AI in Chad
AI in Chad: Server Configuration and Deployment
This article details the server configuration required to support Artificial Intelligence (AI) workloads within the Chad data center. It's aimed at newcomers to our MediaWiki site and provides a technical overview of the hardware and software needed for successful AI deployment. This is an evolving landscape, and this document will be updated as our needs change. Please also refer to the Data Center Standards and Security Protocols for overarching guidelines.
Overview
The deployment of AI services in Chad presents unique challenges due to limited infrastructure and environmental constraints. We've focused on a scalable, resilient, and energy-efficient solution leveraging a hybrid cloud approach, with a primary on-premise cluster supplemented by cloud bursting capabilities via Cloud Provider Integration. Initial AI applications will focus on Agricultural Optimization, Healthcare Diagnostics, and Resource Management. This infrastructure needs to be robust enough to handle the computational demands of Machine Learning Models and the storage requirements of large datasets.
Hardware Configuration
The core of our AI infrastructure is a cluster of dedicated servers located in the Chad data center. These servers are specifically chosen to balance performance, reliability, and power efficiency. Below is a detailed breakdown of the server specifications:
| Component | Specification | Quantity |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores per CPU) | 8 |
| RAM | 512 GB DDR4 ECC Registered | 8 |
| Storage (OS/Boot) | 1 TB NVMe SSD | 8 |
| Storage (Data) | 16 x 8TB SAS HDD (RAID 6) | 2 Arrays |
| GPU | 4 x NVIDIA A100 80GB | 8 |
| Network Interface | Dual 100GbE Ethernet | 8 |
| Power Supply | Redundant 2000W Platinum PSUs | 8 |
This configuration provides substantial processing power and storage capacity. The use of RAID 6 ensures data redundancy and protects against drive failures. The high-speed NVMe SSDs are crucial for fast operating system and application loading times. The NVIDIA A100 GPUs are essential for accelerating machine learning tasks. For more information on our storage solutions, see Storage Architecture.
Software Stack
The software stack is designed for flexibility and ease of management. We utilize a Linux-based operating system and a containerization platform for application deployment.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| Containerization | Docker 20.10 | Application packaging and deployment |
| Orchestration | Kubernetes 1.24 | Container orchestration and scaling |
| Machine Learning Framework | TensorFlow 2.10 / PyTorch 1.12 | AI model development and training |
| Data Science Tools | Jupyter Notebook, Pandas, NumPy | Data analysis and manipulation |
| Monitoring | Prometheus & Grafana | System and application monitoring |
| Logging | ELK Stack (Elasticsearch, Logstash, Kibana) | Log aggregation and analysis |
We also employ a robust version control system using Git Repository Management. The choice of TensorFlow and PyTorch allows for compatibility with a wide range of AI models. Kubernetes simplifies deployment, scaling, and management of AI applications. See Software Licensing Procedures for details on licensing.
Network Infrastructure
The network infrastructure is critical for connecting the AI servers to each other, to the data storage systems, and to the external network.
| Component | Specification | Notes |
|---|---|---|
| Core Switches | Cisco Catalyst 9500 Series | High-bandwidth, low-latency switching |
| Interconnect | 100GbE Fiber Optic | Connects servers and storage arrays |
| Firewall | Palo Alto Networks PA-820 | Network security and access control |
| Load Balancer | HAProxy | Distributes traffic across servers |
| DNS | Bind9 | Domain name resolution |
All network traffic is secured using Network Security Best Practices. Redundancy is built into the network design to ensure high availability. Further details on network topology are available in the Network Diagram Documentation.
Future Considerations
As our AI initiatives grow, we anticipate the need for additional resources. Future upgrades will likely include:
- Increased GPU capacity.
- Expansion of the storage infrastructure.
- Implementation of a dedicated AI model serving platform.
- Integration with more cloud services.
- Exploration of specialized AI hardware accelerators (e.g., TPUs).
Refer to the Capacity Planning Documentation for long-term infrastructure projections.
AI Ethics Policy is also a critical component of our deployment strategy.
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.* ⚠️