LM Studio vs Ollama: which local AI tool should you use?

LM Studio and Ollama are the two apps everyone points you to for running an AI model on your own computer, both free, and the choice between them isn’t about which is more capable. It’s about how you want to work. LM Studio is a normal desktop app: a search box, a download button, a chat window, no command line anywhere. Ollama is an engine that runs quietly in the background so your other software can talk to the model. Sit down to chat with a model yourself and you want LM Studio. Want a coding tool, an automation or your own app to use the model and you want Ollama. Both are free, switching costs nothing, and plenty of people end up running both.

If you’ve not run a local model before, our guide to running an LLM on your own computer covers the setup, the hardware and which model to pick. So this page isn’t the which-one-is-friendlier tour. It’s what decides the choice once you’re past the install: what each tool quietly does with the model you asked for, where the “they’re basically the same” line stops being true, and the trap that catches people the first time they feed Ollama a long document.

The quick answer, by what you’re doing

Choose on how you like to work, not on specs, because on capability these two are the same tool wearing different clothes. If you want to open something and talk to a model like you’d talk to ChatGPT, install LM Studio. If you want the model sitting there as an engine for your other software to call, or you’re happy in a terminal, install Ollama. Here’s the split at a glance, with a couple of differences most comparisons leave out:

	LM Studio	Ollama
Feels like	A normal desktop app	A background engine
You drive it with	Buttons and a chat window	A command, or your other software
Best at	Using a model yourself	Letting other apps use the model
Shows the exact model and compression	Yes, in the filename	No, tidy names hide it
Runs on an old Intel Mac	No, Apple Silicon only	Yes, but slowly
Licence	Closed, but free to use anywhere	Open source (MIT)
Cost to run locally	Free	Free

Most people don’t need to agonise. The question that settles it is whether you want to use the model or plug things into it. Everything below is the detail, and the two surprises, behind that one line.

What you’re actually running

Now the part that catches people out. When you type ollama run deepseek-r1, you don’t get DeepSeek R1. You get an 8-billion-parameter distilled version of it, squeezed down to about 5GB, because that’s what Ollama’s plain deepseek-r1 name points at by default. The real R1 is a 671-billion-parameter model that needs a server rack, so defaulting to something that fits a laptop is a sensible call, but nothing on screen tells you you’re running a small imitation of the model whose name you typed. The compression is hidden the same way: Ollama serves a middling-quality quant unless you go looking for another, and its neat one-word names bury both facts.

LM Studio works the other way. You see the full filename before you download, DeepSeek-R1-Distill-Qwen-8B-Q4_K_M and all, so the size, the distillation and the compression level are right there in front of you. It’s less tidy and more honest, and for a beginner that visible detail is exactly what stops you telling people you’re running R1 when you’re running a small model wearing its name. Neither behaviour is wrong; they suit different users. Ollama optimises for “just give me something that works”, LM Studio for “show me what I’m getting”.

Feed Ollama a long document and watch the front of it vanish. Ollama caps how much text the model reads at a conservative default, as low as 2,048 words’ worth on a modest machine and scaling up with your graphics memory, then silently drops anything past the line. No error, no warning. Paste a twenty-page contract, ask about page one, and you can get a confident answer built from text the model never actually saw. The fix is to raise the context length yourself. LM Studio puts that same setting in front of you when you load a model, which is why this one bites Ollama users specifically.

Is either actually faster?

Not in any way a normal user will notice, and the reason is worth getting right because it’s shifting. Both tools grew out of the same engine, llama.cpp, so for years they were the same code with a different shell around it and generated at identical speed. That’s still true for most models. But Ollama has started building its own engine and now runs some newer models on it instead, so “they’re literally the same underneath” is on its way out. It doesn’t change the practical answer: feed each the same model on the same machine and you won’t clock the difference, and the benchmarks that hand it to one or the other by ten percent are measuring their own setup and a bit of noise.

The one real speed story is on a Mac. Apple’s own MLX backend runs models noticeably quicker there than the default engine, up to twice as fast on current models, and both tools now use it: LM Studio for a while, Ollama in preview since 30 March 2026. LM Studio also lets you switch and update that backend by hand in its settings if you want to chase the last few percent. The other difference that’s real but rarely matters to one person: Ollama handles many simultaneous requests better than LM Studio, which counts when you’re serving a model to several people or a busy automation, not when you’re chatting to it. If you want to watch them side by side, this walkthrough, Ollama vs LM Studio vs llama.cpp, covers it clearly.

Open, closed, and where each is heading

The clean hard difference is licensing. Ollama is open source under the MIT licence; LM Studio is closed source but free to use anywhere, including at work since it dropped its commercial licence on 8 July 2025. For most people this changes nothing, because both keep your prompts and files on your own machine either way, which is the whole reason to run local in the first place, covered in full in our sovereignty guide. The privacy you’re buying is identical from both.

It starts to matter in three cases: if your organisation’s policy requires open-source software, only Ollama qualifies; if you want to audit exactly what touches your data rather than trust a vendor’s word, only open source lets you; and if you want to build the tool into a product you ship, the MIT licence gives you room a closed app doesn’t. Outside those, LM Studio being closed but free is a fine trade, and its interface is worth more to most people than the ability to read code they were never going to read.

Worth knowing which way each is travelling, because both are businesses now, not weekend projects. Ollama raised $65M in a July 2026 funding round and is pushing a paid Ollama Cloud (from $20 a month) for running models too big for your own machine. LM Studio sells an Enterprise tier with team controls like single sign-on. Neither move touches the free local tool you’d actually run, and Ollama’s MIT licence means its open version can’t be clawed back, but the shape is the same on both sides: a free core with a paid layer growing on top of it.

Running both, and moving a model between them

Running both is the setup a lot of people land on once local AI sticks. LM Studio becomes where you browse, test and chat by hand, because its interface makes that pleasant. Ollama runs in the background as the always-on engine behind your coding tools and automations, because that’s what it’s good at. They don’t step on each other.

One thing to get right, because it’s more limited than it sounds: the two don’t share a model folder. They both use the same open model format underneath (GGUF, the standard local-model file), and both can pull the same files from Hugging Face, so you’re never locked into one tool’s library. But Ollama tucks its downloads into its own storage in a way LM Studio can’t read directly, so “moving” a model between them is really just downloading it again in the other tool, costing you bandwidth and disk, not money. The clean pattern is to keep the models you chat with in LM Studio and the ones your software calls in Ollama, and not fight to make one folder serve both.

So, which one

Start with LM Studio if you want to open an app and talk to a model, and reach for Ollama the moment you want other software to use it. That single distinction, using the model versus plugging things into it, decides it more reliably than any feature list. If you’re brand new, LM Studio does you two extra favours: it shows you which models fit your machine, and it shows you exactly what you’re running, which spares you both traps above.

Neither pick is a commitment. Both are free, the models come from the same place, and swapping later is a download, not a migration. Get one running this week, see which way you actually reach, and add the other if your use grows into it. When it grows into a whole team on a shared server, that’s the territory of our self-hosted AI guide, and running a local model is one lane of the wider small-business AI shortlist.