# How to run AI models locally on your PC: a beginner's guide

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Running AI models locally on your own computer is now realistic for beginners. This guide explains how to install and use open-source models without cloud services.

## TL;DR
- Local AI models run entirely on your hardware, keeping your data private and removing subscription costs.
- Ollama and LM Studio are the easiest tools for beginners, with simple installers and commands.
- Start with a 7B or 8B model like Llama 3 or Mistral 7B — they work on 8GB of RAM.
- Quantized versions of models use less memory and run on weaker computers.
- You do not need a powerful GPU; CPU-only inference works, just slower.

## Why Run AI Locally?
Running a model locally means your data never leaves your machine. This matters for private documents, work files, or anything sensitive.

There are no monthly fees and no internet connection is required. Once the model is downloaded, you can chat with it offline forever.

You also get full control. You can change parameters, switch models freely, and even fine-tune later.

## Choose Your Model
The model is the brain. For beginners, smaller models are better because they run on regular computers.

- Llama 3, released by Meta in April 2024, comes in 8B and 70B sizes. The 8B version is a great starting point.
- Mistral 7B, released in September 2023, is efficient and performs well for its size.
- Microsoft's Phi-3, also from April 2024, is a small model that can run on modest hardware.

A rule of thumb: start with a 7B or 8B model. If your computer has 16GB of RAM or more, you can try larger models.

## Pick a Tool
You do not need to write code. Several tools handle everything for you.

- Ollama, first released in October 2023, is a command-line tool. You install it, type one command, and the model downloads and runs.
- LM Studio is a graphical application. You browse models, click download, and chat in a window.
- llama.cpp, created in March 2023, is the engine behind many local AI tools. It supports CPU inference, so it works without a GPU.

For absolute beginners, Ollama is the fastest path. For people who prefer a visual interface, LM Studio is better.

## Hardware Requirements
The good news: you do not need a supercomputer.

- 8GB of RAM is enough for a 7B model, though 16GB is more comfortable.
- A GPU with 6GB of VRAM speeds up generation significantly.
- Without a GPU, CPU-only inference works but is slower. llama.cpp is optimized for this.

The model size matters more than the tool. A 7B model in a quantized format takes about 4 to 5GB of disk space and runs on most modern laptops.

## Step-by-Step: Run Your First Model
Here is the simplest path using Ollama.

- Go to ollama.com and download the installer for your operating system.
- Install it and open a terminal.
- Type `ollama run llama3` and press Enter.
- Wait for the download to finish — the 8B model is roughly 4.7GB.
- Start typing your questions. The model answers right in the terminal.

To try another model, type `ollama run mistral`. To exit the chat, type `/bye`.

If you prefer a graphical interface, install LM Studio, search for "Llama 3" in the built-in catalog, download it, and press "Chat".

## Practical Tips
Close other applications before running a model. Free RAM makes generation faster.

Use quantized models. A file named Q4_K_M is a good balance between quality and memory usage.

Keep your tools updated. Both Ollama and LM Studio release frequent updates with new features and model support.

Start with short prompts and simple tasks. Once you understand the basics, experiment with system prompts and temperature settings.

## FAQ
**What is a local AI model?**
A local AI model runs on your own computer using your CPU, GPU, and RAM, instead of sending data to a cloud server.

**What is the difference between Ollama and LM Studio?**
Ollama is a command-line tool with a simple API, while LM Studio provides a graphical interface for browsing, downloading, and chatting with models.

**How do I choose the right model?**
Start with a 7B or 8B model like Llama 3. If your computer has 16GB of RAM or more, try larger models; if it is weak, use quantized versions.

**Do I need a powerful GPU to run AI locally?**
No. CPU-only inference works with llama.cpp, but a GPU with 6GB of VRAM makes responses much faster.