How to Run an LLM Locally on Your Laptop (Beginner’s Guide)

A local large language model (LLM) runs on your computer instead of sending each prompt to a hosted AI service. You download the model once, load it in an app, and chat locally. The setup is easier than it used to be, but model size, memory, speed, and answer quality still depend on your hardware.

For a first experiment, use a desktop app and a task you can check yourself. This guide uses LM Studio and Ollama, two ways to run local models. If your goal is building an app with a local coding assistant, see our Dyad review for that separate workflow.

What local AI is good for, and where it falls short

Local models can be useful when you want to experiment without a per-request API bill, work with downloaded models while offline, or process a document on-device. LM Studio’s offline documentation says downloaded models, document chat, and a local server can work without internet; searching for and downloading models still requires a connection.

The trade-off is capability and convenience. A small model may be slower or less reliable than a hosted service on difficult questions, and it will not automatically have current web information. Local use also shifts the work to your CPU, GPU, memory, storage, and battery. Keep your expectations narrow: a local assistant can be handy for drafts, simple summaries, and private experiments, but it may not be the best tool for every job.

Check your computer before downloading a model

Start with the current LM Studio system requirements, then check free disk space. Its documentation recommends 16 GB of RAM on Apple Silicon Macs and Windows PCs, with smaller models and modest context sizes as the practical route on an 8 GB Mac. Windows x64 requires AVX2 support, and LM Studio recommends at least 4 GB of dedicated GPU memory. These are product recommendations, not a promise that every model will run well.

Model downloads often come in different quantized versions. Quantization reduces file size while trading away some fidelity; LM Studio recommends choosing a 4-bit option or higher when your machine can handle it. Begin with a smaller download, close memory-heavy apps, and only move up if the first model leaves enough headroom. LM Studio explains the download options here.

Choose a simple way to run the model

Option 1: LM Studio for a graphical setup

Download LM Studio for your operating system, open the Discover tab, and search for a model. Select a smaller quantized option that fits your available memory. When the download finishes, load it in the Chat tab and send a test prompt. LM Studio’s getting-started guide walks through the same basic sequence.

To try a private document task, open a non-sensitive sample file and ask for a short summary plus three questions the document answers. Check each answer against the source. The goal is not to prove the model is perfect; it is to see whether this setup is accurate and responsive enough for your real use.

Option 2: Ollama for a terminal workflow

Install Ollama for macOS, Windows, or Linux, open a terminal, and run the sample command from the official quickstart:

ollama run gemma4

The first run downloads the model, so leave time and storage for that step. Type a prompt in the chat and use `/bye` to exit. If you are new to the terminal, LM Studio’s graphical path may feel simpler; Ollama is useful when you want a repeatable command or plan to connect a local model to another tool.

Test it before trusting it with important work

Use one task you already know how to evaluate. For example, give the model a short product FAQ and ask it to list the cancellation policy, summarize the setup steps, and identify anything the FAQ does not answer. Compare the result with the source. Note how long it takes, whether the answer stays grounded in the file, and whether you had to repeat the prompt.

If it struggles, try a smaller context, a different model, or a shorter prompt before assuming you need a more powerful computer. For critical decisions, verify the answer yourself. A local model can confidently invent details just like a hosted model.

Privacy: local does not mean every workflow is offline

A downloaded model can answer locally, but model discovery, downloads, and app updates need an internet connection. Some apps also offer cloud models or integrations, which are different from local inference. Confirm the selected model and connection mode before sharing confidential material. If you enable an API server, keep its access limited to devices and users you trust; a local-network server is not the same as an offline chat window.

When to use local AI instead of a hosted model

Use local AI when offline access, experimentation, or keeping a specific file on-device matters more than top-end capability and convenience. Use a hosted model when you need stronger reasoning, current web access, or do not want to manage local memory and downloads. Many builders can keep both options: a local model for low-risk, repeatable work and a hosted assistant for tasks where quality or current information matters.

If you are choosing a model for software work rather than general offline chat, compare the trade-offs in our guide to AI coding models.

Bottom line

Start with one small model and one testable task. LM Studio is the friendlier first stop if you want a visual interface; Ollama is a good fit if you prefer commands and integrations. Check requirements before downloading, verify answers against the source, and keep a hosted model available for work your computer cannot handle well.