup Power Efficiency Security Patch Power Consumption Vulnerability Remediation Power Management Network Intrusion Detection Power Efficiency TPU Power Management Edge Computing Power Consumption HAProxy Power Management Nginx Power Efficiency Git Power Consumption Python Power Management Jupyter Notebook Power Efficiency TensorFlow Power Consumption PyTorch Power Management scikit-learn Power Efficiency CentOS Power Consumption Ubuntu Power Management Intel Xeon Power Efficiency AMD EPYC Power Management NVIDIA GPU Power Consumption NVLink Power Efficiency RDMA Power Management Storage Power Consumption Operating System Power Efficiency CPU Power Consumption RAM Power Management GPU Power Efficiency Networking Power Consumption Firewall Power Management VLAN Power Efficiency Load Balancer Power Consumption Encryption Power Management Authentication Power Efficiency Authorization Power Management Security Monitoring Power Consumption Server Log Power Efficiency Docker Container Power Consumption Resource Allocation Power Efficiency Distributed System Power Consumption Model Optimization Power Efficiency API Design Power Management Scalability Power Consumption Performance Tuning Power Efficiency Security Power Management Monitoring Dashboard Power Consumption Log Analysis Power Efficiency Troubleshooting Power

[[Category:Server Hardware

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.* ⚠️