A local LLM for your business, on your own hardware.
A local LLM for business is an open-source language model that runs on your company's own hardware instead of a cloud API. It answers from your documents, keeps every prompt inside your network, and replaces per-token fees with equipment you own. Small companies can run one. Here is what it actually takes.
Can a small business actually run its own AI?
Yes. This stopped being a big-company capability once open-source models became good enough for scoped work. A model that answers questions from your internal files, drafts in your formats, or sorts your tickets runs on a single server, not a data center. The honest caveat: for open-ended frontier work, cloud models still win. Most business use is scoped, which is why a local model covers it.
Why companies move off public AI tools.
The reasons businesses come to us sound like this: “our staff are pasting customer data into ChatGPT”, “we got flagged in a security questionnaire for using AI”, “my accountant says we shouldn’t upload client files”. Banning public AI tools rarely works; people use them anyway, quietly. A local LLM is the sanctioned alternative: the same convenience, inside your network, with no third party in the data path.
What do you need to run a local LLM?
Model size drives the hardware. The classes below are indicative; the right fit depends on how many people use it at once and what latency you need, which is exactly what an assessment scopes.
| Deployment | Runs on | Good for |
|---|---|---|
| Small open model (7–8B class) | A single workstation-class GPU | One team, one scoped task: drafting, lookup, classification |
| Mid-size model (30B class) | One dedicated GPU server | A company-wide assistant answering from internal documents |
| Large model (70B class) | A multi-GPU server in your rack | Heavier reasoning, several teams, higher concurrency |
| Air-gapped | Any of the above, with no internet connection | Environments where nothing may leave the building |
Is Ollama good enough for a business?
For one person on one machine, yes: it’s excellent, and it’s often how a pilot starts. A business deployment needs more: serving many users at once, access control, logging, backups, and someone accountable when a model update changes behaviour. The gap between “runs on my laptop” and “runs for the company” is an operations gap, and closing it is most of what we do.
What does a local LLM cost for a company?
There is no honest flat answer. Model size, hardware, and integration depth drive it, so every build is scoped and quoted individually. The comparison that matters is against your monthly API bill, which scales with usage and never stops. Hardware you own amortises; the heavier your use, the sooner owning beats renting. Get a quote and we’ll put real figures on your case.
When you shouldn’t do this.
If you have no compliance driver, light usage, and no sensitive data, a cloud API is cheaper and simpler, and we’ll tell you so in the assessment. We wrote down the disqualification criteria we actually use: when not to self-host an LLM.