AI in Scotland
- AI in Scotland: A Server Configuration Overview
This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives within Scotland. It's designed for newcomers to our MediaWiki site and provides a technical overview of the hardware and software used. We'll cover server specifications, networking, and key software components. This guide assumes a basic understanding of server administration and Linux systems.
Introduction
Scotland is rapidly becoming a hub for AI research and development, driven by universities like the University of Edinburgh and a growing number of AI-focused startups. This increased activity necessitates a robust and scalable server infrastructure. The following sections outline the composition of these systems. Our current focus is on providing sufficient compute power for Machine Learning tasks, specifically Deep Learning. Understanding the server configuration is critical for System Administrators and developers alike.
Server Hardware Specifications
Our core AI servers are built around high-performance components. The current standard configuration is detailed below. This configuration is subject to change based on project requirements and budget constraints. We utilize a hybrid approach, combining on-premise servers with cloud resources via Amazon Web Services for peak demand.
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores per CPU) |
| RAM | 512GB DDR4 ECC Registered @ 3200MHz |
| Storage (OS) | 1TB NVMe SSD |
| Storage (Data) | 16TB RAID 6 HDD array (SAS, 7200 RPM) |
| GPU | 4x NVIDIA A100 (80GB HBM2e) |
| Network Interface | Dual 100GbE Mellanox ConnectX-6 |
| Power Supply | 2000W Redundant Power Supplies |
We also maintain a cluster of smaller servers for less demanding tasks such as data preprocessing and model deployment. These servers typically feature a single NVIDIA RTX 3090 GPU. The selection of Hardware RAID controllers is crucial to ensure data integrity.
Networking Infrastructure
The server infrastructure is connected via a high-speed, low-latency network. A dedicated VLAN is used to isolate AI traffic from other network activity. We employ BGP for routing and utilize redundant network paths to ensure high availability. All communication is encrypted using TLS/SSL.
| Network Component | Specification |
|---|---|
| Core Switches | Cisco Nexus 9508 |
| Distribution Switches | Arista 7050X |
| Interconnect | 100GbE fiber optic cabling |
| Firewall | Palo Alto Networks PA-820 |
| VLAN | 192.168.10.0/16 (AI Network) |
Regular Network Monitoring is performed using tools like Nagios and Zabbix to identify and address potential bottlenecks. We are currently investigating the implementation of Software-Defined Networking (SDN) to improve network agility.
Software Stack
The servers run a customized distribution of Ubuntu Server 22.04 LTS. The core software stack consists of the following components. Version control is managed using Git and hosted on a private GitLab instance.
| Software Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| CUDA Toolkit | 12.1 | GPU programming framework |
| cuDNN | 8.7.0 | Deep neural network library |
| TensorFlow | 2.12.0 | Machine learning framework |
| PyTorch | 2.0.1 | Machine learning framework |
| Docker | 24.0.5 | Containerization platform |
| Kubernetes | 1.27 | Container orchestration |
We prioritize Security Updates and regularly patch the servers to address vulnerabilities. Automated deployment pipelines are used to streamline the software installation and configuration process. We use Ansible for configuration management. Logging is centralized using the ELK stack (Elasticsearch, Logstash, Kibana).
Future Considerations
We are actively exploring the integration of new technologies to further enhance the AI server infrastructure. This includes investigating the use of Quantum Computing resources and exploring alternative GPU architectures. We are also planning to expand our cloud infrastructure to accommodate growing demand. The implementation of Federated Learning is also under consideration to improve data privacy.
See Also
- Server Administration Guide
- Network Configuration
- Security Best Practices
- Data Storage Solutions
- GPU Computing
- Cloud Computing
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.* ⚠️