Running an LLM locally is now a three-step job.
Pick a tool, download a model that fits your memory, and start chatting, with nothing leaving your machine.
In short
Running an LLM locally means downloading a language model and running it on your own computer with a tool like Ollama or LM Studio, so your prompts stay on your machine instead of going to a company server.
What running an LLM locally means
Most AI runs in the cloud: your words travel to a company server, the model thinks there, and the answer comes back. Running an LLM locally flips that. The model sits on your own hard drive and does the thinking on your machine, so nothing you type has to leave it.
The payoff is privacy and independence: no server sees your prompts, it works offline, and there is no usage bill. This page is the practical how-to. If you want the concept and the trade-offs first, the companion page on local AI covers the why.
Step 1: choose your tool
Two free tools dominate in 2026, and the right one depends on how you like to work. Both download and run models for you; you do not touch the model files directly.
- LM StudioA polished desktop app. You search a model by name, click Download, click Load, and start chatting. No terminal, no setup to babysit. The easiest starting point if you just want a local ChatGPT-style window.
- OllamaA lighter, command-line tool. You type one line to pull a model and one to run it. It is popular with developers because it exposes a local API other apps can talk to, but many non-developers get it working in about ten minutes.
- How to pickWant a friendly window and zero terminal? Start with LM Studio. Comfortable in a terminal, or planning to wire the model into other tools later? Ollama fits better. You can install both and keep the one you like.
Step 2: choose a model that fits your RAM
The single thing that decides what you can run is memory. Models come in sizes measured in billions of parameters (7B, 13B, and up), and they are shipped in compressed forms called quantizations that shrink the memory they need in exchange for a small quality dip. The Q4 level is the usual sweet spot for everyday machines. Honest orders of magnitude:
- 8 GB of RAMComfortable with a 7B model at Q4, which needs roughly 4 to 5 GB plus overhead. Good for chat, drafting, and summaries.
- 16 GB of RAMEnough for 13B models, which are noticeably sharper. A solid all-round target for a capable local setup.
- 32 GB or more, or a strong graphics chipOpens the door to larger models that get closer to the big cloud ones. A good GPU speeds everything up a lot.
- The rule of thumbBigger model equals smarter but heavier. If a model runs out of memory or crawls, drop to a smaller size or a lower quantization and it will run fine.
Step 3: install, download, and chat
The whole setup is three steps, and it is the same idea in either tool. First, install the app from its official site, ollama.com or lmstudio.ai. Second, download a model: search it by name in LM Studio and click download, or type a single pull command in Ollama. A good first model is a well-known 7B or 8B, which fits most laptops.
Third, start chatting. In LM Studio you load the model and a chat window opens. In Ollama you type run followed by the model name and talk to it in the terminal, or point a chat app at its local address. There is no account, no API key, and no data leaving your computer.
What to expect on performance
Be realistic and you will not be disappointed. On a normal laptop a local model is smaller than the biggest cloud models, so it is a little less sharp on hard reasoning and slower to type out long answers. The first reply after loading a model can take a moment while it warms up.
That said, for everyday work, drafting, summarizing, rewriting, answering questions about a document, a good 7B or 13B model is genuinely useful. More memory and a real graphics chip close much of the gap. The models also keep getting better at the same size, so local quality rises every few months.
When local is enough, and when the cloud wins
Local shines when privacy matters, when you want to work offline, or when you would rather not pay per message. For sensitive files, personal notes, or private business data, running the model yourself means none of it is ever sent anywhere.
The cloud still wins for the hardest reasoning, very long documents, and the newest frontier models, which are simply too large to fit on a personal computer. A sensible setup is to use both: a local LLM for private, everyday tasks, and the cloud when you need maximum power. You are not forced to choose one forever.
How this connects to owning your AI
Running the model yourself is the strongest form of a principle AskMojo cares about: you should control your AI, not just rent it. The moment the model lives on your machine, no provider can change the terms, raise the price, or cut your access.
It fits the same idea as keeping your skills as portable files and your knowledge base exportable. Build things that are yours, and they run wherever the model does, cloud today, your own machine when it counts.
Frequently asked questions
- How do I run an LLM locally?
- Install a free tool like LM Studio or Ollama from its official site, download a model through it (a 7B or 8B is a good first pick), then start chatting. That is the whole setup on a reasonably modern computer, with nothing sent to the cloud.
- What do I need to run a local LLM?
- A modern computer with enough memory, plus one of the free runner tools. You do not need coding skills, an internet connection after the download, or a paid account. More RAM and a good graphics chip let you run larger, smarter models.
- How much RAM do I need to run a local LLM?
- About 8 GB is enough for a 7B model at Q4 quantization, which suits chat and drafting. 16 GB comfortably runs sharper 13B models, and 32 GB or a strong graphics chip lets you run larger ones that get closer to the big cloud models.
- Is Ollama or LM Studio better for beginners?
- LM Studio is usually easier to start with because it is a graphical app with no terminal: search a model, click download, click load, and chat. Ollama is command-line and favored by developers who want a local API, though many non-developers set it up in about ten minutes.
- Is a local LLM as good as ChatGPT or Claude?
- For everyday tasks like drafting, summarizing, and answering questions about a document, a good local model is genuinely useful. For the hardest reasoning and the very newest models, the biggest cloud services still lead, because those models are too large to run on a personal computer.
- Is running a local LLM free?
- The tools and the open models are free to download and run. You only pay in hardware: a capable computer runs larger models faster. There is no per-message cost, since the model runs on your own machine.
- What is the best local LLM to start with?
- Start with a well-known 7B or 8B open model at Q4, which fits most laptops and runs smoothly. Once you know your machine handles it, try a 13B for sharper answers. The best runner tools list recommended models, so you do not have to guess.
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