AI in Azerbaijan
AI in Azerbaijan: A Server Configuration Overview
This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives within Azerbaijan. It is intended as a guide for new system administrators and developers contributing to these projects. Understanding the underlying hardware and software is crucial for optimal performance and scalability. This document focuses on the core server components, network architecture, and software stack employed.
1. Introduction to AI Deployment in Azerbaijan
Azerbaijan is increasingly investing in AI across various sectors, including agriculture, energy, healthcare, and education. The national strategy focuses on developing local expertise and infrastructure. This requires robust server infrastructure capable of handling large datasets, complex calculations, and real-time processing. The current configuration leverages a hybrid approach, utilizing both on-premise data centers and cloud resources. This allows for flexibility and cost optimization. Data Center Design is a critical consideration.
2. On-Premise Server Infrastructure
The primary on-premise data center, located in Baku, houses the core AI processing capabilities. It's designed for high availability and redundancy.
2.1. Compute Servers
These servers are the workhorses of the AI infrastructure, responsible for training and deploying machine learning models.
| Server Type | CPU | RAM | GPU | Storage |
|---|---|---|---|---|
| Intel Xeon Gold 6248R @ 3.0 GHz | 256 GB DDR4 ECC | NVIDIA Tesla V100 (32GB) | 4 x 4TB NVMe SSD (RAID 0) | ||||
| AMD EPYC 7763 (64 Core) | 512 GB DDR4 ECC | NVIDIA A100 (80GB) | 8 x 8TB NVMe SSD (RAID 0) | ||||
| Intel Xeon Platinum 8280 | 128 GB DDR4 ECC | NVIDIA Tesla P40 (24GB) | 2 x 4TB NVMe SSD (RAID 1) |
These servers operate under a Linux distribution (CentOS 8) and utilize containerization technologies like Docker and Kubernetes for efficient resource management. Resource Allocation is carefully monitored.
2.2. Storage Servers
High-capacity storage is essential for handling the large datasets used in AI training.
| Server Type | Storage Capacity | RAID Level | Network Interface |
|---|---|---|---|
| 500 TB | RAID 6 | 100 GbE | |
| 1 PB | RAID 6 | 100 GbE | |
| 200 TB | RAID 5 | 40 GbE |
The storage servers utilize a distributed file system, specifically Hadoop Distributed File System (HDFS), to ensure scalability and fault tolerance. Data Backup Strategies are implemented regularly.
2.3. Network Infrastructure
A high-bandwidth, low-latency network is critical for communication between servers.
| Component | Specification |
|---|---|
| Cisco Nexus 9508 | |
| Cisco Catalyst 9300 Series | |
| 100 GbE Fiber Optic | |
| Fortinet FortiGate 600E |
Network segmentation and security protocols, detailed in the Network Security Policy, are implemented to protect sensitive data.
3. Cloud Integration
Supplementing the on-premise infrastructure, cloud resources are leveraged for burst capacity and specific AI services. Cloud Computing Basics are essential knowledge.
3.1. Cloud Provider
Amazon Web Services (AWS) is the primary cloud provider, utilizing services such as:
- Amazon EC2 for on-demand compute instances.
- Amazon S3 for object storage.
- Amazon SageMaker for machine learning model building and deployment.
3.2. Hybrid Cloud Architecture
A secure VPN connection and dedicated direct connect links ensure secure and reliable communication between the on-premise data center and AWS. VPN Configuration is documented separately.
4. Software Stack
The software stack comprises various open-source and commercial tools.
- **Operating System:** CentOS 8, Ubuntu Server 20.04
- **Programming Languages:** Python 3.8, R 4.0
- **Machine Learning Frameworks:** TensorFlow 2.x, PyTorch 1.x, scikit-learn
- **Data Science Tools:** Jupyter Notebook, RStudio
- **Database:** PostgreSQL 13, MongoDB 4.4
- **Big Data Tools:** Hadoop, Spark, Kafka
Regular software updates and patch management, as per the Software Update Policy, are critical for security and stability.
5. Monitoring and Management
Comprehensive monitoring and management tools are employed to ensure optimal performance and identify potential issues. Server Monitoring Tools are crucial for proactive maintenance.
- **Prometheus:** For time-series data collection and alerting.
- **Grafana:** For data visualization and dashboarding.
- **Nagios:** For system and network monitoring.
- **Ansible:** For configuration management and automation.
6. Future Considerations
Future expansion plans include upgrading to newer generation hardware, integrating additional cloud services, and adopting edge computing solutions for real-time AI processing closer to the data source. Edge Computing Principles will be a key focus.
Server Virtualization is also being considered for increased resource utilization.
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.* ⚠️