AI in Tyne and Wear
AI in Tyne and Wear: Server Configuration for Regional Deployment
This article details the server configuration recommended for deploying and supporting Artificial Intelligence (AI) applications within the Tyne and Wear region. This guide is intended for newcomers to our MediaWiki site and focuses on practical, scalable solutions. We aim to provide a robust and efficient infrastructure capable of handling the demands of various AI workloads, from machine learning model training to real-time inference. Consideration is given to cost-effectiveness, maintainability, and future scalability.
Overview
The deployment strategy centres around a hybrid approach, leveraging both on-premise infrastructure for sensitive data and cloud-based resources for burstable workloads. This allows for flexibility and control while minimizing capital expenditure. The core on-premise system will reside within a secure data centre in Newcastle upon Tyne, with connectivity to major cloud providers (AWS, Azure, and Google Cloud Platform) via dedicated high-bandwidth links. This article will focus on the on-premise requirements, with notes on cloud integration. We will cover server specifications, networking, storage, and software stacks. See also: Data Centre Security Protocol and Network Topology Overview.
Hardware Specifications
The following table outlines the recommended hardware specifications for the core on-premise AI server cluster. The cluster will be divided into three tiers: Data Ingestion, Model Training, and Inference.
| Tier | Server Role | CPU | RAM | GPU | Storage | Network Interface |
|---|---|---|---|---|---|---|
| Data Ingestion | Data Collection & Preprocessing | 2 x Intel Xeon Gold 6338 | 256GB DDR4 ECC | None | 4 x 4TB NVMe SSD (RAID 10) | 10GbE |
| Model Training | Machine Learning Model Training | 2 x AMD EPYC 7763 | 512GB DDR4 ECC | 4 x NVIDIA A100 80GB | 8 x 8TB SAS HDD (RAID 6) + 2 x 1TB NVMe SSD (OS) | 100GbE |
| Inference | Real-Time Prediction and Analysis | 2 x Intel Xeon Silver 4310 | 128GB DDR4 ECC | 2 x NVIDIA T4 16GB | 2 x 2TB NVMe SSD (RAID 1) | 25GbE |
These specifications are a starting point and can be adjusted based on the specific AI models and datasets being utilized. Refer to Hardware Procurement Guidelines for approved vendor lists. Regular monitoring of resource utilization is essential; see Server Monitoring Dashboard.
Networking Infrastructure
A robust network is critical for AI workloads, particularly for data transfer and distributed training. The following table details the networking requirements.
| Component | Specification | Purpose |
|---|---|---|
| Core Switches | Arista 7050X Series (x2) | High-speed switching and routing |
| Server Network Adapters | As specified in Hardware Specifications | Connectivity to the network |
| Inter-Server Links | 100GbE QSFP28 | High-bandwidth communication between training servers |
| Internet Connectivity | 10Gbps Dedicated Link | Access to external resources and cloud providers |
| Firewall | Palo Alto Networks PA-820 | Network security and access control |
Network segmentation is crucial for security. Separate VLANs should be configured for each tier (Data Ingestion, Training, Inference) and for management traffic. Details can be found in the Network Security Policy. Latency between servers should be minimized, ideally under 1ms.
Storage Configuration
Data storage needs vary greatly depending on the application. The on-premise storage solution is designed to handle both structured and unstructured data.
| Storage Type | Capacity | Technology | Purpose |
|---|---|---|---|
| Raw Data Storage | 200TB | SAS HDD (RAID 6) | Long-term storage of raw datasets |
| Model Storage | 50TB | NVMe SSD | Fast access to trained models for inference |
| Temporary Storage | 20TB per server | NVMe SSD | Temporary files during data processing and training |
| Backup Storage | 500TB | Tape Library (LTO-9) | Offsite data backup and disaster recovery |
Data lifecycle management policies should be implemented to ensure efficient storage utilization. See Data Backup and Recovery Procedures for detailed instructions. The storage system utilizes a distributed file system (Ceph) for scalability and redundancy.
Software Stack
The software stack will be based on Ubuntu Server 22.04 LTS. The key components are:
- Operating System: Ubuntu Server 22.04 LTS
- Containerization: Docker and Kubernetes for application deployment and management. See Kubernetes Cluster Setup Guide.
- Machine Learning Frameworks: TensorFlow, PyTorch, scikit-learn
- Data Science Tools: Jupyter Notebook, Pandas, NumPy
- Database: PostgreSQL for structured data storage. See PostgreSQL Administration Manual.
- Monitoring: Prometheus and Grafana for system monitoring and alerting. Refer to Prometheus Configuration.
- Version Control: Git for code management.
Regular software updates and patch management are essential for maintaining security and stability. See Software Update Policy.
Cloud Integration
For burstable workloads and disaster recovery, integration with cloud providers is essential. Specifically, we utilize:
- AWS S3: For storing large datasets and model artifacts.
- Azure Machine Learning: For distributed training of complex models.
- Google Cloud TPU: For specialized AI acceleration.
Data synchronization between on-premise and cloud storage is automated using rsync and secure transfer protocols. See Cloud Integration Best Practices.
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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.* ⚠️