Enterprise-Grade AI and Security Homelab

Welcome to my homelab documentation, showcasing the infrastructure I’ve built for AI development, security research, and various experimental projects.

MCP Ecosystem

The Model Context Protocol (MCP) ecosystem powers my AI infrastructure, enabling seamless integration between various AI models and services.

Key Components:

• MCP Servers: Custom implementations for various services
• LLM Integration: Local and cloud-based language models
• API Gateway: Unified interface for all AI services
• Message Queue: Asynchronous processing for scalability

Learn more about MCP →

Local Cluster and Network Infrastructure

My homelab features a comprehensive network setup designed for security, performance, and flexibility.

Infrastructure Highlights:

• Kubernetes Cluster: 3-node k3s cluster for container orchestration
• Network Segmentation: VLANs for security isolation
• Storage: NAS with 40TB raw capacity, ZFS for data integrity
• Compute: Mix of Intel NUCs and custom-built servers
• Monitoring: Prometheus, Grafana, and custom dashboards

Featured Projects Running in the Lab

AI & Machine Learning

• Local LLM Deployment: Running Llama, Mistral, and Deepseek models
• Vector Database: Qdrant for semantic search capabilities
• Training Infrastructure: GPU nodes for model fine-tuning

Security Research

• Isolated Testing Environment: Separate VLAN for malware analysis
• Honeypot Network: Monitoring and analyzing attack patterns
• SIEM: Security information and event management system

Development & CI/CD

• GitLab Instance: Self-hosted version control and CI/CD
• Container Registry: Harbor for Docker image management
• Development Environments: Isolated containers for each project

Technical Specifications

Future Expansions

• Edge Computing: Deploying compute nodes at remote locations
• 5G Integration: Private 5G network for IoT devices
• Quantum Computing: Exploring quantum simulators and cloud access