AI in Anguilla
- AI in Anguilla: Server Configuration & Deployment
This article details the server configuration used to support Artificial Intelligence (AI) initiatives within Anguilla. It is aimed at new system administrators and developers contributing to our infrastructure. This documentation outlines hardware, software, and networking details crucial for maintaining a robust and scalable AI platform. Please refer to System Administration Guide for general server maintenance procedures.
Overview
Anguilla's AI infrastructure is currently focused on three key areas: natural language processing (NLP) for local dialect understanding (see Linguistic Analysis Project), computer vision for environmental monitoring (see Coral Reef Monitoring Initiative), and predictive analytics for resource allocation (see Resource Management System). This demands significant computational power and specialized software. The current setup utilizes a hybrid cloud approach, with core processing handled on-island and overflow capacity provided by a reputable cloud provider (see Cloud Provider Integration). Understanding the interplay between local and cloud resources is paramount. This setup is detailed in the Disaster Recovery Plan.
Hardware Specifications
Our primary AI server cluster consists of four high-performance nodes. Detailed specifications are outlined below:
| Component | Specification |
|---|---|
| CPU | 2x AMD EPYC 7763 (64-core, 128 threads) |
| RAM | 512 GB DDR4 ECC Registered (3200 MHz) |
| Storage (OS) | 1 TB NVMe SSD (PCIe 4.0) |
| Storage (Data) | 16 TB RAID 6 (SAS 12Gbps, Enterprise Grade) |
| GPU | 4x NVIDIA A100 (80GB HBM2e) |
| Network Interface | Dual 100GbE QSFP28 |
| Power Supply | 2x 1600W Redundant |
These servers are housed in a dedicated, climate-controlled server room (see Server Room Security Protocol). Each server runs a customized version of Ubuntu Server 22.04. Regular hardware audits are performed, documented in Hardware Inventory Management.
Software Stack
The software stack is built around a core data science platform. Key components include:
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS and system management |
| Python | 3.10 | Primary programming language for AI models |
| TensorFlow | 2.12 | Deep learning framework |
| PyTorch | 2.0 | Deep learning framework |
| CUDA Toolkit | 12.2 | NVIDIA GPU acceleration |
| cuDNN | 8.9 | NVIDIA Deep Neural Network library |
| Docker | 20.10 | Containerization for application deployment |
| Kubernetes | 1.26 | Container orchestration |
All code is managed using Git and stored in a private repository. Continuous integration and continuous deployment (CI/CD) pipelines are managed using Jenkins. See the Software Deployment Guidelines for detailed instructions on deploying new software.
Networking Configuration
The AI server cluster is connected to the Anguilla network via a dedicated VLAN. Key networking details are as follows:
| Parameter | Value |
|---|---|
| VLAN ID | 100 |
| Subnet Mask | 255.255.255.0 |
| Gateway | 192.168.100.1 |
| DNS Servers | 8.8.8.8, 8.8.4.4 |
| Firewall | pfSense 2.7 |
| Intrusion Detection System | Suricata |
Network monitoring is performed using Nagios. All external access to the AI servers is strictly controlled via a reverse proxy (see Reverse Proxy Configuration). Detailed network diagrams can be found in Network Topology Documentation. Regular security audits are performed by the Security Team.
Future Expansion
Planned future expansions include adding more GPU capacity and expanding the cloud integration to utilize serverless functions (see Serverless Computing Overview). We are also investigating the use of specialized AI accelerators (see AI Accelerator Research).
Server Monitoring Backup and Recovery Procedures Security Best Practices Troubleshooting Guide Contact 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.* ⚠️