AI in Slovenia
- AI in Slovenia: A Server Configuration Overview
This article provides a technical overview of server infrastructure suitable for supporting Artificial Intelligence (AI) development and deployment within Slovenia. It is aimed at newcomers to our MediaWiki site and details hardware, software, and networking considerations. We will focus on a scalable architecture capable of handling various AI workloads, from model training to inference serving. This document assumes familiarity with basic server administration concepts.
Overview
Slovenia is experiencing growing interest in AI across various sectors, including manufacturing, healthcare, and finance. A robust server infrastructure is crucial to support this growth. This guide outlines a potential configuration, emphasizing scalability, reliability, and cost-effectiveness. We will cover the core components needed to establish a functional AI server environment, including hardware specifications, software choices, and networking requirements. Consider referencing our Server Room Best Practices document for physical infrastructure guidelines.
Hardware Specifications
The foundation of any AI system is the underlying hardware. The requirements vary based on the specific AI tasks. For model training, powerful GPUs are essential, while inference serving can often be handled by CPUs with sufficient memory. Here's a breakdown of suggested hardware components:
| Component | Specification | Quantity (Initial) | Estimated Cost (EUR) |
|---|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores/64 threads) | 2 | 8,000 |
| GPU | NVIDIA A100 80GB | 4 | 40,000 |
| RAM | 512GB DDR4 ECC REG | 2 | 4,000 |
| Storage (OS & Applications) | 2TB NVMe SSD | 2 | 800 |
| Storage (Data) | 100TB SAS HDD (RAID 6) | 1 Array | 6,000 |
| Network Interface Card | 100GbE | 2 | 1,200 |
| Power Supply | 2000W Redundant | 2 | 1,000 |
These specifications are a starting point and should be adjusted based on anticipated workload. See our Hardware Procurement Policy for details on approved vendors. Remember to account for power and cooling requirements; consult the Data Center Cooling Guide.
Software Stack
The software stack is equally important. We recommend a Linux-based operating system for its flexibility and strong support for AI frameworks.
| Component | Version | Description |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Stable and widely supported Linux distribution. |
| Containerization | Docker 24.0.5 | For packaging and deploying AI models. |
| Orchestration | Kubernetes 1.27 | Managing and scaling containerized applications. See Kubernetes Deployment Guide. |
| AI Frameworks | TensorFlow 2.13, PyTorch 2.0, scikit-learn 1.3 | Popular AI frameworks for model development. |
| Data Science Libraries | Pandas, NumPy, Matplotlib | Essential libraries for data manipulation and visualization. |
| Database | PostgreSQL 15 | For storing and managing AI-related data. Refer to Database Administration for details. |
| Monitoring | Prometheus & Grafana | Monitoring server performance and AI model metrics. |
This stack provides a solid foundation for building and deploying AI applications. Security is paramount; review our Server Security Checklist before deployment.
Networking Configuration
A high-bandwidth, low-latency network is critical for AI workloads, especially when dealing with large datasets and distributed training.
| Component | Specification | Notes |
|---|---|---|
| Network Topology | Spine-Leaf Architecture | Provides high bandwidth and low latency. |
| Inter-Server Connectivity | 100GbE | Essential for fast data transfer between servers. |
| External Connectivity | 1GbE with Redundancy | For access from external networks. |
| Firewall | pfSense 2.7 | Robust firewall for network security. See Firewall Configuration for details. |
| Load Balancing | HAProxy | Distributing traffic across multiple servers. |
| DNS | Bind9 | Reliable DNS server for name resolution. |
Proper network segmentation and security policies are crucial. Consult our Network Security Policy for more information. Consider using a Virtual Private Cloud (VPC) if deploying to a cloud provider such as Amazon Web Services or Microsoft Azure.
Scalability and Future Considerations
The architecture outlined above is designed to be scalable. Adding more GPUs, increasing RAM, or expanding storage capacity can be done relatively easily. Kubernetes simplifies the deployment and management of additional resources. Future considerations include:
- **Specialized Hardware:** Exploring the use of TPUs (Tensor Processing Units) for specific AI workloads.
- **Distributed Training:** Implementing distributed training techniques to accelerate model training.
- **Edge Computing:** Deploying AI models to edge devices for real-time inference. See Edge Computing Strategy.
- **Data Governance:** Establishing robust data governance policies to ensure data quality and compliance. Refer to Data Governance Framework.
- **Regular Security Audits:** Conducting regular security audits to identify and address potential vulnerabilities.
Related Articles
- Server Room Best Practices
- Hardware Procurement Policy
- Data Center Cooling Guide
- Kubernetes Deployment Guide
- Database Administration
- Server Security Checklist
- Firewall Configuration
- Network Security Policy
- Amazon Web Services
- Microsoft Azure
- Edge Computing Strategy
- Data Governance Framework
- Monitoring and Alerting System
- Disaster Recovery Plan
- Backup and Restore Procedures
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.* ⚠️