AI in Georgia
AI in Georgia: Server Configuration and Deployment
This article details the server configuration used to support Artificial Intelligence (AI) initiatives within the state of Georgia. It is intended as a guide for new system administrators and developers deploying or maintaining AI-related infrastructure. This document focuses on hardware, software, and networking considerations.
Overview
The state of Georgia is increasingly leveraging AI for various applications, including traffic management, healthcare diagnostics, and public safety. This requires a robust and scalable server infrastructure. The current deployment utilizes a hybrid cloud approach, combining on-premise servers for sensitive data and cloud resources for burst capacity and specialized AI frameworks. We are committed to Data Security and Compliance.
Hardware Specifications
The core on-premise infrastructure consists of high-performance servers designed for machine learning workloads. Details are listed below:
| Component | Specification | Quantity |
|---|---|---|
| CPU | Dual Intel Xeon Platinum 8380 (40 cores/80 threads per CPU) | 12 |
| RAM | 512 GB DDR4 ECC Registered @ 3200MHz | 12 |
| Storage (OS/Boot) | 1 TB NVMe PCIe Gen4 SSD | 12 |
| Storage (Data) | 16 x 18 TB SAS 12Gbps 7.2K RPM HDD (RAID 6) | 4 Arrays |
| GPU | NVIDIA A100 80GB PCIe 4.0 | 24 (2 per server) |
| Network Interface | Dual 100GbE QSFP28 | 12 |
These servers are housed in a dedicated, climate-controlled data center at the Georgia Technology Authority. Regular Hardware Maintenance is crucial.
Software Stack
The software stack is designed to provide flexibility and support for a variety of AI frameworks. We utilize a Linux-based operating system for its stability and open-source nature.
| Software | Version | Purpose |
|---|---|---|
| Operating System | Ubuntu Server 22.04 LTS | Base OS for all servers |
| Containerization | Docker 24.0.5 | Application and dependency management |
| Orchestration | Kubernetes 1.28 | Container orchestration and scaling |
| Machine Learning Frameworks | TensorFlow 2.13.0, PyTorch 2.0.1, scikit-learn 1.3.0 | AI model development and deployment |
| Database | PostgreSQL 15 | Data storage and management |
| Monitoring | Prometheus & Grafana | System and application monitoring |
All code is managed using Version Control with Git and hosted on a private GitLab instance. Automated Continuous Integration/Continuous Deployment pipelines are in place.
Networking Configuration
The network infrastructure is critical for supporting the high bandwidth requirements of AI workloads. We utilize a dedicated VLAN for AI traffic to ensure isolation and security. See also: Network Security.
| Network Element | Specification | Purpose |
|---|---|---|
| Core Switches | Cisco Nexus 9508 | High-speed switching and routing |
| VLAN for AI Traffic | VLAN 100 | Isolation of AI workloads |
| Firewall | Palo Alto Networks PA-820 | Network security and access control |
| Load Balancers | HAProxy | Distribution of traffic across servers |
| DNS | Bind9 | Domain name resolution |
A dedicated 100Gbps connection to the internet provides access to cloud resources. All network traffic is encrypted using TLS/SSL. We also have a robust Disaster Recovery plan in place.
Cloud Integration
We leverage Amazon Web Services (AWS) for burst capacity and access to specialized AI services. Specifically, we utilize:
- AWS S3: For storing large datasets.
- AWS SageMaker: For model training and deployment.
- AWS EC2: For on-demand compute resources.
Data synchronization between on-premise and cloud resources is managed using rsync and AWS DataSync.
Security Considerations
- Access Control: Strict role-based access control (RBAC) is enforced on all servers and cloud resources.
- Data Encryption: All data at rest and in transit is encrypted.
- Vulnerability Scanning: Regular vulnerability scans are performed using tools like Nessus.
- Intrusion Detection: An intrusion detection system (IDS) monitors network traffic for malicious activity.
- Regular Audits: Security audits are conducted annually by an independent third party. See also: Security Policies.
Future Expansion
Future plans include upgrading to newer GPU architectures (NVIDIA H100) and expanding the on-premise storage capacity. We are also exploring the use of federated learning to enhance data privacy. Further Scalability Testing is planned.
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.* ⚠️