Best Free Local LLM Tools in 2026
This is the one category on our site where every serious option is free, so price cannot break the tie. All of these run large language models on your own machine, none of them charges a subscription, and the differences are entirely about who they were built for.
That makes the real cost something else: your hardware, and your time. A local model needs memory, and the good ones need a lot of it. The tool is free; the machine that runs it usefully is not.
Here is what each one is actually for, checked August 2026.
Quick comparison
| Tool | Licence | Built for |
|---|---|---|
| Ollama | Free, open source | Developers who want a command and an API |
| LM Studio | Free, incl. commercial use | People who want a GUI and model browsing |
| Jan | Free, open source | An open-source ChatGPT replacement |
| llama.cpp | Free, open source | Maximum control and performance |
| GPT4All | Free, open source | Simplicity and document chat |
| Msty | Free, paid Studio tier | Polish, with a commercial tier above |
Ollama: the one that became the default
Ollama is free and open source, and it won this category by making the first five minutes trivial. One command pulls a model, another runs it, and a local API appears that other software can talk to.
That API is why it matters beyond hobbyists. Anything expecting an OpenAI-compatible endpoint can be pointed at Ollama instead, which is how local models get into coding assistants, note tools and scripts without any of them integrating specifically.
It is a command-line tool at heart. Graphical front ends exist and are community-built rather than official.
If you are choosing one thing to install first, install this.
LM Studio: the friendliest way in, and licensed for work
LM Studio is free including commercial use, which is a distinction worth reading twice because it is not universal in open-source-adjacent tooling.
It is a proper desktop application: browse models, see which will fit in your memory before downloading, chat in a real interface, and expose a local server when you want one. For anyone who does not want to think about quantisation formats and parameter counts, it does that thinking visibly.
The model browser is the standout feature. It tells you what your machine can actually run, which is the question every newcomer gets wrong.
Jan: the open-source ChatGPT replacement
Jan is free and open source, and its stated goal is to be the thing you use instead of a hosted chatbot.
The interface is deliberately familiar: conversations, threads, model switching, extensions. It can also connect to remote APIs, so you can keep one interface for both local models and a hosted one when you want the extra capability.
That hybrid ability is the practical argument. Most people are not fully local; they want private by default and powerful when it matters.
llama.cpp: the engine everything else runs on
llama.cpp is free and open source, and it is the inference engine underneath much of this category rather than a competitor to it.
Using it directly gets you the best performance and the most control: quantisation choices, memory settings, hardware acceleration. It is also the first place new model support lands.
It is a build-it-yourself proposition. If you are comfortable compiling and reading flags, nothing else here is faster on the same hardware. If that sentence sounds unappealing, use Ollama, which wraps it.
GPT4All: local documents without the ceremony
GPT4All is free and open source, and its distinguishing feature is chatting with your own documents locally.
Point it at a folder and ask questions about what is in it, with nothing leaving your machine. For anyone with a body of private material, contracts, research, notes, that is the exact use case where local models beat hosted ones outright, not on quality but on the fact that you can use them at all.
Its interface is plainer than LM Studio's and its model selection narrower. The document workflow is why you would choose it.
Msty: polish, with a paid tier above
Msty is free with a paid Studio tier.
Msty is the most designed application here, with parallel conversations against multiple models and a cleaner take on managing prompts and context. It is the one that feels like a commercial product because part of it is.
Free is genuinely usable. Check what sits in Studio before you build a workflow that depends on it.
The cost that is not the software
Memory is the constraint, not the licence. A model's memory requirement scales with its parameter count and quantisation. Small models run comfortably on an ordinary laptop; the ones that feel close to a hosted assistant want considerably more, and on Apple silicon unified memory is the number that matters. LM Studio's model browser is the easiest way to see what fits before you download twenty gigabytes.
Quality is below the frontier and the gap is narrowing. A good local model in 2026 handles summarisation, extraction, classification and routine code well. It is not the tool for your hardest reasoning task. Choosing local is choosing privacy, cost and availability over peak capability, and it is worth being explicit about that trade rather than discovering it.
Local means offline, which is underrated. No rate limits, no outages, no per-token bill, and no question about whether the input left your machine. For confidential work that last one is not a preference, it is the only way the tool is usable at all.
How to choose
Install Ollama first. It is the default for good reasons and its API opens the door to everything else.
If you want a graphical tool and clear licensing for work, LM Studio, free including commercial use.
If you want to replace a hosted chatbot entirely, Jan, which handles both local and remote models in one interface.
If you want maximum speed and control, llama.cpp directly.
If your use case is asking questions of private documents, GPT4All.
Run the small models first. People habitually download the largest thing their disk will hold, get four tokens a second, and conclude local models are unusable. Start small, confirm the workflow, then scale up to what your memory genuinely supports.
Related: our guide to LLM gateways covers routing between local and hosted models, and the Dupple reviews directory has current pricing on the hosted alternatives.
FAQ
Which local LLM tool is best for beginners?
LM Studio, because its model browser tells you what your machine can run before you download anything, which is the mistake almost every newcomer makes. Ollama is the best first install for anyone comfortable with a terminal.
Are local LLM tools really free?
The software is, and all six here are free with no subscription. The cost is hardware and electricity. Note that LM Studio explicitly includes commercial use in its free licence, which is worth confirming for any tool you deploy at work.
Can local models replace ChatGPT?
For summarisation, extraction, classification and routine coding, largely yes, and with total privacy. For your hardest reasoning problems, not yet. Most people who go local end up hybrid: a local model by default and a hosted one for the difficult tasks, which is exactly what Jan is designed to support.
What hardware do I need to run a local LLM?
More memory than you expect, and it is the binding constraint rather than raw processing power. Small models run on ordinary laptops; larger ones want substantially more, and on Apple silicon the unified memory figure is what to check. Start with a small model and confirm the speed before committing to a large download.
