VirtualEdge

Illustration of an AI model running privately and offline on a laptop

You use a chatbot every day, and one evening you wonder where your questions go. A local LLM is the answer to that worry. It is an AI chat model that runs on your own computer, so your words stay on your machine. Free tools such as Ollama and LM Studio make the setup short on a recent laptop. The trade-off is simple: local models are smaller than the big cloud ones, so they know less and make more mistakes.

Who this is for: you are curious about AI, you have a fairly recent laptop, desktop or mini PC, and you want private, offline AI without a subscription. Who it is not for: anyone who needs the best possible answers for work today, or whose computer has little memory (the memory section below explains how to check). A cloud service will serve you better.

What a local LLM is, in plain words

LLM stands for large language model. It is the type of AI behind chat tools: a program that has read a huge amount of text and predicts good next words. The "model" is one big file, often several gigabytes. A "runner" is the app that loads that file and lets you chat with it.

When you use a cloud chatbot, the model runs in a data center. Your question travels over the internet, and the answer comes back. With a local LLM, the model file sits on your own disk. Your computer does the work. Once you download the model, you can unplug the internet and keep chatting.

You may also see the words "open model" or "open weights". That means the company that made the model lets you download it. Each model has its own license (the rules for use), so check the license on the model's page before you use it for a business.

Why run a local LLM?

Privacy. Your notes, letters and family questions stay on your computer. The Ollama FAQ says, as of October 2026, that Ollama does not see your prompts or data when you run models locally.
It works offline. On a plane, in a cabin, or during an internet outage, it still answers.
No monthly fee for local use. Ollama and LM Studio are free to run with local models. Your cost is the hardware you already own and the power it uses.

What a local LLM cannot do (yet)

Be honest with yourself here. A model that fits on a laptop is much smaller than the models behind the big cloud services. In practice that means:

More mistakes. Small models get facts wrong more often. The Home Assistant docs put it plainly: smaller models are more likely to make mistakes than larger ones. Check anything that matters.
Old knowledge. A model only knows what it learned up to its training date. Most local setups do not search the web for you.
Short memory by default. The "context window" is how much text the model can keep in mind at once. As of October 2026, Ollama's docs say it uses a 4k context (about a few pages of text) on machines with under 24 GiB of graphics memory. You can raise it, but that uses more memory.
Speed depends on your machine. A model that answers quickly on one computer can crawl on another. Only a test on your own hardware tells you.

For drafting, summaries of your own notes and brainstorming, a local model is often good enough. For legal, medical or money decisions, no model is a source of truth.

Ollama vs LM Studio: which tool to start with

Both tools download models and run them on your machine. They feel different. These facts are as of October 2026. Check the vendor pages before you rely on them.

Factor	Ollama	LM Studio
What it feels like	A small background service you drive from a terminal (a text window for commands), plus a desktop app on Mac and Windows	A desktop app with a chat window, a model browser and settings
License	Open source, MIT license	Free, but closed source; terms allow personal and internal business use
Platforms	macOS 14 or newer (Intel Macs run on the processor only), Windows 10 22H2 or newer, Linux	macOS 14 or newer on Apple Silicon, Windows (x64 and Arm), Linux (Ubuntu 20.04 or newer)
Where models come from	The Ollama model library	Hugging Face, a large public model hub, searched inside the app
Chat with your documents	Drag a text file or PDF into the Mac or Windows app	Built in, offline
Lets other apps use the model	Yes, a local API (a door other programs use) on port 11434	Yes, a local server that copies the OpenAI API style
Optional cloud models	Yes, after you sign in	Yes, on paid plans
Best for	Home servers, scripts, and feeding other self-hosted apps	Trying models on a laptop with no terminal at all

Sources: the Ollama GitHub repo and docs, and LM Studio's system requirements and app terms.

My suggestion: if you want to click, not type, start with LM Studio. If you plan to run AI on a home server and connect other apps to it later, start with Ollama. This is a judgment on ease of use, not a tested ranking.

What you need: memory first

The single biggest limit is memory. The whole model must fit in memory while it runs, plus room for the conversation. If it does not fit, it runs very slowly or not at all.

There are two kinds of memory to know:

RAM is your computer's main working memory.
VRAM is the separate memory on a graphics card (GPU). Models run much faster when they fit in VRAM. Apple Silicon Macs share one pool of memory between the processor and graphics, which helps.

Here is how to size it without guesswork. Check the official guidance for the model before you download it. As of October 2026, the Ollama quickstart says the gemma4:e2b download is about 7.2 GB, and it recommends 8 GB of available graphics memory, or unified memory on a Mac. With less, Ollama can use system RAM, but answers come more slowly. Bigger models need more. Each model's page on the Ollama library lists the size of every version.

LM Studio's own system requirements recommend 16 GB of RAM or more. On Windows and Linux x64, the processor must support AVX2, a set of math instructions. Check your processor's spec page if you are not sure. A graphics card is optional. Ollama supports NVIDIA, AMD and Apple graphics, and runs on the processor alone if there is no supported card. Expect slower answers that way.

