AI in the United Nations
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- AI in the United Nations: A Server Configuration Overview
This article details the server infrastructure supporting Artificial Intelligence (AI) initiatives within the United Nations (UN). It's geared toward newcomers to our MediaWiki system and provides a technical overview of the hardware and software employed. Understanding this configuration is crucial for developers, system administrators, and anyone contributing to UN AI projects. This document details the current state as of October 26, 2023.
Overview
The UN leverages AI for a growing number of applications, including peacekeeping operations, humanitarian aid distribution, climate change modeling, and data analysis for the Sustainable Development Goals. This requires a robust and scalable server infrastructure capable of handling large datasets, complex computations, and real-time processing. The architecture is broadly divided into three tiers: Data Ingestion & Storage, Processing & Model Training, and Application & API Delivery. Each tier has specific hardware and software requirements. We utilize a hybrid cloud approach, combining on-premise servers for sensitive data and cloud services for scalability. Understanding Server Roles is essential.
Data Ingestion & Storage Tier
This tier focuses on collecting, validating, and storing the massive datasets used by AI models. Data sources are diverse, ranging from satellite imagery to social media feeds to official UN reports. Data security and integrity are paramount. Data Security Protocols are strictly enforced.
| Hardware Component | Specification | Quantity |
|---|---|---|
| Server Type | Dell PowerEdge R750 | 12 |
| Processor | Intel Xeon Gold 6338 (32 cores) | 12 |
| RAM | 512 GB DDR4 ECC REG | 12 |
| Storage | 100TB NVMe SSD RAID 6 | 12 |
| Network Interface | 100GbE | 12 |
Software utilized in this tier includes:
- Hadoop for distributed storage and processing.
- Apache Kafka for real-time data streaming.
- PostgreSQL with PostGIS extension for storing geospatial data.
- Apache NiFi for data ingestion and workflow automation.
- A custom-built data validation pipeline utilizing Python and Pandas.
Processing & Model Training Tier
This tier is the computational heart of the AI infrastructure. It's responsible for training and evaluating AI models using the data stored in the previous tier. High-performance computing (HPC) resources are crucial. We employ both CPU-based and GPU-based servers. GPU Acceleration is a key component.
| Hardware Component | Specification | Quantity |
|---|---|---|
| Server Type | Supermicro SYS-220H-NR | 8 |
| Processor | AMD EPYC 7763 (64 cores) | 8 |
| RAM | 1TB DDR4 ECC REG | 8 |
| GPU | NVIDIA A100 (80GB) | 8 |
| Storage | 2TB NVMe SSD | 8 |
| Network Interface | 200GbE | 8 |
Software in this tier includes:
- TensorFlow and PyTorch for deep learning model development.
- Kubernetes for container orchestration and resource management.
- MLflow for managing the machine learning lifecycle.
- CUDA and cuDNN for GPU-accelerated computing.
- Jupyter Notebooks for interactive data analysis and model development. This is a popular tool for Data Scientists.
Application & API Delivery Tier
This tier exposes trained AI models as APIs for use by various UN applications and external partners. Scalability, reliability, and security are critical concerns. We prioritize low-latency responses. API Management is a vital process.
| Hardware Component | Specification | Quantity |
|---|---|---|
| Server Type | HP ProLiant DL380 Gen10 | 10 |
| Processor | Intel Xeon Silver 4310 (12 cores) | 10 |
| RAM | 128 GB DDR4 ECC REG | 10 |
| Storage | 1TB SATA SSD | 10 |
| Network Interface | 25GbE | 10 |
Software utilized in this tier includes:
- Flask and FastAPI for building RESTful APIs.
- NGINX as a reverse proxy and load balancer.
- Docker for containerizing applications.
- Prometheus and Grafana for monitoring and alerting.
- Keycloak for authentication and authorization. This ties into our Identity Management system.
Future Considerations
We are actively exploring the use of quantum computing for specific AI applications. The integration of edge computing devices for real-time data processing in remote locations is also under investigation. Quantum Computing Research is ongoing. Further advancements in Artificial Neural Networks are also being monitored.
See Also
- Server Maintenance Procedures
- Network Topology
- Disaster Recovery Plan
- Security Audits
- Software Licensing
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.* ⚠️