Run Your Own LLM on a Local Server
A local LLM server runs the language model itself on a machine in your office instead of borrowing one from somebody's cloud. Ask it anything, feed it your documents, build tools on top of it — and none of it crosses the internet. We pick the right open model for your work, install it on a server you own, and tune it so it answers fast on your own network.
The hard part isn't the idea — it's the build
Self-hosting an LLM sounds like a weekend project until you hit GPU drivers, model quantization, memory limits, and the question of which of a hundred open models is actually right for your work.
Most teams either give up and go back to a cloud API or run something slow on the wrong hardware. The idea is easy. The dependable build is the part we do.
The right model, picked for you
We match an open model — Llama, Mistral, Qwen and others — to your task, language, and document load instead of guessing.
Tuned to run fast locally
Quantization, GPU offload, and context sizing dialed in so answers come back quick on your own server.
Offline-capable and private
It works on your LAN with the internet unplugged. Prompts and files never leave the building.
Yours to build on
Connect your apps, documents, and tools through a local API — with no per-call charge.
Local LLM server vs. cloud API
| Local LLM server (TIS) | Cloud LLM API | |
|---|---|---|
| Where the model runs | Your office | A vendor's data center |
| Per-request cost | $0 after build | Per token, forever |
| Your prompts / files | Stay on your LAN | Sent off-site, may be retained |
| Works offline | Yes | No |
| Model choice | Any open model, your call | Vendor's menu |
Want it shared and always-on for the whole team? See the Ollama business server, or start from a custom build.
Quantization, in plain English
Quantization is just compressing a model's weights to fewer bits so it needs far less VRAM. The names you will see are precision levels: FP16 is full size, Q8 is about half, and Q4_K_M is roughly a quarter. The smaller the number, the less memory the model needs to run.
Q4_K_M is the community sweet spot — about 75% smaller than FP16 with only a small quality cost, which is why it is what most business servers run day to day. That single choice is often what turns a model that would not fit into one that runs comfortably on the card you have. To map a specific model and quantization to the VRAM and GPU it needs, use our VRAM calculator.
Which model fits your hardware
A rough map of common open-model classes to the VRAM they need at Q4_K_M and the kind of card that runs them. Figures are ranges — actual needs grow with context length and concurrent users.
| Model class | ~VRAM at Q4_K_M | Card that fits |
|---|---|---|
| 8B (e.g., Llama, Mistral) | ~5–6GB | Any consumer card (24GB+) |
| 32B class | ~20–24GB | RTX 5090 (32GB) or up |
| 70B class | ~38–40GB | One 96GB pro card |
Ranges are 2025–2026 figures; full FP16 needs several times this. Size your exact model with the VRAM calculator.
Private builds, installed around Fulshear and beyond
We set up local LLM servers on-site in Fulshear, Simonton, Wallis and across the Houston metro — kept entirely on your own network. Keeping it locked down is its own discipline: see secure local AI, or our Texas service areas.
Local LLM questions
Which open LLMs can a local server run?+
Most of the well-known open models — Llama, Mistral, Qwen, Gemma, and their fine-tunes. We pick based on your task and your hardware; a chat assistant, a document-search model, and a coding model can all run on one server.
How big a model can I run on my own server?+
It depends on GPU memory. We size the build to the model you need — smaller quantized models run on modest hardware, while large models want more GPU VRAM. We tell you exactly what your target model requires before we build.
Will a local LLM be as good as a big cloud model?+
For most business tasks — drafting, summarizing, searching your own documents — a well-chosen open model on tuned hardware is more than enough, and it is private and free to run. We are honest when a workload genuinely needs a frontier cloud model.
Can my existing software talk to a local LLM server?+
Yes. We expose a local API (commonly OpenAI-compatible) so your apps, scripts, and tools connect the same way they would to a cloud service — except the endpoint is a server you own.
What happens to my data when I self-host?+
It stays put. Prompts, documents, and model outputs live on your server and travel only across your own network. Nothing is sent to us or any vendor after install.
What's the difference between Ollama and vLLM for running my LLM?+
Ollama is the easiest path and great for a small team or low concurrency; vLLM is a high-throughput engine that pulls ahead when many people use the server at once. We set up whichever fits and migrate you if you outgrow it — see our Ollama vs vLLM comparison.
How big a model can one card run?+
It comes down to VRAM. A consumer card (24–32GB) handles 8B–32B models; a 70B model at Q4 needs roughly 38–40GB, so it wants a 96GB card. Our VRAM calculator maps the model you want to the card that fits.
Back to AI Servers · read about custom AI servers on the main site.
Let's host your LLM, on your own hardware
Tell us what you want the model to do and we'll pick it, build the server, and tune it to run fast on your network.