You also need disk space. Ollama's docs warn that models can take tens to hundreds of gigabytes once you collect a few. Start with one small model.

Your first local LLM with Ollama, step by step

These steps follow the Ollama quickstart as of October 2026. Commands change, so check the docs if one fails.

Install Ollama. On Mac or Windows, download the app from ollama.com and run it. No administrator rights are needed on Windows. On Linux, open a terminal and run the official install script. (It is good practice to open the script URL in a browser and read it first.)

curl -fsSL https://ollama.com/install.sh | sh

Download a small model. This is the model the Ollama quickstart uses. The download is about 7.2 GB, so it takes a while on a slow connection.

ollama pull gemma4:e2b

Start a chat. Type a question at the >>> prompt and press Enter.

ollama run gemma4:e2b

Leave the chat. Type /bye or press Ctrl+D.
See what is loaded. This shows running models and whether they use the processor or the graphics card.

ollama ps
ollama ls

Want a chat window instead of a terminal? As of October 2026, Ollama's Mac and Windows apps include one.

The same thing in LM Studio

Install the app for your system. Open the model search, pick a small model, and download it. Compare its file size with your free memory, as above. Load it and start chatting. That is the whole job, and you never open a terminal.

The risky bit: what to keep away from it

A local LLM is private, but it is not automatically safe. Watch for these.

Do not open it to the internet. As of October 2026, Ollama listens only on your own computer (address 127.0.0.1, port 11434) by default. Its API has no password, so anyone who can reach that port can use it. The OLLAMA_HOST setting can change that. Never forward that port on your router. If you want to reach it from your phone, use a private VPN instead.
Know when you are using the cloud. Both tools now offer optional cloud models. In Ollama, model names that contain "cloud" run on Ollama's servers, not yours. If privacy is your reason, pick a local model.
Do not hand it the keys. A chat model that only talks is low risk. An AI agent that can run commands, read files or send email is a different thing. Keep agents in a virtual machine, a computer-in-a-computer you can reset. The Hermes vs OpenClaw comparison explains how.
Do not paste secrets. Passwords, bank details and other people's private data do not belong in any chat, local or not. Chat history can be saved on disk.
Get models from known places. Use the official Ollama library or well-known publishers on Hugging Face.
Check its answers. It will sound confident when it is wrong.

Connect it to AI agents (when you are ready)

A local model can also be the "brain" for a self-hosted agent. Both Hermes Agent and OpenClaw can use Ollama. Hermes needs a much bigger context than Ollama's small default, so you must raise that setting. Follow the step-by-step guides to install Hermes Agent in an Ubuntu VM or run OpenClaw with Docker Compose in a VM. Expect an agent on a local model to need more memory than chat alone.

The recovery step: clean up or remove it

Local AI is easy to undo. Models are just files.

Free memory now: ollama stop gemma4:e2b. Ollama also unloads an idle model after five minutes by default.
Free disk space: ollama rm gemma4:e2b deletes that model.
Find the files: models live in ~/.ollama/models on a Mac, /usr/share/ollama/.ollama/models on Linux, and C:\Users\%username%\.ollama\models on Windows.
Remove Ollama on Linux: the Linux docs list the full steps. They start with these two commands.

sudo systemctl stop ollama
sudo systemctl disable ollama

On Mac and Windows, uninstall the app the normal way, then delete the models folder. In LM Studio, delete models from its model list. If you test on a spare machine or a virtual machine, a snapshot taken before you start is the simplest undo of all. The guide to starting a home lab shows cheap ways to get one.

Local AI is one part of a bigger move to run apps at home. The overview of self-hosting in 2026 shows where it fits.

FAQ

Is running an LLM locally free?

The software can be. As of October 2026, Ollama is open source and LM Studio is free for personal and internal business use with local models. You pay for hardware and power. Both also offer optional cloud models, which you do not need.

Can I run a local LLM without a graphics card?

Yes. Ollama runs on the processor alone when there is no supported graphics card. Answers come more slowly. Start with the smallest version of a model and see if the speed suits you.

How much RAM do I need for a local LLM?

It depends on the model. Check its official page before you download. As of October 2026, the Ollama quickstart recommends 8 GB of available graphics memory, or unified memory on a Mac, for its starter model. LM Studio recommends 16 GB of RAM or more.

Is a local LLM as good as ChatGPT?

Usually not. Models small enough for home hardware make more mistakes and know less than the large cloud models. They are good enough for many everyday writing and summary tasks, and they keep your data at home.

Is Ollama or LM Studio better?

Neither wins for everyone. LM Studio suits people who want a point-and-click app. Ollama suits people who want a background service that other apps and home servers can use. You can install both.

Is a local LLM private?

When you run a local model, your prompts stay on your computer. Privacy breaks if you choose a cloud model, expose the port to the internet, or connect it to tools that send data out. Check each of those.