Running an LLM Locally

Running a language model locally means executing it on your own computer or server, without sending data to a third-party service. Tools like Ollama, LM Studio, or Jan make this accessible: you download an open model (Llama, Mistral, Qwen, Gemma...) and chat with it offline.

Using an AI model on your own machine, without the cloud.

What it is

Where running an LLM locally stands out

Maximum confidentiality: data never leaves the machine.
No per-request cost; works offline.
Simple tools: a few clicks to install a model and start using it.
Ideal for prototyping, learning, or handling sensitive data.

Limits and precautions

Quality depends on model size, and therefore on hardware: a small local model can't compete with large hosted services.
A GPU with enough video memory changes everything; without one, you're limited to small models.
Installation and updates are on you.
For a team, you need a shared server and a minimum of operational upkeep.

Access and pricing

The tools (Ollama, LM Studio, Jan...) are free. Open models can be downloaded at no cost. The only real cost is hardware — a recent computer is enough to get started with small models. Plans and capabilities change quickly — check current pricing on the official site.

Professional use cases

Processing confidential documents without them ever leaving the organization.
An internal assistant on a controlled server, at a fixed cost.
Developing and testing an AI feature before committing to a hosted model.
Areas with unstable connectivity: offline use.

Frequently Asked Questions

What hardware do I need?

For small models, a recent computer with 16 GB of memory is enough. For better results, a GPU with plenty of video memory.

Is it as good as ChatGPT?

Not with a small local model. The gap narrows with good hardware and large open models, but the main appeal remains confidentiality and cost.

Can a team use it?

Yes, by installing the model on a shared server, with a web interface and access control.