Homelab + AI + Self-hosting
Tinkering with servers, running local AI models, and connecting it all together. I self-host everything I can and document the journey here.
Say hi01 What I do
Mostly servers, AI setups, and whatever rabbit hole I fall into next.
Proxmox clusters, LXC containers, VMs for everything. I like building infrastructure that just runs, even when I forget about it for weeks. Multiple nodes at home and in data centers, all connected through a mesh VPN.
Running LLMs and image generation on my own hardware. No cloud APIs, no per-token billing. Just a GPU doing its thing on a shelf in my room.
Web hosting, email, DNS, git repos, CI runners. If there's a self-hosted alternative, I'm probably running it somewhere.
Pipelines, deployment scripts, and AI agents that do the boring stuff so I don't have to. Still tweaking, always tweaking.
Encrypted tunnels everywhere, key-only SSH, firewall rules, monitoring. Not because I'm paranoid, but because getting hacked would be really annoying.
02 Projects
Side projects, tools I built for myself, and things that started as "this should be quick" and never were.
03 The Stack
A mix of dedicated servers, mini PCs, and a GPU workstation, all stitched together with Tailscale.
Proxmox VE clusters with a bunch of VMs and containers. Dedicated servers in data centers plus a few machines at home. Overkill for one person, but that's kind of the point.
An RTX 3090 running Ollama and ComfyUI, serving local AI to every machine on the network. No cloud, no API keys, no monthly bills.
Tailscale mesh VPN connecting everything. Home, data center, laptop, phone. Every connection encrypted, every device reachable by name.
Dashboards, alerting, automated backups. I get a notification before things break, and snapshots so I can roll back when I inevitably break something myself.
04 Latest
Write-ups about things I've built, broken, and eventually fixed.
Eleven local models, three real tasks, two RTX 3090 hosts, 198 requests, zero errors. 90 of 99 outputs came back byte-identical across machines — and the nine that did not all belong to the one model that spills onto the CPU. Plus why "21 of 33 failed the doctype check" does not mean a single model forgot the doctype.
Two RTX 3090s, eleven models with identical digests, 528 requests and no errors. Reserving a 128k KV cache is nearly free; filling it costs 10 to 66 percent of throughput — with nothing offloaded to the CPU on ten of eleven models. Plus: an attached monitor costs you 1.8 GB of usable VRAM.
Ollama 0.32.5 was out, my workstation was still on 0.31.2, and I had just finished benchmarking 26 models on it. So I had a rare opportunity: the same…
05 Contact
Based in Europe. Always happy to chat about homelab stuff, AI, or whatever you're building.