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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.

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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