AI in Latvia
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- AI in Latvia: Server Configuration and Landscape
This article details the current server configuration landscape supporting Artificial Intelligence (AI) initiatives within Latvia. It is intended as a technical overview for newcomers contributing to the MediaWiki infrastructure supporting these projects. The information presented is current as of late 2023 and early 2024. Please refer to External Resources for the most up-to-date details.
Overview
Latvia is experiencing growing interest and investment in AI, particularly within sectors like fintech, healthcare, and logistics. This has necessitated a robust and scalable server infrastructure. Currently, the deployment model is a hybrid approach, leveraging both on-premise data centers and cloud services, primarily from providers operating within the European Union. Security and data sovereignty are key considerations, driving a preference for EU-based infrastructure. The Latvian IT Infrastructure Agency plays a critical role in coordinating infrastructure development.
On-Premise Data Center Specifications
Several key research institutions and companies maintain on-premise data centers. These centers generally prioritize high-performance computing (HPC) capabilities to support machine learning (ML) model training and deployment. The following table details the typical specifications of a medium-sized on-premise AI server cluster:
| Component | Specification | Quantity (per cluster) |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores/64 threads per CPU) | 16 |
| RAM | 512 GB DDR4 ECC Registered 3200MHz | 16 |
| GPU | NVIDIA A100 80GB | 8 |
| Storage (OS & Applications) | 2 x 1TB NVMe PCIe Gen4 SSD (RAID 1) | 16 |
| Storage (Data) | 10 x 16TB SAS HDD (RAID 6) | 1 |
| Network Interface | 100Gbps Ethernet | 2 |
| Power Supply | 2000W Redundant Power Supplies | 2 |
These servers typically run Linux, often a distribution like Ubuntu Server or CentOS, chosen for their stability and broad software support. Containerization technologies like Docker and orchestration platforms like Kubernetes are widely used for application deployment and management. Monitoring Systems such as Prometheus and Grafana are also essential for maintaining optimal performance.
Cloud Service Provider Landscape
While on-premise infrastructure handles sensitive data and specialized workloads, cloud services provide scalability and cost-effectiveness. The most commonly used providers in Latvia include:
| Provider | Services Utilized | Region | Compliance |
|---|---|---|---|
| Amazon Web Services (AWS) | EC2, S3, SageMaker, Lambda | Frankfurt (EU-Central-1) | GDPR, ISO 27001 |
| Microsoft Azure | Virtual Machines, Blob Storage, Azure Machine Learning, Azure Functions | West Europe (EU-West) | GDPR, ISO 27001 |
| Google Cloud Platform (GCP) | Compute Engine, Cloud Storage, Vertex AI, Cloud Functions | Frankfurt (EU-West) | GDPR, ISO 27001 |
The choice of provider often depends on the specific AI framework being used. For example, TensorFlow integrates well with GCP, while PyTorch has strong support across all three major providers. Virtualization is a core component of cloud deployments, allowing for rapid scaling and resource allocation.
Networking and Security Considerations
The network infrastructure supporting AI applications in Latvia is undergoing significant upgrades to handle the increased bandwidth demands of data transfer and model deployment. 5G connectivity is expanding, providing faster and more reliable wireless access.
Security is paramount. All data centers and cloud deployments adhere to the General Data Protection Regulation (GDPR). Key security measures include:
- Firewalls: Implementing robust firewall rules to control network access.
- Intrusion Detection/Prevention Systems (IDS/IPS): Monitoring network traffic for malicious activity.
- Data Encryption: Encrypting data both in transit and at rest.
- Access Control: Implementing strict access control policies to limit access to sensitive data.
- Regular Security Audits: Conducting regular security audits to identify and address vulnerabilities.
The following table summarizes common security protocols utilized:
| Protocol | Purpose | Implementation |
|---|---|---|
| TLS 1.3 | Secure communication between clients and servers | Standard across all cloud providers and on-premise deployments |
| SSH | Secure remote access to servers | Enabled with key-based authentication |
| VPN | Secure connection to internal networks | Utilized for remote access by researchers and developers |
| Two-Factor Authentication (2FA) | Enhanced user authentication | Enforced for all administrator accounts |
Future Trends
Future development will likely focus on:
- Edge Computing: Deploying AI models closer to the data source to reduce latency.
- Federated Learning: Training AI models on decentralized data without sharing the data itself.
- Specialized Hardware: Adopting specialized AI accelerators like TPUs (Tensor Processing Units) to improve performance.
- Increased investment in Data Storage Solutions to meet growing data needs.
- Further integration with the National Research and Education Network (NREN).
External Resources
- [Latvian IT Infrastructure Agency](https://www.liaa.lv/)
- [GDPR official website](https://gdpr-info.eu/)
Main Page AI Algorithms Data Science Machine Learning Deep Learning Cloud Computing Network Security Database Management Software Development System Administration Hardware Maintenance Virtual Machines Containerization Monitoring Systems Linux Operating System Kubernetes Data Centers 5G Technology National Research and Education Network Data Storage Solutions Virtualization Software Frameworks Security Protocols External Resources
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.* ⚠️