Homelab AI infrastructure
An NVIDIA DGX Spark on my desk: local models, self-hosted services, and infrastructure as a learning practice.
NVIDIA DGX Spark · local LLMs · Linux · self-hosting
The most valuable AI education I've found doesn't come from API calls — it comes from running the machines yourself.
My homelab is built around an NVIDIA DGX Spark: desk-sized, GPU-accelerated, and mine. It runs the local language models behind the AI writing pipeline and whatever else I'm currently curious about.
Why local
- You learn the whole stack. Serving a model yourself teaches you about memory, quantization, throughput, and failure modes that an API abstracts away.
- Privacy is structural, not contractual. Drafts, experiments, and personal data never leave the room.
- Cost is a one-time decision. Experimentation stops being metered, which changes what you're willing to try.
What runs here
The centerpiece is the writing pipeline. Around it: model experiments, self-hosted services as they earn their keep, and — coming soon — this site's analytics, self-hosted on the same box.
This project is active — I write up pieces of it on the blog as they mature.