AI in Sint Maarten
- AI in Sint Maarten: Server Configuration and Deployment
This article details the server configuration for deploying Artificial Intelligence (AI) applications within Sint Maarten. It is geared towards newcomers to our MediaWiki site and provides a detailed technical overview. We will cover hardware, software, networking, and security considerations. This setup aims to provide a robust and scalable foundation for various AI workloads, from machine learning model training to real-time inference. See also Server Room Setup and Disaster Recovery Planning.
Hardware Specifications
The core of our AI infrastructure relies on a cluster of high-performance servers. Below are the specifications for each node in the cluster. We utilize a hybrid approach, combining on-premise servers with cloud resources for scalability. Refer to Cloud Integration Guide for details on cloud connectivity.
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores/64 threads per CPU) |
| RAM | 512 GB DDR4 ECC Registered RAM (3200 MHz) |
| Storage (OS) | 1 TB NVMe SSD (PCIe Gen4) |
| Storage (Data) | 16 TB NVMe SSD RAID 0 Array (PCIe Gen4) |
| GPU | 4 x NVIDIA A100 (80GB HBM2e) |
| Network Interface | Dual 100 GbE Network Adapters |
| Power Supply | 2 x 1600W Redundant Power Supplies |
We also maintain a separate set of edge servers for localized AI processing. These servers have reduced specifications to manage cost and power consumption, but are crucial for low-latency applications. See Edge Computing Architecture for further explanation.
Software Stack
The software stack is designed for flexibility and ease of management. We leverage containerization for application deployment and orchestration. Understanding Docker Fundamentals is recommended before proceeding.
| Software Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers |
| Containerization | Docker 24.0.5 | Application packaging and isolation |
| Orchestration | Kubernetes 1.27 | Container deployment, scaling, and management |
| AI Frameworks | TensorFlow 2.13, PyTorch 2.0, scikit-learn 1.3 | Machine learning and deep learning frameworks |
| Data Storage | PostgreSQL 15 | Relational database for metadata and model management |
| Monitoring | Prometheus 2.47, Grafana 9.5 | System and application monitoring |
| Logging | ELK Stack (Elasticsearch, Logstash, Kibana) 8.11 | Centralized logging and analysis |
We have standardized on Python 3.10 for AI development and deployment. Refer to the Python Best Practices document for coding standards. We also utilize a version control system (Git) and a CI/CD pipeline for automated deployments. Consult the Git Workflow Guide for details.
Networking Configuration
The network infrastructure is critical for ensuring high bandwidth and low latency between servers. We utilize a dedicated VLAN for AI traffic. Detailed information can be found in the Network VLAN Configuration document.
| Network Element | Configuration | |
|---|---|---|
| VLAN ID | 100 | |
| Subnet | 192.168.100.0/16 | |
| Gateway | 192.168.100.1 | |
| DNS Servers | 8.8.8.8, 8.8.4.4 | |
| Firewall Rules | Allow traffic within VLAN 100, restrict external access except for specific ports (e.g., SSH, HTTPS) | |
| Load Balancer | HAProxy | Distributes traffic across server nodes. See HAProxy Configuration |
Furthermore, we implement network segmentation to isolate the AI infrastructure from other parts of the network, enhancing security. See Network Segmentation Policy for details. All network traffic is monitored using intrusion detection systems.
Security Considerations
Security is paramount. We employ a multi-layered security approach to protect the AI infrastructure. Regular security audits are conducted. Review the Security Audit Schedule.
- **Access Control:** Strict role-based access control (RBAC) is implemented using Kubernetes RBAC and Linux user permissions.
- **Data Encryption:** Data at rest and in transit is encrypted using TLS/SSL and AES-256 encryption.
- **Firewall:** A robust firewall protects the network perimeter and internal segments.
- **Intrusion Detection:** Intrusion detection systems (IDS) monitor network traffic for malicious activity.
- **Vulnerability Scanning:** Regular vulnerability scans are performed to identify and address security weaknesses.
- **Regular Updates:** All software is kept up-to-date with the latest security patches. See Patch Management Policy.
- **Data Backup:** Regularly scheduled backups are performed to ensure data recovery in case of a disaster. Refer to Backup and Restore Procedures.
We also implement data anonymization and privacy-preserving techniques to protect sensitive data. See Data Privacy Guidelines.
Future Expansion
We plan to expand the AI infrastructure by incorporating more edge servers and leveraging cloud-based AI services. We are also exploring the use of specialized AI accelerators, such as TPUs. See Future Infrastructure Roadmap. We are also investigating Federated Learning techniques to improve model accuracy and privacy.
Server Monitoring Dashboard provides real-time insights into the server performance.
Contact IT Support for any questions or assistance.
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.* ⚠️