My homelab infrastructure provides a distributed compute environment used for AI experimentation, automation systems, monitoring services, and application development. The environment is designed to function as a miniature production platform where new systems can be designed, tested, and operated across multiple nodes.
Homelab Infrastructure
Distributed compute fabric for local AI, automation, observability, media workflows, and experimental applications.
Overview
Problem
Running everything on a single machine limits scalability and reliability. I needed a distributed environment capable of supporting local AI inference, containerized services, automation runtimes, observability, and experimentation with distributed workflows.
Architecture
- Control Plane
- Mac Mini M4 M4
- orchestration services
- automation runtime
- Inference Layer
- Dell Pro Max GB10
- Windows Gaming PC (fallback)
- local model serving
- Application Layer
- Raspberry Pi 5
- dashboards
- apps + media workflows
- Edge Layer
- Pi Zero 2 W (InkyPi)
- Pi Zero 2 W (Pi-hole)
- Raspberry Pi 4B (DogCam)
Public-safe topology view with generalized service roles and sanitized network presentation.
Technology Stack
Technologies
Mac Mini M4 M4
Dell Pro Max GB10
Raspberry Pi 5
Raspberry Pi Zero 2 W
Raspberry Pi 4B
Windows gaming PC
Docker
private LAN
secure remote access
monitoring + dashboarding
Capabilities
- Distributed compute environment for AI experimentation
- Local LLM inference infrastructure
- Containerized service hosting
- Foundation for automation and agent workflows
- Platform for rapid systems experimentation
Implementation
The infrastructure combines compact compute systems and edge devices connected through a private network. Nodes run containerized workloads and participate in shared automation workflows with reproducible deployment patterns and lightweight orchestration.
Outcome
The homelab provides a stable platform for experimentation and system development, enabling rapid prototyping and operation of AI systems, distributed services, and automation workflows without full cloud dependence.
What's Next
- Stronger observability and metrics collection
- Distributed model serving
- Expanded service orchestration capabilities
- Public-safe topology and service visualizations