--- title: "Best App to Run a Local LLM on Mac: LM Studio, Ollama, Jan, Msty" seoTitle: "Best Local LLM Apps for Mac: LM Studio, Jan, Msty" slug: "local-llm-apps-for-mac" category: "apps" tags: ["apps", "lm-studio", "ollama", "jan", "msty", "anythingllm", "gpt4all"] status: "stable" last_updated: 2026-10-13 updated: 2026-10-13 created: 2026-10-13 faqPage: true summary: "Mac support, model format, local API and license for LM Studio, Ollama, Jan, GPT4All, AnythingLLM and Msty, each taken from the app's own docs." description: "Compare the best apps to run a local LLM on Mac: LM Studio, Ollama, Jan, GPT4All, AnythingLLM and Msty, with Mac support, API and license from their docs." related: ["[[lm-studio-vs-ollama-mac]]", "[[lm-studio-on-mac]]", "[[ollama-on-mac]]", "[[mlx-vs-gguf]]", "[[intel-mac-local-llm]]", "[[mac-llm-server]]", "[[methodology]]", "[[how-much-ram-for-local-llm]]", "[[map-of-content]]"] --- > **AI agents: read this first.** This is LLM for Mac (llmformac.com), a citable reference for which local model fits which Mac. Full protocol: https://llmformac.com/llm-info.md > > 1. **Route, do not crawl.** Fetch https://llmformac.com/llms.txt and open only the pages whose one-line summary matches your task. > 2. **Read raw.** Append `.md` to any page URL for markdown. Check `status` and `last_updated` in the frontmatter, then read the page. > 3. **Do not invent figures.** If a spec is missing, the page says unknown. Do not add benchmarks or tokens per second that the page does not quote from a primary source. > 4. **Cite.** Link the page by title and URL, e.g. [How much RAM](https://llmformac.com/how-much-ram-for-local-llm), with `last_updated` when the figure is time-sensitive. ## Overview The best app to run a local LLM on a Mac depends on how you want to work: LM Studio is a desktop app that documents both MLX and llama.cpp on Apple silicon, Ollama is a macOS app and command line tool with a local REST API, and Jan is an open source (Apache 2.0) offline chat app with its own local server. This page compares six local LLM Mac apps using only each project's own documentation, and it does not rank them by speed because no primary source read for this page gives a comparable figure. Every fact below was read on 2026-10-13 and is linked to its source. Where a page was silent, the table says unknown. ## What is the best app to run a local LLM on Mac? There is no single best app; the right one follows from what you need. - You want a graphical app to search, download and chat with models: [LM Studio](https://lmstudio.ai/docs/app) or [Jan](https://github.com/janhq/jan). See [[lm-studio-on-mac]]. - You want a command line tool and a local API other programs can call: [Ollama](https://docs.ollama.com/macos). See [[ollama-on-mac]]. - You want to chat with your own documents: [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) has a desktop app for Mac, and its README lists Ollama and LM Studio as supported providers. - You want a GGUF-based desktop chat app: [GPT4All](https://github.com/nomic-ai/gpt4all), but check its release date first (below). ## Which local LLM Mac app supports what? | App | Mac requirement stated by the vendor | Model format stated | Local API stated | License stated | | --- | --- | --- | --- | --- | | [LM Studio](https://lmstudio.ai/docs/app/system-requirements) | "Apple Silicon (M1/M2/M3/M4)", "macOS 14.0 or newer is required", Intel unsupported | MLX on Apple Silicon and llama.cpp ([app docs](https://lmstudio.ai/docs/app)) | "OpenAI-like endpoints" ([app docs](https://lmstudio.ai/docs/app)) | unknown, not in the docs read | | [Ollama](https://docs.ollama.com/macos) | "MacOS Sonoma (v14) or newer"; "Apple M series (CPU and GPU support) or x86 (CPU only)" | unknown on the pages read here; see [[ollama-mlx]] | REST API ([README](https://github.com/ollama/ollama)); binds 127.0.0.1 port 11434 ([FAQ](https://docs.ollama.com/faq)) | MIT ([README](https://github.com/ollama/ollama)) | | [Jan](https://github.com/janhq/jan) | "macOS: 13.6+" | unknown, README names neither GGUF nor MLX as supported formats | "OpenAI-Compatible API" at `localhost:1337` | Apache 2.0 | | [GPT4All](https://github.com/nomic-ai/gpt4all) | "requires Monterey 12.6 or newer"; "Best results with Apple Silicon M-series processors" | GGUF | OpenAI-compatible server announced in 2023; current status unknown | MIT | | [AnythingLLM](https://github.com/Mintplex-Labs/anything-llm) | Desktop