How to Run an LLM Locally in 2026 (Setup, Hardware and Real Cost)

Short answer: install a free runner, Ollama for the terminal or LM Studio for a desktop app, download an open model that fits your computer's memory, and chat with it offline. Both tools cost $0 for local use, verified on their pricing pages in October 2026. The real cost is hardware: Apple's cheapest Mac mini starts at $899, which equals 45 months of a $20-a-month cloud AI plan (our arithmetic).

The two routes at a glance

Prices read on ollama.com/pricing and lmstudio.ai/pricing on 28 October 2026. Both vendors also sell optional cloud plans, covered below; neither is needed to run models on your own machine.

Why run an LLM locally at all?

The honest case for local AI is privacy and control, not savings. When a model runs on your laptop, your prompts and files never leave it, you can work on a plane, and nobody changes the model under you or caps your messages. For anyone handling client documents, unreleased code or personal notes, that is a real reason.

What you give up is quality and speed. The open models that fit on a normal computer are smaller than the frontier models behind the big cloud assistants, and they run only as fast as your hardware allows. Ollama says it plainly on its download page: large models are slow on a computer without a strong GPU.

So the right question is not "local or cloud" but "which jobs can a small model do well enough?" Summarizing a document, drafting an email, explaining a code snippet or brainstorming are good fits. Long research with web sources, or hard reasoning on a big codebase, still favors a cloud model.

Dupple data: on Toolradar, our software directory, Ollama's head-to-head comparison pages had 113 visits in the 30 days to October 2026, ranking #5 of 50 tools with 20+ compare-page visits in Terminal Tools. Visitors most often compare it with oMLX and Hugging Face (Ollama on Toolradar). Toolradar tracks 1,572 tools in its AI Assistants category, and local runners are a small, heavily cross-shopped corner of it.

How much RAM do I need to run an LLM locally?

Memory decides which models you can run, more than the processor does. A model has to fit in your GPU's video memory, or in a Mac's unified memory, with room left for the operating system and the conversation itself.

Ollama's quickstart gives a concrete reference point: Gemma 4 E2B is about a 7.2 GB download, and Ollama recommends 8 GB of available VRAM, or unified memory on a Mac, to run it. With less, Ollama can spill over into system RAM, but responses get slower. Larger context windows need more memory too.

Here are download sizes for popular models, read on each model's tags page in the Ollama library on 28 October 2026. The last column is our rule of thumb, not a vendor figure: total memory at least double the model size leaves room for your OS, your browser and a reasonable context window.

Where a size shows a range, the model ships in several variants on Ollama's tags page. The pattern is simple: 16 GB of memory gets you a useful everyday model, 32 GB gets you the 20B-class models, and anything larger needs a workstation.

How to run an LLM locally with Ollama (terminal route)

Ollama is the faster route if you are comfortable with a terminal, and the only one of the two that gives you a local API for scripts and coding tools. These steps follow Ollama's own download page and quickstart.

Step 1install Ollama

On macOS or Linux, Ollama's download page gives a one-line installer:

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

On Windows, run this in PowerShell instead:

irm https://ollama.com/install.ps1 | iex

You can also download the desktop app manually from ollama.com/download. On Linux, start the background server with ollama serve if it is not already running.

Step 2download a model that fits your memory

Pick a model from the table above and pull it. For a first try on a mid-range laptop, the small Gemma 4 model is the one Ollama's quickstart uses:

ollama pull gemma4:e2b

The download is several gigabytes, so start it on a fast connection. Models stay on your disk until you remove them.

Step 3chat with it

ollama run gemma4:e2b

This opens a chat prompt in your terminal. Type a question and press Enter; the answer is generated on your machine, with no account and no API key. Ollama's pricing page puts it plainly: running models on your own hardware is always unlimited.

Step 4 (optional): call it from your own code

Ollama runs a local server on port 11434, and its docs show an OpenAI-compatible endpoint, so many existing scripts and tools work by changing one URL:

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "gemma4:e2b", "messages": [{"role": "user", "content": "Say hello in one sentence."}]}'

One trap to know: in Ollama, a model name ending in :cloud (for example gemma4:cloud) runs on Ollama's servers, not on your computer. If privacy is the reason you went local, stick to tags without that suffix.

How to run an LLM locally with LM Studio (no terminal)

LM Studio suits people who want a chat window rather than a command line. Its desktop app is now branded LM Studio Bionic, and the Free plan includes local models through llama.cpp and MLX, offline voice transcription and a limited web search.

1. Check your machine. LM Studio's system requirements call for an Apple Silicon Mac on macOS 14 or newer, a Windows PC with AVX2 support, or a recent Linux. They recommend 16 GB of RAM or more; 8 GB Macs work only with smaller models and modest context sizes. Intel Macs are not supported.
2. Install the app from lmstudio.ai.
3. Download a model in the Discover tab, either from the curated picks or by searching a name such as Gemma or Qwen.
4. Load it from the model loader in the Chat tab. Loading means reserving memory for the model's weights, which is why the memory table above matters.
5. Chat. LM Studio's docs say the app can run fully offline once you have model files on disk.

