TL;DR
Both LM Studio and Ollama now run Apple's MLX framework on Apple Silicon, but not on equal terms. LM Studio has shipped MLX since October 2024. Ollama added it on 30 March 2026 as a preview that requires a Mac with more than 32GB of unified memory. If your Mac has 16GB or 24GB, which most do, Ollama is still on its llama.cpp path and the performance gap people describe is real. Above 32GB on a supported model, it is not.
Almost every published comparison still says these two perform identically because "both use llama.cpp under the hood." That was already wrong in 2024 and it is more wrong now.
This post is about picking a runtime. Whether to run models locally at all is priced separately, and which model size to run is its own decision.
Choosing a local inference setup for a product, not a laptop? We build AI agents on the runtime that matches the deployment target, which is rarely the one that benchmarks best on a workstation.
Key Takeaways
- The "both are llama.cpp so speed is identical" claim is stale. LM Studio has run Apple's MLX since version 0.3.4 in October 2024, through an MIT-licensed engine it maintains in the open.
- Ollama moved its Apple Silicon path to MLX in 0.19 on 30 March 2026, reporting decode throughput roughly doubling on M5 hardware, from 58 to 112 tokens per second.
- Ollama's MLX is a preview with a hardware floor: more than 32GB of unified memory, and at launch it accelerated a narrow set of architectures. Below that threshold you get the older path.
- The durable difference is not speed, it is shape. Ollama is a background service with an HTTP API. LM Studio is a desktop application. That decides more than tokens per second will.
- Community advice on this question ages badly. Threads recommending LM Studio specifically for MLX were correct when written and stopped being correct on 30 March 2026.
The claim that everyone repeats
Search this comparison and you will find the same sentence in most of the top results: performance is comparable because both tools use llama.cpp underneath, so any difference comes from settings.
It is a reasonable thing to have believed. It is also two years out of date on one side and four months out of date on the other.
Apple's MLX is a machine learning framework built for Apple Silicon's unified memory architecture, and for larger models it outperforms the llama.cpp Metal path on the same hardware. Which tool can use it, and under what conditions, is now the single biggest variable on a Mac. On Windows and Linux none of this applies and the old answer still holds.
Where each one actually stands
LM Studio shipped MLX in version 0.3.4 in October 2024. The engine is open source under MIT and built on Apple's mlx-lm for text and mlx-vlm for vision models. It has been through a unified multi-modal rearchitecture since, and more recent releases added KV cache checkpointing aimed at long, repeated agentic runs. It is mature, and it works across Apple Silicon regardless of memory size.
Ollama replaced its Apple Silicon inference path with MLX in version 0.19 on 30 March 2026. The reported gains are large: on M5 hardware, prefill went from 1,154 to 1,810 tokens per second and decode from 58 to 112, with int4 quantization pushing decode to 134. Those are Ollama's own published figures on their own hardware, so treat them as directional.
The caveats matter more than the numbers. Ollama describes it as a preview, it requires a Mac with more than 32GB of unified memory, and the announcement centres on Qwen3.5-35B-A3B with wider architecture support described as in progress.
What that means for your machine
This turns into a simple threshold rather than a general verdict.
Below 32GB, LM Studio's MLX advantage is real on the larger models that fit, and it is the reason so much community advice points that way. Above 32GB with a supported architecture, Ollama's preview closes most of it, and you should choose on the things below instead.
Two honest qualifiers. MLX gains scale with model size, so on small models the difference is minor either way. And new models still tend to appear in GGUF first, with MLX conversions following, so the format with the best day-one availability is not always the fastest one.
The part that did not change
Speed was never the main axis anyway.
Ollama is a service. It installs as a background process, exposes an OpenAI-compatible API on port 11434, runs headless, works under systemd or Docker, and keeps models warm across requests. Applications talk to it. That is what makes it the practical option on a server or inside a product.
LM Studio is an application. It gives you a model browser wired to Hugging Face, a chat window, and visible controls for context length and GPU layers. It has a server mode on port 1234, but that server exists while the app is open. It is a desktop tool that can serve, not a service with a desktop skin.
| LM Studio | Ollama | |
|---|---|---|
| Shape | Desktop application | Background service with an HTTP API |
| MLX on Apple Silicon | Since Oct 2024, GA, any memory size | Since Mar 2026, preview, requires >32GB |
| Engine | MLX plus llama.cpp | MLX above the threshold, otherwise llama.cpp |
| Model discovery | Hugging Face search in the UI | Curated registry, plus GGUF import |
| Headless or remote | Not the intended use | Designed for it |
| Local API | localhost:1234, while the app runs |
localhost:11434, always on |
| Best at | Evaluating models, tuning by hand, non-technical users | Powering applications, servers, automation |
Which one to pick
Pick LM Studio if you are choosing between models rather than shipping one, if you are on a Mac under 32GB and want the fastest path on bigger models, or if the people using it would rather not touch a terminal.
Pick Ollama if something other than a human is calling it. An always-on API, headless operation and container support are the whole reason it exists, and none of them are LM Studio's job.
The common answer is both, and it is not a cop-out. Evaluate models in LM Studio where browsing and parameter tuning are quick, then serve the one you picked from Ollama where your application can reach it.
Frequently asked questions
Is LM Studio faster than Ollama?
On Apple Silicon under 32GB of unified memory, usually yes on larger models, because LM Studio runs MLX and Ollama falls back to llama.cpp there. Above 32GB with a supported architecture, Ollama's MLX preview closes most of that gap. On Windows and Linux the two are close, since neither uses MLX and both sit on llama.cpp.
Does Ollama support MLX?
Yes, since version 0.19 on 30 March 2026, as a preview. It requires a Mac with more than 32GB of unified memory, and architecture support was still expanding at launch. Advice written before that date saying Ollama has no MLX was accurate when published and is not any more.
What about GPT4All and the other options?
GPT4All shows up in the same searches as a third desktop option, and llama.cpp, Jan and MLX-native tools all have real followings. This comparison covers the two that dominate the question, but the field is wider than two names, and if neither shape fits, it is worth looking further.
Can LM Studio connect to a remote Ollama server?
Not directly. LM Studio is a runtime, not a general OpenAI-API client. To get a desktop chat window in front of a remote Ollama instance, use a client built for that job and point it at the Ollama endpoint.
Which uses less memory?
The model dominates, not the runtime. LM Studio carries more idle overhead because it is a GUI application, while Ollama's background service is lighter. Once a model is loaded, that difference is small relative to the weights.