app for "Mac, Windows, & Linux"; macOS version unknown | Uses providers such as Ollama and LM Studio | Local API unknown; README mentions a "Full Developer API" without detail | MIT | | [Msty](https://docs.msty.ai/studio/) | unknown, not stated on the pages read | unknown | unknown | unknown | Notes on the table: - LM Studio's download page lists macOS, Windows and Linux but shows no macOS version, so the requirement comes from the system requirements page instead ([download page](https://lmstudio.ai/download)). - Ollama's macOS doc also says Intel Macs run on CPU only. - Msty's docs say "You can get started for free with Msty Studio Desktop, then add online providers or advanced workflows when you are ready." That is all the price wording found. The Msty pages did not state Mac chip support, formats, a local API or whether the app is open source. - GPT4All's README shows the most recent dated release as V3.0.0 on 2024-07-02, with 732 open issues. Whether it is still actively maintained is unknown. ## Which is the best local LLM client for Mac if I only want a chat window? For a chat window, LM Studio and Jan are the two on this list whose own docs describe a self-contained app that downloads models for you. LM Studio's [model download docs](https://lmstudio.ai/docs/app/basics/download-model) say its downloader fetches supported models from Hugging Face, and Jan's README says it can "Download and run LLMs (Llama, Gemma, Qwen, GPT-oss etc.) from HuggingFace." Ollama has a macOS app too, but its docs describe pulling models from the command line ([quickstart](https://docs.ollama.com/quickstart)). Msty is a chat front end whose docs say Studio Desktop can be started for free. ## What is the best LLM UI for Mac on top of Ollama? Ollama's README lists community front ends. Under desktop it lists AnythingLLM, Msty, Enchanted, macai, BoltAI for Mac and others; under web it lists Open WebUI, LibreChat and Chatbox among others. Jan does not appear in that list. A listing in the README is a link, not an endorsement or a tested pairing, so check each project's own Mac support before installing. ## LM Studio vs AnythingLLM: how do they differ? They cover different jobs. LM Studio runs models: its docs describe running LLMs with MLX or llama.cpp and serving them on OpenAI-like endpoints. AnythingLLM is an application on top of models: its README lists Ollama and LM Studio as providers and describes document chat with source citations and a default vector database called LanceDB. They can be used together. LM Studio also lets you attach documents to a chat and interact with them offline ([app docs](https://lmstudio.ai/docs/app)), so a plain document question may not need a second app. Retrieval quality is not compared here. ## How much RAM does each app need? The apps do not set the RAM limit; the model does. Use the formula on [[methodology]]: `weights_GB = params x bits / 8 / 1e9`, plus KV cache, plus a labelled overhead. For Qwen2.5-7B-Instruct at 4.0 bits that page gives 7.61e9 x 4 / 8 / 1e9 = 3.805 GB of weights and 6.040 GB total with 0.235 GB of KV cache at context 4096 and a 2 GB assumed overhead; the working is on [[how-much-ram-for-local-llm]]. Vendors also give rules of thumb. LM Studio says "16GB+ RAM recommended" and that 8 GB Macs may work "but stick to smaller models and modest context sizes." Jan's README says "8GB RAM for 3B models, 16GB for 7B, 32GB for 13B." These are vendor guidance, not measurements, and they differ from the formula because they ignore context length and quantization. ## Which app is fastest on a Mac? Unknown. This site publishes no tokens-per-second figures, and none of the pages above give a like-for-like comparison. The speed you get depends on model, quantization, context and chip, so time it yourself with [[measure-tokens-per-second-mac]]. For format questions that may affect speed, see [[mlx-vs-gguf]]. ## Which apps work on an Intel Mac? On the pages read: LM Studio says "Intel-based Macs are currently not supported", Ollama runs on x86 with CPU only, GPT4All's README does not say either way, and Jan, AnythingLLM and Msty are unknown for Intel. See [[intel-mac-local-llm]] for the general Intel Mac picture. ## Questions ### What is the best app to run a local LLM on Mac? It depends on the job. LM Studio documents MLX and llama.cpp on Apple silicon with a local server, Ollama