AI Innovation
# AI Innovation Server Configuration
This document details the configuration of the "AI Innovation" server, a dedicated resource for artificial intelligence and machine learning workloads. This guide is intended for new system administrators and developers becoming familiar with the server infrastructure. It covers hardware specifications, software stack, networking, and important configuration details.
Overview
The AI Innovation server is designed to provide a high-performance computing environment for tasks such as model training, data analysis, and deployment of AI applications. It leverages a powerful GPU-accelerated architecture and a robust software stack to deliver optimal performance. The server is a critical component of our research and development pipeline, supporting projects across various AI domains. Understanding its configuration is essential for effective utilization and maintenance. See also Server Infrastructure Overview for broader context.
Hardware Specifications
The following table outlines the core hardware specifications of the AI Innovation server:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 Cores / 64 Threads per CPU) |
| RAM | 512 GB DDR4 ECC Registered 3200MHz |
| Primary Storage | 2 x 1TB NVMe PCIe Gen4 SSD (RAID 1) - Operating System & Applications |
| Secondary Storage | 8 x 16TB SAS HDD (RAID 6) - Data Storage |
| GPU | 2 x NVIDIA A100 80GB PCIe 4.0 |
| Network Interface | Dual 100GbE QSFP28 |
| Power Supply | Redundant 2000W 80+ Platinum |
Detailed information on Hardware Maintenance Procedures can be found on the wiki. Regular hardware checks are vital, refer to System Monitoring.
Software Stack
The AI Innovation server utilizes a Linux-based operating system and a curated software stack optimized for AI/ML workloads.
- Operating System: Ubuntu 22.04 LTS (Long Term Support)
- Containerization: Docker 20.10.12 and Kubernetes 1.24
- GPU Drivers: NVIDIA Driver 525.85.12
- CUDA Toolkit: CUDA 11.8
- Machine Learning Frameworks: TensorFlow 2.12.0, PyTorch 2.0.1, scikit-learn 1.2.2
- Programming Languages: Python 3.10, R 4.2.1
- Data Science Tools: Jupyter Notebook, VS Code with Python extension
- SSH Access: Access is restricted to authorized personnel via SSH using key-based authentication. Password authentication is disabled.
- User Accounts: Dedicated user accounts are created for each researcher and developer. Access permissions are managed through group membership. Refer to User Account Management.
- Data Backup: Daily backups of the primary storage are performed and stored on a separate network-attached storage (NAS) device. See Backup and Recovery Procedures.
- Monitoring: The server is continuously monitored using Prometheus and Grafana for CPU utilization, memory usage, GPU performance, and network traffic. See System Monitoring.
- GPU Resource Management: GPU resources are managed using NVIDIA's Multi-Instance GPU (MIG) technology, allowing for the partitioning of GPUs into smaller, isolated instances.
- Server Documentation Index
- Troubleshooting Guide
- Contact IT Support
- Telegram: @powervps Servers at a discounted price
For detailed software installation guides, please see Software Installation Guide. Version control is managed using Git Repository Access.
Networking Configuration
The server is connected to the internal network via dual 100GbE interfaces. These interfaces are configured with static IP addresses and are members of a dedicated VLAN for research traffic.
| Interface | IP Address | Subnet Mask | Gateway |
|---|---|---|---|
| enp1s0f0 | 192.168.10.10 | 255.255.255.0 | 192.168.10.1 |
| enp1s0f1 | 192.168.10.11 | 255.255.255.0 | 192.168.10.1 |
DNS resolution is handled by our internal DNS servers. Firewall rules are configured to allow necessary traffic for research purposes while restricting unauthorized access. See Network Security Policy for more details. Port forwarding requests should be submitted via IT Support Ticket System.
Configuration Details
Several key configuration parameters are specific to the AI Innovation server.
GPU Configuration
The NVIDIA A100 GPUs are configured for maximum performance.
| Parameter | Value |
|---|---|
| GPU Model | NVIDIA A100 80GB |
| Driver Version | 525.85.12 |
| CUDA Version | 11.8 |
| MIG Configuration | Enabled (Up to 7 instances per GPU) |
| GPU Memory Utilization Threshold | 85% (Alerts triggered above this level) |
Detailed instructions on utilizing MIG can be found in GPU MIG Configuration. Regular GPU driver updates are scheduled to ensure optimal performance and security.
Security Considerations
The AI Innovation server handles sensitive data and is therefore subject to strict security protocols. All access must be authorized, and regular security audits are conducted. Data encryption is implemented both in transit and at rest. See Server Security Best Practices for detailed security guidelines. Report any security vulnerabilities through Security Incident Reporting.
Further Information
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.* ⚠️