What Is a Local LLM? How to Run AI on Your PC

Can you use AI without an internet connection?

You can run a language model on your own computer to rewrite text, draft messages, or summarize material. With the right software and a downloaded model, these tasks can happen on your device rather than on a remote server.

This setup is commonly called a local LLM. You do not need to buy an expensive computer before trying it. Start with a small model on the machine you already own and see how it handles your everyday work.

What does “local LLM” mean?

LLM stands for large language model: an AI model trained on large amounts of language data to generate text. “Local” describes where it runs.

A cloud service processes requests on remote infrastructure. A local model uses your computer’s processor, graphics hardware, and memory to generate responses. You are usually downloading an already trained model, rather than training one from scratch.

The application and the model are separate parts of the setup. LM Studio and Ollama help you download and run models. A model such as Qwen does the actual language generation.

Check your hardware before choosing a model

There is no single memory requirement that covers every local LLM. Model size, quantization, conversation length, and hardware support all affect what you can run.

For Windows, LM Studio recommends at least 16GB of RAM and 4GB of dedicated video memory. Its x64 version requires a processor with AVX2 support. On Mac, it requires Apple Silicon and macOS 14 or newer, with 16GB or more of memory recommended. Smaller models and modest conversation lengths may work on an 8GB Mac.

These recommendations do not guarantee that every model will run comfortably. When memory is tight, responses may slow down or the model may fail to load. Choose a smaller model before assuming that you need a hardware upgrade.

Names such as “4B” refer to roughly four billion parameters—the numerical values that hold what a model learned during training. A label such as “Q4” indicates quantization, which stores those values at reduced precision to lower memory requirements. This can affect output quality.

Start with LM Studio if you prefer a graphical interface

LM Studio lets you download a model and chat with it through an application window.

• Download the installer for your operating system from https://lmstudio.ai.
• Open Discover and search for a model.
• Choose a small conversational model and a download suited to your hardware.
• Switch to Chat and load the downloaded model.
• Enter a short prompt and check the response.

Installing the application is only the first step. You must also download a model and load it into memory before you can start chatting.

Try a request with an easy-to-check result: “Rewrite this email in clear, polite English without changing its meaning.” You can then move on to outlines, short summaries, or routine writing tasks.

Use Ollama if you prefer the terminal

Ollama is another option for running local models. It is useful if you are comfortable entering commands or want to connect a model to another application.

Install it from https://ollama.com, then open a terminal. On Windows, you can use PowerShell or Windows Terminal. Enter:

ollama run qwen3:4b

This example runs the 4B version of Qwen3. The first run needs to download the model. Once it is ready, you can enter questions in the terminal.

The listed download is approximately 2.5GB, but the memory needed during execution is larger than the model file itself. Conversation data and other working memory add to the total.

Ollama supports both local and cloud models. If your goal is to keep inference on your computer, check the selected model’s execution location. Installing a local application does not mean that every model it offers runs locally.

Understand what works offline

Once the model and required runtime components are downloaded, local chat can work without an internet connection. LM Studio documents offline support for core functions such as chatting with downloaded models and working with documents locally.

Searching for new models, downloading files, and updating software require connectivity. Connected tools that use web search or external services may also send requests over the internet.

Local processing gives you more control over where your material is handled. It does not automatically secure saved conversations or files on your computer. Check storage settings and external integrations before using confidential material.

Test the model with your own language and tasks

A multilingual label does not tell you how well a model will handle your particular writing. Tone, grammar, specialist vocabulary, and instruction following can vary across models and languages.

Choose a few realistic tests: rewrite an email, summarize a passage, and draft a short notice with specific constraints. Compare how much editing each result needs. A smaller model that follows your instructions reliably may be more useful than a larger one that runs too slowly.

Local models can still produce incorrect information. Installing one does not automatically give it access to current news or recently changed rules. Verify dates, figures, and factual claims against reliable material.

If you plan to use a model in a business or distribute it as part of a product, read its license. Downloadable model weights do not imply unrestricted use.

Build around one useful task

You do not need the largest available model to make local AI worthwhile. A practical starting point is a small model that runs comfortably and handles a task you repeat often.

Begin with something easy to review, such as polishing a paragraph. If the speed and output meet your needs, expand to longer documents or connected workflows. That experience will give you a clearer reason to change models or upgrade your hardware.