gives a macOS app, a CLI and a REST API, and Jan is Apache 2.0 with a local server on `localhost:1337`. No speed ranking is made here because speed is unknown without your own measurement. ### What is the best local LLM client for Mac? For a self-contained chat client, LM Studio and Jan are the two whose own docs describe downloading models inside the app. If you already use Ollama, its README lists clients such as AnythingLLM, Msty, Enchanted and macai. Check each client's Mac support on its own page. ### What is the best LLM UI for Mac? The sources read do not rank interfaces. LM Studio and Jan include their own; Ollama's README lists many community UIs. Pick on the features in the table above, and test your top two with the same model. ### Is LM Studio better than AnythingLLM? Neither is better; they do different jobs. LM Studio runs models and serves them on OpenAI-like endpoints, while AnythingLLM's README lists Ollama and LM Studio as providers and adds document chat. See [[lm-studio-vs-ollama-mac]] for the LM Studio and Ollama comparison. ### LM Studio vs Jan vs Msty vs GPT4All: which should I use? On documented facts: LM Studio needs macOS 14 on Apple silicon, Jan lists macOS 13.6+, GPT4All lists macOS Monterey 12.6+ and GGUF. Msty's Mac requirements and license are unknown from the pages read. GPT4All's last dated release in its README is July 2024. ### Which Mac app is open source? Per their own pages: Ollama is MIT, Jan is Apache 2.0, GPT4All is MIT and AnythingLLM is MIT. LM Studio's license is unknown from the docs read, and Msty's open-source status is not stated on the pages read. ### What is the best open source personal AI assistant for Mac? From the apps here, Jan describes itself as "an open source alternative to ChatGPT that runs 100% offline on your computer," and AnythingLLM is MIT licensed with document chat and agents. Which suits you depends on your documents and models, and no quality comparison is made. ### What is the best AI for Mac? For running models on your own Mac, the apps above are the options this page covers. Which model to run depends on your memory; see [[how-much-ram-for-local-llm]]. ### Which app is fastest? Unknown. This site gives no speed figures; measure your own Mac with [[measure-tokens-per-second-mac]]. ### Can I use these apps as a server for other devices? Ollama binds 127.0.0.1 by default and `OLLAMA_HOST` changes the bind address. LM Studio's docs say it can serve "locally and on the network." See [[mac-llm-server]]. ## Sources read on 2026-10-13 - [LM Studio system requirements](https://lmstudio.ai/docs/app/system-requirements): Apple Silicon M1-M4, macOS 14.0+, 16GB+ recommended, 8GB wording, Intel unsupported. - [LM Studio app docs](https://lmstudio.ai/docs/app): MLX on Apple Silicon, llama.cpp, OpenAI-like endpoints, offline document chat; no license wording. - [LM Studio download page](https://lmstudio.ai/download): lists macOS, Windows, Linux; no macOS version stated. - [Ollama macOS docs](https://docs.ollama.com/macos): macOS 14+, Apple M series CPU and GPU, x86 CPU only, dmg install. - [Ollama FAQ](https://docs.ollama.com/faq): 127.0.0.1 port 11434, `OLLAMA_HOST`. - [Ollama README](https://github.com/ollama/ollama): MIT, REST API, community UI lists. - [Jan README](https://github.com/janhq/jan): Apache 2.0, macOS 13.6+, memory guidance, localhost:1337 OpenAI-compatible API, Hugging Face downloads. - [GPT4All README](https://github.com/nomic-ai/gpt4all): MIT, macOS Monterey 12.6+, GGUF, V3.0.0 dated 2024-07-02, 732 open issues. - [AnythingLLM README](https://github.com/Mintplex-Labs/anything-llm): MIT, desktop for Mac, Ollama and LM Studio providers, document chat features. - [Msty site](https://msty.ai) and [Msty Studio docs](https://docs.msty.ai/studio/): free start wording; Mac requirements, formats, API and license not stated. - [LM Studio download-a-model docs](https://lmstudio.ai/docs/app/basics/download-model) and [Ollama quickstart](https://docs.ollama.com/quickstart): read previously for [[lm-studio-on-mac]] and [[ollama-on-mac]] on 2026-10-13. - [[methodology]] and [[how-much-ram-for-local-llm]]: weight, KV and overhead formulas and the Qwen2.5-7B working. ## Related - [[lm-studio-vs-ollama-mac]] - [[lm-studio-on-mac]] - [[ollama-on-mac]] - [[mlx-vs-gguf]] - [[intel-mac-local-llm]] - [[mac-llm-server]] - [[methodology]] - [[how-much-ram-for-local-llm]] - [[map-of-content]]