Homelab Series – Introduction

Welcome to the first post in a new blog series where I’ll be documenting the setup, evolution, and use cases of my home lab. This series aims to provide insight into the hardware and software stack that powers my projects—both ongoing and upcoming—and why I chose this particular approach. Whether you’re considering setting up your own home lab or just curious about the trade-offs between self-hosted and cloud-based solutions, this series should serve as a useful reference.

Why a Home Lab?

Before diving into the technical aspects, it’s worth asking: Is a home lab really necessary?

The short answer is no, at least not for every project. Many experiments can be carried out using a powerful laptop or a budget-friendly virtual private server (VPS). In fact, for years, I successfully relied on affordable VPS providers like Webtropia and Contabo, where a reasonably powerful VPS (e.g., 12 vCPU cores, 48 GB RAM) costs around $30 per month. When specialized hardware, such as GPUs for training large language models (LLMs), was needed, I rented cloud-based GPUs on an hourly basis rather than making a significant upfront investment in expensive hardware.

However, while VPS and cloud-based solutions provide flexibility, they also come with downsides. One of the biggest challenges is that when running projects and experiments in the cloud, you are paying even when, as it happens often, your focus has shifted to other things. The cumulative cost of maintaining cloud resources can be significant, especially when projects remain idle for extended periods. In contrast, a home lab allows for greater control and cost efficiency, eliminating the need to keep paying for unused resources.

What to Expect in This Series

Over the course of this series, I will cover various aspects of my home lab setup, including:

1. Hardware Overview – A deep dive into my workstation setup, including servers, networking equipment, and storage solutions.
2. Power & Cooling Considerations – Managing power consumption, surge protection, and cooling to ensure longevity and efficiency.
3. Virtualization & Containerization – How I use Docker, Kubernetes, and Proxmox to manage services and workloads.
4. Storage & Backup Strategies – Balancing performance, redundancy, and reliability for critical data.
5. Networking & Security – Securing the home lab, setting up VPN access, and configuring firewalls.
6. Running AI/ML Workloads Locally – Optimizing hardware for deep learning and large language models.
7. Self-Hosting Services – Deploying and managing self-hosted alternatives to cloud services.
8. Monitoring & Automation – Tools and techniques to track system performance and automate routine tasks.
9. Cost Analysis: Home Lab vs. Cloud – A practical comparison of costs over time and when each approach makes sense.

Each post will provide detailed insights, configurations, and lessons learned from my personal experience. Whether you’re setting up your first home lab or optimizing an existing one, I hope this series will provide valuable guidance.

Stay tuned for the next post, where I’ll break down the hardware I’ve chosen and why!

Here are some photos of my setup