AI in the Bahamas
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AI in the Bahamas: Server Configuration Overview
This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives within the Bahamas. It’s intended for new system administrators and developers contributing to these projects. The Bahamas is rapidly developing its technological capabilities, and robust server infrastructure is crucial for supporting data-intensive AI workloads. This document focuses on the core components and their configurations. We'll cover hardware, software, networking, and security considerations. This guide assumes familiarity with basic Linux server administration and networking concepts.
Hardware Specifications
The foundation of our AI infrastructure lies in high-performance servers. We’ve standardized on a hybrid approach, utilizing both on-premise and cloud resources. On-premise servers are housed in a secure, climate-controlled data center in Nassau.
| Server Component | Specification | Quantity |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 Cores/64 Threads) | 12 |
| RAM | 256GB DDR4 ECC Registered 3200MHz | 12 |
| Storage (OS) | 1TB NVMe SSD | 12 |
| Storage (Data) | 8 x 16TB SAS HDD (RAID 6) | 4 Arrays |
| GPU | NVIDIA A100 (80GB) | 6 |
| Network Interface | Dual 100GbE QSFP28 | 12 |
These servers are interconnected via a dedicated high-speed network, detailed in the next section. We prioritize redundancy to ensure high availability; all critical components have hot-swappable replacements. See also Server Redundancy.
Networking Infrastructure
A robust network is paramount for efficient data transfer and communication between servers and external resources.
| Network Component | Specification | Quantity |
|---|---|---|
| Core Switch | Cisco Nexus 9516 | 2 |
| Distribution Switch | Cisco Catalyst 9300 | 4 |
| Network Protocol | TCP/IP v4 & v6 | N/A |
| Inter-Server Bandwidth | 100Gbps | N/A |
| Internet Connectivity | Redundant 10Gbps Fiber Optic Lines | 2 |
| DNS Servers | Internal BIND, Cloudflare DNS | N/A |
We employ a layered network architecture with a core, distribution, and access layer. Firewalls and intrusion detection systems (IDS) are implemented at multiple points to protect against unauthorized access. Refer to the Network Security Policy for detailed information. Furthermore, we utilize Virtual LANs (VLANs) to segment traffic and enhance security.
Software Stack
The software environment is carefully selected to support AI development and deployment. We primarily utilize Linux-based systems.
| Software Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Server Base |
| Containerization | Docker 20.10.7 | Application Packaging |
| Orchestration | Kubernetes 1.23 | Container Management |
| Programming Languages | Python 3.9, R 4.2.0 | AI Development |
| AI Frameworks | TensorFlow 2.8, PyTorch 1.12 | Machine Learning |
| Database | PostgreSQL 14 | Data Storage and Management |
All code is managed using Git version control, and continuous integration/continuous deployment (CI/CD) pipelines are in place to automate the software release process. We also utilize Monitoring Tools like Prometheus and Grafana to track server performance and identify potential issues. Regular Security Audits are conducted to ensure the integrity of the software stack. The choice of Ubuntu Server is based on its community support and wide availability of packages.
Security Considerations
Security is a top priority, especially given the sensitive nature of the data processed by AI systems. We implement a multi-layered security approach:
- **Firewalls:** We utilize both hardware and software firewalls to control network traffic.
- **Intrusion Detection/Prevention Systems (IDS/IPS):** These systems monitor network traffic for malicious activity.
- **Access Control:** Strict access control policies are enforced, limiting access to sensitive data and systems. We follow the principle of Least Privilege.
- **Data Encryption:** Data is encrypted both in transit and at rest.
- **Regular Security Audits:** We conduct regular security audits to identify and address vulnerabilities.
- **Vulnerability Scanning**: Automated tools continuously scan for known vulnerabilities.
Detailed security procedures are documented in the Security Incident Response Plan. All personnel are required to undergo regular security training.
Future Expansion
We are planning to expand our AI infrastructure in the coming months. This includes:
- Adding more GPU servers to increase processing power.
- Implementing a distributed storage system for large datasets.
- Exploring the use of Federated Learning to leverage data from multiple sources while preserving privacy. Refer to the documentation on Federated Learning Concepts.
- Integrating with cloud providers for scalability and disaster recovery.
Server Maintenance procedures are in place to ensure the ongoing stability and performance of the AI infrastructure. This document is subject to change as the infrastructure evolves. Please refer to the Wiki Home Page for the latest updates.
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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.* ⚠️