Ollama or LM Studio: which should I use?

Use Ollama if you write code, want to plug a local model into a coding agent, or need an API. Ollama's quickstart can launch Claude Code, Codex CLI or OpenCode against your models with commands such as ollama launch claude. Its weakness is the interface: out of the box you get a terminal and an API, so non-developers will find it bare.

Use LM Studio if you mainly want to chat, browse models or chat with your documents without touching a terminal. Its weakness is the reverse: developer features sit behind a separate CLI and SDKs, and Intel Macs are left out.

Nothing stops you from installing both. For a wider field, including Jan, llama.cpp and GPT4All, see our ranking of the best free local LLM tools. Developers who want a deeper look at Ollama itself can read Devshot's Ollama review.

Is running an LLM locally free?

The software is. Ollama's free tier includes running models locally with no subscription and no service fees, and LM Studio's Free plan costs $0. Both companies make money from optional cloud plans:

Sources: Ollama pricing and LM Studio pricing, read 28 October 2026.

The cost that is not free is the machine, plus electricity while it works. If your current laptop already has enough memory for the models above, your local AI setup costs nothing extra. If it has 8 GB, you are limited to the smallest models, and the hardware question below becomes the real decision.

What does the hardware cost? (Mac mini vs a cloud plan)

Take someone who pays $20 a month for a cloud AI plan and wonders whether buying a dedicated machine for local models would pay off. Apple's Mac mini is the common pick because its unified memory acts as video memory for models. Prices below were read on Apple's Mac mini store page and memory on its tech specs page on 28 October 2026.

Memory upgrades raise the price; Apple shows the cost of each option in its configurator.

The arithmetic is not kind to local AI as a money saver. The cheapest Mac mini costs as much as 45 months of that cloud plan, and its base memory runs the 4B to 12B models, not the frontier models a cloud plan includes. Stepping up to an M5 Pro, for 32 GB-class models, pushes break-even past seven years.

Where the hardware does pay off is when the machine has a second job. If you were going to buy a new computer anyway, choosing more memory (24 GB or 32 GB rather than the base amount) turns it into a capable local AI box for the cost of the upgrade alone. That is the cheapest serious way into local models.

Is a local LLM as good as ChatGPT or Claude?

Not on hard tasks. The models in the memory table are capable at drafting, summarizing and answering focused questions, but the cloud assistants run far larger models with web search, file tools and long memory built in. Expect a local 9B or 12B model to need more guidance and to make more factual slips.

The fair comparison is task by task. For private document summaries, quick rewrites and code explanations, a mid-size local model is often good enough and costs nothing per message. For research, complex reasoning or anything you will publish without checking, a cloud assistant is still the safer tool; our Claude vs ChatGPT comparison covers which one fits which job, and our ChatGPT alternatives guide covers the rest of the cloud field. If cost is what pushes you toward local, read whether DeepSeek is free too; DeepSeek's open models, such as deepseek-r1 in the Ollama library, are among the options you can run this way.

How do I use a local model for coding?

Run Ollama and point your coding tool at it. Ollama's quickstart shows ollama launch claude, ollama launch codex and ollama launch opencode, each of which starts that agent from your project folder and lets you pick a model. If you would rather run Claude Code on Anthropic's own models, our Claude Code pricing guide covers what that costs. Editors and extensions that accept an OpenAI-compatible endpoint can use http://localhost:11434/v1 instead.

Keep expectations matched to the hardware. A 20B-class model such as gpt-oss:20b needs the larger memory tier by our rule of thumb, and smaller models are better at explaining or completing code than at multi-file changes. For a comparison of paid and free coding tools, see our guides to the best AI coding assistants and the best free AI coding assistants. If you end up mixing local and cloud models in one app, an LLM gateway routes requests between them, and our explainer on what a token is helps you estimate cloud costs.

How we checked this guide

We read Ollama's pricing, download, quickstart and hardware support pages, the Ollama library tags for each model named, LM Studio's pricing, getting-started and system requirements pages, and Apple's Mac mini store and specs pages on 28 October 2026. Model sizes are Ollama's listed download sizes; the "total memory we'd want" column and all break-even figures are our own arithmetic and rule of thumb. We describe what each vendor's documentation says the commands do. We plan to recheck prices and model sizes every 60 days.

FAQ

Bottom line

Running an LLM locally takes about three commands with Ollama or a few clicks with LM Studio, and the software costs nothing. The decision that matters is memory: 16 GB gives you a useful private assistant for drafting and summaries, 32 GB gets you the 20B-class models, and buying a machine only for local AI rarely beats a cloud plan on price. Go local for privacy and offline work, and keep a cloud assistant for the hard problems.

Cite this: Dupple, "How to Run an LLM Locally in 2026 (Setup, Hardware and Real Cost)", November 2026.

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