AI in Catalonia
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AI in Catalonia: Server Configuration Overview
This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives within Catalonia. It's intended for new system administrators and developers contributing to these projects. Understanding the underlying hardware and software is crucial for effective development and maintenance. This document will cover hardware specifications, software stack, networking, and security considerations.
Hardware Infrastructure
The core of the AI infrastructure resides within a dedicated data center located near Barcelona. This data center houses a cluster of servers designed for high-performance computing and machine learning tasks. Redundancy and scalability are key design principles.
| Server Role | Model | CPU | RAM | Storage | GPU |
|---|---|---|---|---|---|
| Compute Node 1-10 | Dell PowerEdge R750xa | 2 x AMD EPYC 7763 (64 cores/128 threads each) | 512 GB DDR4 ECC REG | 8 x 4TB NVMe SSD (RAID 0) | 4 x NVIDIA A100 (80GB) |
| Storage Node 1-3 | HPE ProLiant DL380 Gen10 | 2 x Intel Xeon Gold 6338 (32 cores/64 threads each) | 256 GB DDR4 ECC REG | 24 x 16TB SAS HDD (RAID 6) | |
| Management Node | Supermicro SuperServer 1U | 2 x Intel Xeon Silver 4310 (12 cores/24 threads each) | 64 GB DDR4 ECC REG | 2 x 1TB NVMe SSD (RAID 1) | N/A |
The network infrastructure utilizes a 100GbE backbone for high-speed data transfer between nodes. A dedicated InfiniBand network is also available for particularly demanding workloads, offering lower latency. See Network Topology for detailed diagrams.
Software Stack
The servers operate under a Linux distribution, specifically Ubuntu Server 22.04 LTS. This provides a stable and well-supported base for the AI software stack. Containerization using Docker and orchestration with Kubernetes are employed to manage application deployments and scalability.
| Software Component | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| Container Runtime | Docker 24.0.5 | Containerization platform |
| Orchestration | Kubernetes 1.27 | Container orchestration |
| Machine Learning Frameworks | TensorFlow 2.12, PyTorch 2.0, Scikit-learn 1.2 | Core AI/ML libraries |
| Data Storage | Ceph Octopus | Distributed storage system |
Access to the cluster is managed through SSH and a web-based dashboard built using Flask. Version control is handled via Git and hosted on a private GitLab instance. All code is subject to rigorous Code Review processes.
Networking Configuration
The network is segmented into several VLANs to enhance security and isolate different workloads. The main VLANs are:
- VLAN 10: Management Network
- VLAN 20: Compute Network
- VLAN 30: Storage Network
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Each server has multiple network interfaces, allowing for redundancy and increased bandwidth. A Load Balancer distributes traffic across the compute nodes. DNS resolution is provided by internal BIND9 servers. Firewall rules are managed using iptables.
| Network Segment | VLAN ID | IP Range | Description |
|---|---|---|---|
| Management | 10 | 192.168.10.0/24 | Server administration and monitoring |
| Compute | 20 | 10.0.20.0/16 | AI/ML workloads |
| Storage | 30 | 10.0.30.0/16 | Data storage and access |
Security Considerations
Security is paramount. All servers are protected by a hardware firewall. Regular security audits are conducted. Access control is strictly enforced using LDAP for user authentication. Data at rest is encrypted using LUKS and data in transit is encrypted using TLS. Intrusion detection and prevention systems (IDS/IPS) are deployed to monitor network traffic for malicious activity. See Security Protocols for more details on implemented security measures. Regular Backup and Recovery procedures are in place to mitigate data loss. We also utilize Two-Factor Authentication for all administrative access.
Future Expansion
Planned expansions include the addition of more GPU-accelerated servers and an upgrade to a 400GbE network backbone. We are also investigating the use of Federated Learning techniques to distribute AI workloads across multiple locations.
Data Privacy is a critical consideration in all future development.
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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.* ⚠️