AI in Wolverhampton
- AI in Wolverhampton: Server Configuration & Deployment
This article details the server configuration deployed to support Artificial Intelligence (AI) initiatives within Wolverhampton. It's designed for newcomers to the MediaWiki platform and provides a technical overview of the hardware and software used. Understanding this setup is crucial for anyone contributing to or maintaining the AI infrastructure. Refer to the MediaWiki User's Guide for more information on editing this page.
Overview
The AI infrastructure in Wolverhampton is designed for scalability, reliability, and performance. It supports a range of AI applications, including machine learning model training, data analysis, and real-time inference. The core of the system comprises a cluster of high-performance servers, networked using a low-latency interconnect. This infrastructure leverages both on-premise resources and cloud services, as detailed in the Cloud Integration Policy. The system's architecture is based on principles of Distributed Computing and Microservices.
Hardware Specifications
The primary compute nodes are based on the following specifications:
| Component | Specification |
|---|---|
| CPU | Dual Intel Xeon Gold 6338 (32 cores per CPU) |
| RAM | 512 GB DDR4 ECC Registered RAM (3200 MHz) |
| GPU | 4 x NVIDIA A100 (80GB HBM2e) |
| Storage | 4 x 8TB NVMe SSD (RAID 0) for OS and temporary data |
| Network | 200 Gbps InfiniBand |
| Power Supply | 2 x 2000W Redundant Power Supplies |
These specifications were chosen after careful consideration of Performance Benchmarks and cost-benefit analysis. Redundancy is a key feature, ensuring high availability. Refer to the Hardware Procurement Guidelines for details on the procurement process.
Software Stack
The software stack is built around a Linux operating system and a suite of AI-focused tools. The primary operating system is Ubuntu Server 22.04 LTS.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base operating system |
| CUDA Toolkit | 12.2 | GPU-accelerated computing platform |
| cuDNN | 8.9.2 | Deep neural network library |
| TensorFlow | 2.13.0 | Machine learning framework |
| PyTorch | 2.0.1 | Machine learning framework |
| Kubernetes | 1.27 | Container orchestration system |
| Docker | 24.0.5 | Containerization platform |
The use of containers, managed by Kubernetes, allows for easy deployment and scaling of AI applications. See the Containerization Best Practices for detailed instructions. The software stack also integrates with our Data Storage Solutions for efficient data management.
Networking Configuration
The network is a critical component of the AI infrastructure. It's designed to provide low-latency, high-bandwidth connectivity between the compute nodes and the storage systems.
| Network Component | Specification |
|---|---|
| Interconnect | 200 Gbps InfiniBand |
| Switches | Mellanox Spectrum-2 |
| Network Topology | Fat-tree |
| Firewall | pfSense 2.7.2 |
| Load Balancer | HAProxy 2.6 |
The Fat-tree topology minimizes network congestion and ensures consistent performance. The firewall provides robust security, as outlined in the Network Security Policy. Detailed network diagrams can be found on the Network Documentation Page. Load balancing distributes traffic across multiple servers, enhancing reliability and responsiveness.
Monitoring and Logging
Comprehensive monitoring and logging are essential for maintaining the health and performance of the AI infrastructure. We utilize Prometheus for metrics collection and Grafana for visualization. Logs are aggregated using the ELK stack (Elasticsearch, Logstash, Kibana).
Please review the Monitoring and Alerting Procedures for information on how to respond to alerts. The Logging Standards document outlines the required log format and retention policies. Regularly reviewing the System Performance Reports is vital for identifying potential issues.
Future Expansion
Future expansion plans include adding more GPU nodes and increasing the storage capacity. We are also exploring the use of new AI accelerators, such as TPUs. The Capacity Planning Document details these plans. We are committed to staying at the forefront of AI technology and providing our researchers with the resources they need to succeed.
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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.* ⚠️