AI in Brunei
- AI in Brunei: Server Configuration and Considerations
This article details the server configuration required to support Artificial Intelligence (AI) initiatives within Brunei. It is aimed at newcomers to our MediaWiki site and assumes a basic understanding of server infrastructure. We will cover hardware, software, networking, and security considerations.
Introduction
The adoption of AI in Brunei is rapidly increasing, spanning sectors like finance, healthcare, and government services. This necessitates robust and scalable server infrastructure. Deploying AI solutions demands significantly more computational power than traditional applications due to the intensive nature of machine learning algorithms. This document outlines the minimum and recommended specifications for servers supporting AI workloads within our Brunei-based infrastructure. Understanding the interplay between hardware and software, and ensuring data security, are paramount. See also Server Room Best Practices and Data Center Cooling Systems.
Hardware Specifications
The server hardware forms the foundation of any AI infrastructure. The following table details the minimum and recommended specifications. We prioritize GPU acceleration for most AI tasks.
| Specification | Minimum Requirement | Recommended Requirement |
|---|---|---|
| Processor | Intel Xeon Silver 4210 or AMD EPYC 7262 | Intel Xeon Gold 6248R or AMD EPYC 7763 |
| RAM | 64GB DDR4 ECC Registered | 256GB DDR4 ECC Registered |
| Storage (OS) | 500GB NVMe SSD | 1TB NVMe SSD |
| Storage (Data) | 4TB HDD (RAID 5) | 16TB HDD (RAID 6) or 8TB NVMe SSD (RAID 1) |
| GPU | NVIDIA Tesla T4 (16GB) | NVIDIA A100 (80GB) or equivalent AMD Instinct MI250X |
| Network Interface | 10GbE | 40GbE or 100GbE |
| Power Supply | 750W Redundant | 1200W Redundant |
Consider using blade servers for space efficiency and centralized management. Refer to Blade Server Configuration for more details. Ensure compatibility with existing Power Distribution Units and Uninterruptible Power Supplies.
Software Stack
The software stack is crucial for managing and utilizing the hardware effectively. We primarily use Linux-based operating systems due to their flexibility and open-source nature.
| Component | Software | Version (as of 2024-02-29) |
|---|---|---|
| Operating System | Ubuntu Server | 22.04 LTS |
| Containerization | Docker | 23.0.6 |
| Orchestration | Kubernetes | 1.27 |
| Machine Learning Frameworks | TensorFlow, PyTorch | Latest Stable Releases |
| Data Science Tools | Jupyter Notebook, RStudio | Latest Stable Releases |
| Monitoring | Prometheus, Grafana | Latest Stable Releases |
Regular software updates are essential for security and performance. See Patch Management Procedures for details. We encourage the use of containerization for application isolation and portability. Detailed instructions on installing Kubernetes on Ubuntu are available.
Networking Configuration
A high-bandwidth, low-latency network is critical for AI workloads, especially when dealing with large datasets or distributed training.
| Network Component | Configuration Details |
|---|---|
| Network Topology | Spine-Leaf Architecture |
| Switching | 100GbE Switches (Cisco Nexus or Arista) |
| Interconnect | RDMA over Converged Ethernet (RoCEv2) |
| VLANs | Separate VLANs for Management, Data, and AI workloads |
| Firewall | Next-Generation Firewall with Deep Packet Inspection |
| Load Balancing | HAProxy or Nginx Plus |
Network segmentation is crucial for security. Implement robust access control lists (ACLs) to restrict access to sensitive data. Review Network Security Best Practices for further guidance. Ensure proper configuration of DNS Servers for internal resolution.
Security Considerations
Security is paramount, especially when handling sensitive data used in AI models.
- **Data Encryption:** Encrypt data at rest and in transit. Use tools like LUKS for disk encryption and TLS/SSL for network communication.
- **Access Control:** Implement Role-Based Access Control (RBAC) to restrict access to data and resources based on user roles.
- **Vulnerability Management:** Regularly scan for vulnerabilities and apply security patches promptly. See Vulnerability Scanning Procedures.
- **Intrusion Detection/Prevention:** Deploy intrusion detection and prevention systems (IDS/IPS) to monitor network traffic for malicious activity.
- **Data Privacy:** Adhere to relevant data privacy regulations (e.g., PDPA).
- **Regular Audits:** Conduct regular security audits to identify and address potential vulnerabilities.
Refer to the Security Policy Document for comprehensive security guidelines.
Future Scalability
Plan for future scalability by choosing hardware and software that can be easily upgraded or expanded. Consider using a cloud-based infrastructure for increased flexibility and scalability. Explore options like Cloud Provider Integration for hybrid deployments. We also need to consider Server Virtualization Techniques for efficient resource allocation.
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.* ⚠️