Local LLMs: run your own AI on your own data
Guides to choosing, running and tailoring open-weight language models on hardware you control, so your business gets AI that knows your work without sending data to a third party.
No per-token bills, no data leaving the building, and a model shaped around how your business actually runs.
All Local LLMs posts
First Local LLMs posts are on the way. The topics below show what this category will cover.
What local LLMs covers
Everything from the first model download to an assistant wired into your workflows.
Model picks
Comparing open-weight models by size, speed, license and fit for your tasks.
Hardware
What it takes to run a model well, from a single GPU workstation to a small server.
RAG
Connecting a model to your documents and data so its answers are grounded in your business.
Fine-tuning
When to train a model on your own examples, and when retrieval is enough.
→Want it done for you?
Interested in a private LLM build?
We are developing private LLM builds: an open-weight model paired with your documents, running in an environment you control, at a flat cost.
Questions
Can a small business run its own LLM?
Yes. Smaller open-weight models run well on a single workstation with a capable GPU, and pairing one with your own documents through retrieval covers many everyday tasks without training a model from scratch.
Is a local LLM cheaper than ChatGPT or an API?
It depends on volume. You trade per-token or per-seat fees for upfront hardware and setup, so local models tend to pay off when usage is steady and high, or when keeping data private is a requirement.
What is the difference between RAG and fine-tuning?
RAG (retrieval-augmented generation) looks up relevant passages from your documents and hands them to the model at answer time. Fine-tuning changes the model itself by training it on your examples. Most businesses should start with RAG.
Keep exploring
Is a private model right for you?
Book a free consultation to talk through your data, your workflows and whether a local LLM makes sense.