AI in Lithuania
- AI in Lithuania: A Server Configuration Overview
This article provides a technical overview of server configurations suitable for deploying and running Artificial Intelligence (AI) workloads within Lithuania, considering infrastructure availability, cost, and performance. It is targeted towards newcomers to our wiki and assumes basic familiarity with server administration concepts. This document will cover hardware, software, networking, and considerations for data residency.
1. Introduction to the Lithuanian AI Landscape
Lithuania is experiencing growing interest in AI, particularly in areas like fintech, cybersecurity, and smart city initiatives. This translates to a rising demand for robust and scalable server infrastructure. Key factors influencing server configuration choices include electricity costs (relatively low in Lithuania), access to skilled IT professionals, and increasing bandwidth availability. Data privacy regulations, aligning with GDPR, are also crucial. We will discuss how to configure servers to meet these needs. This article will not cover the *development* of AI models, but rather the infrastructure to *run* them. See Server Basics for a general overview of server infrastructure.
2. Hardware Considerations
Choosing the right hardware is paramount for AI workloads. The specific requirements depend heavily on the type of AI being deployed (e.g., machine learning training vs. inference). GPU acceleration is often essential.
2.1 Server Specifications for Machine Learning Training
Training large models requires significant computational power and memory. The following table outlines a suggested configuration:
| Component | Specification | Notes |
|---|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores/64 threads per CPU) | High core count is essential for data preprocessing. |
| RAM | 512 GB DDR4 ECC REG 3200MHz | Large models require substantial RAM. |
| GPU | 4 x NVIDIA A100 80GB | A100 GPUs provide excellent performance for training. Alternatives include H100. |
| Storage | 4 x 4TB NVMe PCIe Gen4 SSD (RAID 0) | Fast storage is crucial for loading datasets. |
| Network | 100 Gbps Ethernet | Required for fast data transfer. |
| Power Supply | 2000W Redundant Power Supplies | GPUs are power-hungry. |
2.2 Server Specifications for AI Inference
Inference, or deploying a trained model for predictions, is generally less resource-intensive than training.
| Component | Specification | Notes |
|---|---|---|
| CPU | Intel Xeon Silver 4310 (12 cores/24 threads) | Sufficient for most inference tasks. |
| RAM | 64 GB DDR4 ECC REG 3200MHz | Adequate for serving models. |
| GPU | 2 x NVIDIA T4 16GB | T4 GPUs offer a good balance of performance and cost for inference. |
| Storage | 1 x 1TB NVMe PCIe Gen4 SSD | Fast storage for model loading. |
| Network | 10 Gbps Ethernet | Sufficient for many inference applications. |
| Power Supply | 750W Redundant Power Supplies |
2.3 Server Rack Configuration
Servers should be housed in a secure data center.
| Item | Specification | Notes |
|---|---|---|
| Rack Unit (RU) | Standard 42U Rack | Provides space for multiple servers. |
| Power Distribution Units (PDUs) | Redundant PDUs with monitoring | Ensures reliable power delivery. |
| Cooling | Precision cooling system | Prevents overheating of servers. |
| Physical Security | Access control, surveillance | Protects against unauthorized access. |
3. Software Stack
The software stack is as important as the hardware.
3.1 Operating System
Ubuntu Server 22.04 LTS is a popular choice due to its strong community support and extensive package availability. Ubuntu Server Installation details the installation process.
3.2 Containerization
Docker and Kubernetes are essential for managing and scaling AI applications. Docker Basics and Kubernetes Overview provide introductory information. These tools enable efficient resource utilization and portability.
3.3 AI Frameworks
Popular AI frameworks include TensorFlow, PyTorch, and scikit-learn. Installation instructions can be found on their respective websites. Ensure that the correct CUDA and cuDNN versions are installed for GPU acceleration (see CUDA Installation).
3.4 Monitoring Tools
Prometheus and Grafana are effective tools for monitoring server performance and resource utilization. Prometheus Configuration and Grafana Setup offer guidance.
4. Networking and Data Residency
Lithuania adheres to GDPR regulations. Data must be processed and stored within the EU or in countries with adequate data protection standards.
- **Network Connectivity:** Utilize high-bandwidth connections to ensure fast data transfer.
- **Firewall:** Implement a robust firewall to protect against unauthorized access. Firewall Configuration
- **Data Encryption:** Encrypt data at rest and in transit.
- **Data Backup:** Implement a comprehensive data backup and recovery plan. See Disaster Recovery Planning.
5. Cost Considerations
Server costs in Lithuania can vary depending on the provider and configuration. Cloud providers (e.g., AWS, Azure, Google Cloud) offer flexible pricing models. On-premise solutions require upfront investment but may be more cost-effective in the long run. Consider the cost of electricity, cooling, and IT personnel. Refer to Cost Analysis.
6. Future Trends
The AI landscape is rapidly evolving. Future trends include:
- **Edge Computing:** Deploying AI models closer to the data source to reduce latency.
- **Quantum Computing:** Utilizing quantum computers for complex AI tasks.
- **Specialized AI Accelerators:** Adoption of new hardware architectures optimized for AI.
AI Security is also a growing concern.
Server Maintenance is a crucial ongoing task.
Troubleshooting Common Server Issues will help resolve problems.
Contact Support if you require further assistance.
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.* ⚠️