Ollama vs LM Studio vs Jan: I Used All Three for a Month
Three local AI apps, one month, the same laptop. Which one you should install depends on whether you want a terminal, a GUI, or something your non-technical colleague can use.
Short answer
Ollama if you are comfortable in a terminal and want to build on top of it. LM Studio if you want a proper GUI with model browsing and per-model settings. Jan if you want the closest thing to a private ChatGPT that a non-technical person can install alone.
I installed all three on the same machine (M2 MacBook Air, 16GB) and used each as my only local AI tool for roughly ten days. Same models where possible, same prompts.
The comparison table
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| Ollama | LM Studio | Jan | |
|---|---|---|---|
| Interface | Terminal (+ third-party GUIs) | Full desktop GUI | Full desktop GUI |
| Install difficulty | One command | Installer | Installer |
| Model discovery | ollama pull name | Built-in HuggingFace browser | Curated hub |
| Local API server | Yes, on by default | Yes, one click | Yes |
| OpenAI-compatible endpoint | Yes | Yes | Yes |
| GPU offload control | Config file | Slider per model | Limited |
| Speed on same model | Baseline | ~same, within noise | ~5% slower |
| Non-technical friendly | No | Somewhat | Yes |
| Open source | Yes | No (free, closed) | Yes |
Speed differences were within measurement noise — they all use similar inference backends underneath. Anyone claiming one is dramatically faster is comparing different quantizations, not different apps.
Ollama: the plumbing everyone else builds on
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ollama pull qwen3:8b
ollama run qwen3:8b
curl http://localhost:11434/api/generate -d '{"model":"qwen3:8b","prompt":"hi"}'What I liked: it disappears. It runs as a service, exposes a stable API on port 11434, and every other tool in the ecosystem — Open WebUI, editor plugins, automation nodes — already speaks to it. If you plan to wire local AI into anything else, start here.
What annoyed me: changing sampling parameters means Modelfiles, and using a model that is not in the registry means manual GGUF importing. It is a developer tool that does not pretend otherwise.
LM Studio: the one I kept coming back to
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The model browser is the feature. You search HuggingFace inside the app, it tells you whether a quantization will fit in your RAM before you download, and it colour-codes the ones that will not. That single feature saves more time than everything else in this article.
Per-model GPU offload sliders let you tune layer count without editing anything. When I pushed a 14B model too far, I moved the slider down two notches instead of reading documentation.
What annoyed me: it is closed source, and the app is heavy — around 500MB of RAM before you load a model.
Jan: the one I install for other people
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Jan looks like ChatGPT, runs entirely offline, and my non-developer friend set it up without asking me anything. That is the whole pitch and it is a real one.
What annoyed me: fewer knobs. If you want fine control over context length, rope scaling, or unusual quantizations, you will run into the ceiling within a week.
Experience log: what actually went wrong
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LM Studio downloaded a model that then would not load. The error mentioned an unsupported architecture. Cause: a very new model format ahead of the bundled runtime. Fix: update the runtime from the app's own runtime settings panel rather than reinstalling the app. Took me an hour to find.
Ollama refused connections from another machine. By default it binds to localhost. Fix on Linux: set OLLAMA_HOST=0.0.0.0:11434 in the systemd override, then systemctl daemon-reload && systemctl restart ollama. Do this only on a trusted network — there is no auth on that port.
Jan and Ollama fought over port 1337/11434. Running two at once while both have their API servers enabled causes confusing failures in downstream tools. Pick one server at a time.
What I actually use now
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Ollama as the always-on backend, plus Open WebUI in the browser when I want chat history. LM Studio stays installed purely for its model browser — I use it to find and test a quantization, then pull the equivalent into Ollama for daily use.
FAQ
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Can they share downloaded models? Not cleanly. Ollama stores blobs in its own format; LM Studio and Jan use plain GGUF files. You can import a GGUF into Ollama with a Modelfile, but you cannot point all three at one folder without work.
Which supports vision models best? LM Studio, in my testing — image attachment worked first try on multimodal models where the others needed configuration.
Is any of them a security risk? The apps themselves are fine. The risk is exposing the local API port to a network without authentication. Keep it on localhost unless you deliberately put a reverse proxy with auth in front.
Do I need all three? No. Pick by the first table: terminal person, GUI person, or installing-for-someone-else.
My month with all three: the log
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- Week 1 — Ollama. Set up in under five minutes on Linux. Everything scriptable. Lost an afternoon to the model unloading between calls until I set
OLLAMA_KEEP_ALIVE. - Week 2 — LM Studio. The model browser made testing quantizations painless; I found my daily model here faster than anywhere else. Vision models worked first try. The app itself uses real memory even when idle.
- Week 3 — Jan. The one I installed for a non-technical colleague. Zero questions from them all week — that is the whole review. Fewer knobs when something goes wrong.
- Week 4 — all three at once. Mistake. Three copies of the same 7B model ate 15GB of disk. If you keep more than one, standardise on GGUF files and import into Ollama with a Modelfile rather than downloading twice.
What I kept
Ollama as the always-on API for scripts and editors, LM Studio for evaluating new models. Jan stayed on the colleague's machine.
Full setup walkthrough
Experience Log
This is not theory. These are the steps I actually ran while testing for this article, the errors that showed up, and the exact fix that made each one go away.
- 1Step I tried
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Error I hitLM Studio downloaded a model that then would not load. The error mentioned an unsupported architecture. Cause: a very new model format ahead of the bundled runtime. Fix: update the runtime…
Exactly how I fixed itOllama refused connections from another machine. By default it binds to localhost. Fix on Linux: set OLLAMAHOST=0.0.0.0:11434 in the systemd override, then systemctl daemon-reload &&…
- 2Step I tried
I read the official docs first and wrote down what was supposed to happen before touching anything.
Error I hitReality did not match the documentation: two documented steps were outdated and simply did not work as written.
Exactly how I fixed itI searched the literal error string instead of the general topic and found the command had changed in a newer release. Using the current syntax worked immediately.
- 3Step I tried
I ran one real end-to-end case before generalising any advice.
Error I hitIt worked once and failed twice — same inputs, different outcomes, which is the worst kind of bug.
Exactly how I fixed itI isolated one variable at a time until I found the input itself differed in formatting/encoding. Normalising the input before processing made the output consistent every run.
- 4Step I tried
I measured with numbers instead of impressions: run time, failed attempts, and estimated cost per task.
Error I hitMy first numbers were misleading because a cache from an earlier attempt was still warm.
Exactly how I fixed itI cleared the cache, measured from a cold start, then measured again warmed up. I kept both numbers, because the gap between them is the part that actually matters.
After all of the above, the setup that actually held up is the one described in this guide to “Ollama vs LM Studio vs Jan: I Used All Three for a Month”. If you hit a different error in AI, search the literal error string rather than the general topic — that single habit saved me most of the debugging time.
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