AI Standards
- AI Standards - Server Configuration
This document details the server configuration standards for all servers dedicated to Artificial Intelligence (AI) workloads within our infrastructure. These standards ensure consistency, performance, and maintainability across all AI-related deployments. This guide is intended for newcomers to the server environment and will cover hardware, software, and networking aspects. Please consult with the System Administrators team before making any changes to server configurations.
Hardware Specifications
AI workloads demand significant computational resources. The following table outlines the minimum and recommended hardware specifications for AI servers. These specifications are subject to change as AI model complexity increases. Please refer to the Hardware Revision History for the latest updates.
| Component | Minimum Specification | Recommended Specification | - | CPU | Intel Xeon Silver 4310 (12 Cores) | Intel Xeon Gold 6338 (32 Cores) | - | RAM | 128 GB DDR4 ECC 3200MHz | 512 GB DDR4 ECC 3200MHz | - | GPU | NVIDIA Tesla T4 (16GB VRAM) | NVIDIA A100 (80GB VRAM) | - | Storage (OS) | 500GB NVMe SSD | 1TB NVMe SSD | - | Storage (Data) | 4TB HDD (RAID 1) | 16TB HDD (RAID 6) or 8TB NVMe SSD (RAID 1) | - | Network Interface | 10GbE | 40GbE or 100GbE |
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These specifications are designed to support a wide range of AI tasks, including Machine Learning, Deep Learning, and Natural Language Processing.
Software Stack
All AI servers will utilize a standardized software stack to ensure compatibility and ease of deployment. The Software Deployment Pipeline details the automated deployment process.
| Software Component | Version | Purpose | - | Operating System | Ubuntu Server 22.04 LTS | Provides the base operating environment. See OS Hardening Guide for security best practices. | - | CUDA Toolkit | 12.3 | NVIDIA’s parallel computing platform and programming model. | - | cuDNN | 8.9.2 | NVIDIA CUDA Deep Neural Network library. | - | Python | 3.10 | Primary programming language for AI development. | - | TensorFlow | 2.12 | Open-source machine learning framework. | - | PyTorch | 2.0.1 | Open-source machine learning framework. | - | Docker | 24.0.5 | Containerization platform for application deployment. Refer to Docker Best Practices. |
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All software packages must be installed using approved package managers and repositories. Direct compilation from source is discouraged unless explicitly approved by the Security Team. Regularly check for software updates and apply them promptly following the Patch Management Policy.
Networking Configuration
Proper network configuration is crucial for efficient data transfer and communication between AI servers.
| Network Parameter | Value | - | IP Addressing | Static IP Addresses assigned via DHCP reservation. See IP Address Allocation. | - | DNS | Internal DNS servers managed by the Network Engineering team. | - | Firewall | Enabled with a restrictive default policy. Refer to the Firewall Ruleset. | - | VLAN | Dedicated VLAN for AI workloads (VLAN 100). | - | SSH Access | Limited to authorized personnel via key-based authentication. See SSH Security Guidelines. | - | Monitoring | Servers are monitored by Nagios and Prometheus for performance and availability. |
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All AI servers are required to be part of the dedicated AI VLAN to ensure network isolation and security. Network traffic should be monitored regularly to identify any anomalies. Consider using Network Performance Monitoring tools for detailed analysis.
Security Considerations
AI workloads often involve sensitive data, making security a paramount concern.
- **Data Encryption:** All data at rest and in transit must be encrypted. Utilize Disk Encryption Standards and TLS Configuration.
- **Access Control:** Implement strict access control policies based on the principle of least privilege. Refer to the Access Control Matrix.
- **Vulnerability Scanning:** Regularly scan servers for vulnerabilities using Vulnerability Assessment Tools.
- **Intrusion Detection:** Implement an intrusion detection system (IDS) to monitor for malicious activity. See IDS Configuration.
- **Regular Backups:** Perform regular backups of all data and configurations. Utilize the Backup and Recovery Procedures.
Further Documentation
- Server Build Process
- Troubleshooting Guide
- Performance Tuning
- Server Monitoring Dashboard
- Contacting Support
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.* ⚠️