AI in Guam
AI in Guam: Server Configuration and Deployment
This article details the server configuration for deploying Artificial Intelligence (AI) workloads in Guam. This guide is geared towards new system administrators and engineers tasked with setting up and maintaining these systems. We will cover hardware specifications, software stack, networking considerations, and security best practices. This deployment focuses on edge computing for localized AI processing, minimizing latency and bandwidth requirements.
1. Hardware Infrastructure
The core of our AI infrastructure relies on a distributed server architecture. Due to Guam’s unique environmental factors (humidity, limited cooling capacity in some locations), we employ robust, energy-efficient server hardware.
| Component | Specification | Quantity |
|---|---|---|
| Server Type | High-Density GPU Server (Dell PowerEdge R750xa or equivalent) | 8 |
| CPU | Intel Xeon Gold 6338 (32 cores, 2.0 GHz) | 8 per server |
| GPU | NVIDIA A100 80GB PCIe 4.0 | 4 per server |
| RAM | 512GB DDR4 ECC Registered 3200MHz | 8 x 64GB DIMMs per server |
| Storage (OS) | 1TB NVMe SSD (PCIe 4.0) | 1 per server |
| Storage (Data) | 16TB SAS HDD (7.2k RPM) in RAID 6 | 2 per server |
| Network Interface | Dual 100GbE Network Interface Cards (NICs) | 2 per server |
| Power Supply | Redundant 1100W Platinum Power Supplies | 2 per server |
These servers are housed in a Tier 3 data center facility in Hagatna. The data center provides redundant power, cooling, and network connectivity. Detailed information on the data center’s infrastructure can be found on the Data Center Specifications page.
2. Software Stack
The software stack is crucial for enabling AI workloads. We utilize a containerized environment for portability and scalability. The primary operating system is Ubuntu Server 22.04 LTS.
| Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers |
| Containerization Platform | Docker 20.10 | Packaging and running AI applications |
| Container Orchestration | Kubernetes 1.25 | Managing and scaling containerized applications |
| AI Framework | TensorFlow 2.12 | Deep learning framework |
| AI Framework | PyTorch 2.0 | Deep learning framework |
| Programming Language | Python 3.10 | Primary language for AI development |
| Monitoring System | Prometheus 2.40 | System and application monitoring |
| Visualization Tool | Grafana 9.0 | Visualizing monitoring data |
The AI models are developed using Python and deployed as Docker containers managed by Kubernetes. Model Deployment Procedures outlines the detailed steps for deploying new AI models. We leverage NVIDIA Triton Inference Server for optimized model serving. The Software Licensing page details all software licenses.
3. Networking Configuration
Network configuration is vital for low-latency communication between servers and external clients. We utilize a dedicated VLAN for AI traffic.
| Network Component | Configuration | Notes |
|---|---|---|
| VLAN ID | 100 | Dedicated VLAN for AI traffic |
| IP Address Range | 192.168.100.0/24 | Static IP addresses assigned to each server |
| DNS Servers | 8.8.8.8, 8.8.4.4 | Google Public DNS |
| Gateway | 192.168.100.1 | Default gateway for the VLAN |
| Firewall | iptables | Configured to allow only necessary traffic |
| Load Balancer | HAProxy | Distributes traffic across servers |
The network topology is a full mesh, providing redundancy and minimizing latency. See the Network Diagram for a visual representation. Firewall Rules details the specific firewall configuration. All network traffic is monitored using Nagios.
4. Security Considerations
Security is paramount, especially when dealing with sensitive data. We implement a multi-layered security approach.
- **Access Control:** Role-Based Access Control (RBAC) is implemented using Kubernetes RBAC.
- **Encryption:** All data at rest and in transit is encrypted using TLS 1.3.
- **Intrusion Detection:** An Intrusion Detection System (IDS) is deployed using Snort.
- **Regular Security Audits:** We conduct regular security audits as detailed in the Security Audit Schedule.
- **Vulnerability Scanning:** Automated vulnerability scanning is performed weekly using Nessus.
5. Future Scalability
The infrastructure is designed for scalability. Additional servers can be added to the Kubernetes cluster as needed. We are also exploring the use of serverless computing for certain AI workloads. The Capacity Planning Guide provides detailed guidance on scaling the infrastructure. Consideration is being given to utilizing edge computing nodes closer to data sources.
Server Maintenance procedures are regularly updated. For troubleshooting common issues, refer to the Troubleshooting Guide.
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.* ⚠️