How to Self-Host an LLM on Your Own Server
Self-hosting a language model is more approachable than it sounds: the right hardware, an open model, a runtime to serve it, and a simple interface your team uses. Here’s the shape of it.
Step 1 — Right-size the hardware
Start from the model you want and the number of people who’ll use it at once. That tells you the GPU and VRAM you need. A single-GPU server handles most small-business workloads; plan headroom if you expect growth.
Step 2 — Pick an open model
Open models like Llama, Mistral and Qwen run entirely on your hardware. You choose based on the tasks — general chat, document Q&A, coding — and the size your GPU can hold. You can run more than one.
Step 3 — Serve it with a runtime
A runtime (for example, an Ollama-based setup) loads the model and exposes it to your network. This is what turns a model file into something your apps and staff can actually talk to, on your LAN, with no internet round-trip.
Step 4 — Give people a way in
A simple chat interface on the office network lets staff use the AI exactly like a cloud tool — except nothing leaves the building. From here you can connect it to documents and workflows.
Where a team helps
You can do this yourself, or hand the whole thing to a Texas team that specs, builds, installs and supports it — so day one you have a working private LLM and someone to call.
Key takeaways
- ✓Self-hosting = hardware + open model + runtime + a simple interface.
- ✓Open models (Llama, Mistral, Qwen) run fully on your own server.
- ✓A local runtime keeps every prompt on your LAN — nothing leaves the building.