A private or local LLM is an AI language model that runs on hardware you control, such as your own computer or server, instead of a cloud service run by an AI company. Its main advantage is control over where your data goes. Its main cost is that you become responsible for the setup, quality and maintenance. In short, a private LLM trades convenience for control.
How is it different from using ChatGPT or Gemini?
- Cloud AI: you send prompts to a provider’s servers and get answers back. It’s easy and powerful, with the provider handling the infrastructure.
- Local or private AI: the model runs on your machine or inside your network. Prompts can stay within your systems, depending on how you configure it.
“Enterprise” in this context usually means setting it up for a team, with access controls and integration into company tools, rather than one person experimenting.
Who might need a private LLM?
- Businesses handling sensitive documents such as client records, contracts or internal financials.
- Organisations with strict internal rules or contractual limits on sending data to third parties.
- Teams that want AI features inside their own workflows without per-use cloud fees.
- Technical teams who want to experiment with open models.
Who probably doesn’t?
Most small local businesses. If you mainly draft emails, summarise public information or generate ideas, a reputable cloud tool with careful use is simpler and often stronger. Open models you can run locally may be less capable than the largest cloud models on some tasks, and you’ll need suitable hardware and someone to maintain it.
What does running one involve?
- Choose an open model and a tool to run it. Ollama is one popular option.
- Check your hardware can handle the model you want. Needs vary by model.
- Control who can reach it, and don’t expose it to the internet by accident.
- Test it on your real tasks before relying on it.
- Plan for updates and backups.
Is local automatically safe?
No. Keeping data on your own machine removes one risk but adds others: weak passwords, open network ports, unpatched software and unmanaged access. Privacy depends on how you configure and secure it. See keeping business data private when using AI for practical steps.
A private LLM is worth considering only when the data rules justify the effort. We help businesses decide between cloud and private options through our related services. Not sure which suits you? Talk to us.
Frequently Asked Questions
What does LLM stand for?
Large language model, the kind of AI system behind tools such as ChatGPT and Gemini.
Is a local LLM as good as ChatGPT?
It depends on the model and task. Some local models do well for specific jobs, while the largest cloud models may still be stronger on others. Test on your own work.
Do I need a powerful computer?
Larger models need more memory and often a capable GPU, while smaller models run on modest machines. Check the requirements of the specific model you choose.
Does local mean my data never leaves my network?
Only if you configure it that way and don’t connect it to outside services. Review every integration.
Is it cheaper than cloud AI?
Not always. You avoid per-use fees, but pay in hardware, setup and maintenance time. Compare total cost for your use case.