LM Studio vs Ollama on a Mac: setup and a same-model test

LM Studio and Ollama are applications for running language models on your own computer. Both offer a desktop app and a local API. LM Studio also ships the lms command-line tool; Ollama supports model creation through a Modelfile. The choice depends on how you want to download, configure and use a model.

For a first desktop setup, I would start with LM Studio's model search and loading controls. For a setup I want to recreate from a text file, I would start with Ollama's Modelfile. Both can serve a local client. I checked that path with the same GGUF model on my M4 Max, using three small tasks repeated three times.

Choose by the work you want to do

These are setup preferences. Check the exact API features your client uses before moving it between runners. Compatibility with one chat-completions request does not establish support for every OpenAI API feature. The LM Studio compatibility documentation and Ollama quickstart describe their supported interfaces.

Both are free to download. Ollama is open source under the MIT licence; the LM Studio app is not open source. You can also keep both installed: this check used one GGUF file in each, loaded directly in LM Studio and imported into Ollama.

What ran on this Mac

The check ran on 31 October 2026 on an Apple M4 Max with 128 GiB of unified memory. LM Studio was version 0.4.26+4, using its llama.cpp Apple Metal runtime 2.67.0. Ollama was version 0.40.13. These are the installed versions tested, not a claim about the latest release.

Both loaded Ornith-1.5-35B-Q4_K_M.gguf, a 22,616,284,864-byte file already on the machine. Ollama imported the same file. The file's SHA-256 is recorded in the downloadable test record so a rerun can check the model bytes. This is one model choice for a functional check, not a recommendation that it is the best local LLM.

• Context configured to 2,048 tokens; one request at a time.
• Temperature 0, maximum output 256 tokens, non-streaming chat completions.
• The same system message and user prompts, including /no_think, sent to both runners.
• Three tasks, each repeated three times. No constrained-output or JSON-schema mode.
• Each answer passed only if its entire content parsed as JSON and exactly matched the expected object.

The runners executed sequentially. LM Studio was explicitly preloaded; Ollama loaded on its first request. Chat templates and runtime defaults were not normalized. This setup cannot support a fair speed ranking or isolate the cause of every output difference. The recorded responses also show reasoning despite the prompt's /no_think text, so that text should not be treated as a verified reasoning-off setting.

Correct values, six format failures

All nine responses from each runner contained the expected values. The addition and missing-date answers wrapped the JSON in Markdown code fences. A person could read them, but the strict parser rejected them. The ticket answers were bare JSON and passed.

For example, the addition answer contained this correct object inside a Markdown code block:

{"currency": "AED", "total": 150}

Removing the surrounding fence would make that answer parse, but fence removal was not part of the test. In a real workflow I would decide the output contract first, then test a supported structured-output mode and validate every result. If the receiving system expects raw JSON, a prompt requesting JSON is insufficient on its own.

The ticket check is one simple example of treating source text as data. Three passes do not establish resistance to prompt injection. These checks also say nothing about Arabic quality, coding performance, long documents, tool calling or concurrent users.

Repeat the check with your own task

Download the prompts, expected objects and full response records. The record includes timing for auditability, with the unequal loading conditions noted above. It contains synthetic task data only.

To import an existing GGUF into Ollama, use a Modelfile like this and substitute your own file path:

FROM /path/to/model.gguf
PARAMETER num_ctx 2048
PARAMETER temperature 0
PARAMETER num_predict 256

Run ollama create runner-check -f Modelfile. In LM Studio, load that same file with a 2,048-token context and start the local server. Point your test client at each server in turn, using the model identifier that server exposes. The Ollama import guide, LM Studio CLI guide and LM Studio server guide cover the setup.

Keep the model hash, prompts and expected answers fixed. Save failed answers as well as successful ones. If you change a template, context setting or output mode, treat it as another run so you can see what changed.

For a UAE team's local workflow

Start with examples from the actual job. An English invoice check in AED does not test Arabic. If colleagues work in Arabic and English, include both languages, mixed-language messages and the document layouts they receive. Have someone qualified to judge the language review those answers.

Check where the whole workflow sends data. A local model can still sit inside an application that uses remote tools, logs or cloud models. Ollama's documentation covers both local and cloud use; a familiar application name alone does not establish where inference runs. Record the selected model and endpoint, and inspect the surrounding application's settings before using private material.

Use the UAE workflow-readiness guide to define permissions and acceptable failures. For hardware sizing, start with the local LLM memory calculator. For a different kind of test, the M4 Max coding comparison measures completed coding tasks. The Local AI hub connects those decisions.

Sources and scope

Product features were checked against the official documentation on 31 October 2026: LM Studio model downloads, its CLI and server guides linked above, and Ollama's quickstart and import guide. The results here come from the downloadable local run records. This small check supports a setup decision and shows an output-format failure; it does not establish a general winner between the applications.

Suhail Mohebi · published 31 October 2026 · the cover is a generated illustration