AI in Bermuda
AI in Bermuda: Server Configuration
Welcome to the documentation for the "AI in Bermuda" server infrastructure. This article details the hardware and software configuration powering our artificial intelligence initiatives. This guide is intended for new system administrators and developers joining the project. It covers the core components, networking, and software stack. Please familiarize yourself with these details before making any changes to the system. This server environment supports both training and inference workloads for various AI models, with a focus on machine learning and deep learning.
Overview
The "AI in Bermuda" project utilizes a distributed server architecture to handle the computational demands of modern AI. The core infrastructure consists of multiple server nodes, interconnected via a high-speed network. We employ a hybrid cloud approach, leveraging both on-premise hardware and cloud resources for scalability and cost optimization. The primary goal is to provide a robust and reliable platform for AI research and development. Understanding the server architecture is crucial for effective maintenance and troubleshooting.
Hardware Specifications
The server nodes are built using high-performance components optimized for AI workloads. The following table summarizes the key specifications:
| Component | Specification | |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores per CPU) | |
| RAM | 512 GB DDR4 ECC Registered | |
| GPU | 8 x NVIDIA A100 (80GB HBM2e) | |
| Storage | 2 x 8 TB NVMe SSD (RAID 1) for OS and applications | 16 x 18 TB SAS HDD (RAID 6) for data storage |
| Network Interface | 2 x 100 GbE Mellanox ConnectX-6 | |
| Power Supply | 2 x 2000W Redundant Power Supplies |
These specifications ensure sufficient processing power, memory, and storage capacity for demanding AI tasks. Regular hardware monitoring is essential to prevent failures and maintain optimal performance.
Networking Configuration
The network infrastructure is designed for high bandwidth and low latency communication between server nodes. We utilize a dedicated VLAN for AI traffic, isolated from other network segments. The network topology is a full mesh, providing multiple paths for data transmission.
| Network Parameter | Value |
|---|---|
| Network Topology | Full Mesh |
| VLAN ID | 100 |
| IP Address Range | 192.168.100.0/16 |
| DNS Servers | 192.168.100.1, 192.168.100.2 |
| Gateway | 192.168.100.254 |
| Network Monitoring | Nagios, Prometheus |
Proper network configuration is vital for efficient data transfer and distributed training. We also employ firewall rules to secure the network.
Software Stack
The software stack is built on a foundation of Linux, with various AI frameworks and tools installed. We prioritize containerization for application deployment and management.
| Software Component | Version |
|---|---|
| Operating System | Ubuntu 22.04 LTS |
| Containerization | Docker 24.0.6, Kubernetes 1.27 |
| AI Frameworks | TensorFlow 2.13, PyTorch 2.0, JAX 0.4 |
| Data Science Libraries | NumPy, Pandas, Scikit-learn |
| Monitoring Tools | Prometheus, Grafana, ELK Stack |
| Version Control | Git |
We utilize Kubernetes for orchestrating containerized AI applications. Regular software updates and security patching are crucial for maintaining a secure and stable environment. The development workflow involves extensive testing and code review. We also leverage CI/CD pipelines for automated deployments. Familiarity with Linux administration is essential for managing the server environment. Understanding containerization concepts will greatly help in troubleshooting and deployment. Detailed documentation on data backup and recovery procedures is also available.
Security Considerations
Security is paramount in the "AI in Bermuda" project. We implement a multi-layered security approach, including:
- Firewall rules to restrict network access.
- Regular security audits to identify vulnerabilities.
- Data encryption to protect sensitive information.
- Access control mechanisms to limit user privileges.
- Intrusion detection systems to monitor for malicious activity.
Future Expansion
We are planning to expand the server infrastructure to accommodate future growth and increasing computational demands. This includes adding more server nodes, upgrading network bandwidth, and exploring new AI frameworks. We are also investigating the use of specialized hardware accelerators, such as TPUs, to further enhance performance.
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.* ⚠️