Overview
Choose from the docs, not from a speed table. Both pages below were fetched on 2026-10-12. Neither vendor publishes a head-to-head speed result, so this page states none.
Should I use LM Studio or Ollama on a Mac?
Use LM Studio when you want the desktop app that documents GGUF and MLX on Apple silicon. Use Ollama when you want the macOS app plus a CLI and a local HTTP API for scripts.
Ollama’s macOS doc requires macOS Sonoma (v14) or newer, and “Apple M series (CPU and GPU support) or x86 (CPU only).” The preferred install is to mount ollama.dmg and drag the app to Applications. On first start it can link the ollama CLI in /usr/local/bin. The project README shows ollama run gemma4 as the chat example and points at ollama.com/library for the catalog. This page does not quote a size for Gemma 4, because that card is not the source used here.
LM Studio’s system requirements name “Apple Silicon (M1/M2/M3/M4)” and macOS 14.0 or newer. They say 16 GB or more of RAM is recommended, and that you may still be able to use LM Studio on 8 GB Macs with smaller models and modest context sizes. The requirements page does not name M5 or M6. Current Apple spec pages do name those chips. Whether LM Studio runs on them is unknown from the requirements page fetched here. Intel Macs are outside the Apple silicon line that page lists.
Which one supports MLX models?
LM Studio’s app page says: “Run llama.cpp (GGUF) or MLX models” and “On Apple Silicon Macs, LM Studio also supports running LLMs using Apple’s MLX.” Ollama’s macOS page does not mention MLX. Treat Ollama MLX support as unknown until that doc says otherwise. Format details are on mlx-vs-gguf.
Do both offer a local API?
Both document a local HTTP API. Ollama’s README shows a REST call to http://localhost:11434/api/chat. The API doc also lists OpenAI compatibility at http://localhost:11434/v1. LM Studio’s app page says “Serve local models on OpenAI-like endpoints” and names an OpenAI compatibility API. The child URL for that LM Studio endpoint is not guessed here. Cite the app page.
Ollama’s documented chat call, from the README:
curl http://localhost:11434/api/chat -d '{
"model": "gemma4",
"messages": [{"role": "user", "content": "Why is the sky blue?"}],
"stream": false
}'The model name in that snippet is the README’s example. Fit for a model you actually download is on what-fits-my-ram.
Is one faster than the other?
Neither vendor page fetched on 2026-10-12 publishes a head-to-head speed comparison, so this page makes no speed claim. LM Studio names llama.cpp (GGUF). Ollama documents ollama run and a local API. This page does not add a file-format claim beyond those sentences. Memory still has to fit. Use methodology before treating a download as runnable.
Windows and discrete-GPU picks are on the sister site: best GPU for a local LLM.
Questions
Does Ollama’s macOS page require Apple silicon?
It allows “Apple M series (CPU and GPU support) or x86 (CPU only).” GPU support in that sentence is tied to Apple M series. x86 is CPU only, as written.
Where are the install commands?
On run-llm-on-mac, copied from the same docs, with the access date.