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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:

⚠️ *Note: All benchmark scores are approximate and may vary based on configuration. Server availability subject to stock.* ⚠️

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