How can I run an AI model on my own computer?

Running an AI model on your own computer is much easier than it was a couple of years ago. With free tools, you can have a private chatbot working offline in under half an hour, as long as your hardware is reasonable.

First, the hardware. The key resource is memory. Small models, around 3 to 8 billion parameters, run comfortably on a laptop with 16 GB of RAM, and some smaller ones run on 8 GB. Medium models, around 12 to 32 billion parameters, need roughly 24 to 32 GB of RAM or a graphics card with plenty of video memory. Very large models need high-end GPUs or servers. A dedicated NVIDIA GPU speeds things up a lot, and Apple Silicon Macs (M-series) handle local AI well because of their unified memory. You can run small models on a CPU alone, just more slowly.

Next, choose a tool. Two popular beginner-friendly options are:

1. LM Studio. A desktop app for Windows, Mac and Linux with a graphical interface. You browse models, click download and start chatting. It is the easiest way to begin if you prefer not to use the command line.
2. Ollama. A lightweight tool that runs from the terminal. After installing, a single command such as "ollama run" followed by a model name downloads the model and starts a chat. It also runs a local API, which developers can use to build apps.

Then pick a model. Good starting choices are smaller versions of popular families such as Llama, Qwen, Gemma, Mistral or DeepSeek. Look for "quantized" versions, often labelled with terms like Q4. Quantization compresses the model so it uses much less memory with only a small drop in quality. Start small, check the speed, then try bigger models if your machine copes.

Once it is running, you can chat, summarise documents, generate code or translate text, all without an internet connection and without your data leaving your computer. Some apps also let you chat with your own files locally.

Set realistic expectations. A small local model will be noticeably less capable than the top cloud models. It may make more mistakes, follow complex instructions less reliably and run slower. But for privacy-sensitive tasks, learning how AI works, offline use or building prototypes, local AI is a fantastic and free option.