Skip to content

Quick-guide on Running LLMs Locally on macOS

This guide compares the five most popular local toolchains, complete with download links, quick overviews, and pros & cons. A comparison table follows for easy reference.

1. Ollama

Download: https://ollama.com/download/mac

Ollama wraps llama.cpp in a slick native menu-bar app and CLI. It auto-downloads/quantises models (Llama 3, Mistral, Gemma …​) and speaks Apple Metal out of the box. Requires macOS 11+.

Pros

  • Zero-config install (drag-and-drop .dmg)
  • GUI and script-friendly CLI (ollama run …)
  • Curated model library; automatic updates

Cons

  • Closed-source core (only the model files & starter projects are OSS)
  • Limited tuning - no token streaming API yet
  • ~3 GB disk footprint after first launch

2. LM Studio

Download: https://lmstudio.ai

A cross-platform GUI that bundles an open-source CLI/SDK, plus Apple-only MLX acceleration. You get a model catalogue, a local inference server, and simple RAG chat with your files.

Pros

  • Friendly "App-Store" model browser
  • Ships both GUI and MIT-licensed SDK (Python & JS)
  • Runs GGUF or MLX models, ideal for Apple-silicon GPUs

Cons

  • GUI itself is closed source
  • Heavier install (~750 MB); Intel Macs need Rosetta
  • Fewer advanced CLI flags than raw llama.cpp

3. llama.cpp

Repo: https://github.com/ggml-org/llama.cpp

The reference C/C++ project behind most local LLM front-ends. Compile once via Homebrew/CMake and you have maximum control - quantisation, streaming, batching - direct from Terminal.

Pros

  • Fastest path to bleeding-edge features (updated daily)
  • Full CLI flag set; linkable C API & Python bindings
  • Lean (< 30 MB build) and truly open source (MIT)

Cons

  • Steeper learning curve (manual model downloads, GGUF knowledge required)
  • No GUI - bring your own front-end
  • Occasional breaking changes on master

4. GPT4All Desktop

Download: https://gpt4all.io

A Qt-based chat client from Nomic. One click fetches a model (Llama 3, DeepSeek, Nous-Hermes, etc.) and you're chatting offline. Also doubles as an OpenAI-compatible local server.

Pros

  • Privacy-first (all data stays local)
  • Built-in "LocalDocs" RAG panel
  • MIT-licensed core & growing plugin ecosystem

Cons

  • GUI only - no headless mode yet
  • Heavier RAM use than Ollama/LM Studio
  • Fewer nerd knobs for quantisation or GPU tuning

5. KoboldCPP

Repo: https://github.com/LostRuins/koboldcpp

A single-file, zero-install fork of llama.cpp aimed at storytellers (derives from the KoboldAI interface). Universal binaries are provided for M-series Macs; just chmod +x and run.

Pros

  • One executable - no CMake, no Brew
  • Web UI tuned for long-form creative writing
  • Supports mix-precision GGUF and GPU acceleration

Cons

  • Niche UI; less general-purpose than others
  • AGPL-3 licence (copyleft) may deter commercial use
  • Smaller maintainer team → slower feature parity with upstream

Side-by-Side Cheat-Sheet

Tool Interface Install Effort Apple-GPU / MLX Licence Best For
Ollama Menu-bar app + CLI 1-click .dmg ✔ (Metal) Proprietary core "It should just work"
LM Studio Rich GUI + SDK 1-click (.dmg) ✔ (Metal + MLX) MIT SDK / closed GUI Devs who want GUI and code API
llama.cpp CLI / C API brew install cmake ✔ (Metal) MIT Power users & tinkerers
GPT4All Desktop chat 1-click (.pkg) ✔ MIT Privacy-first chat & RAG
KoboldCPP Web/CLI hybrid Download binary ✔ AGPL-3 Fiction & role-play sessions

Choosing in One Minute

  • Need the fastest path from idea → prompt? Pick Ollama.
  • Prefer a full GUI and Python hooks? Go LM Studio.
  • Want total control, scripting, or to embed an LLM in your own app? Compile llama.cpp.
  • Just want a local ChatGPT-style app with zero cloud? GPT4All is the most polished.
  • Writing interactive fiction? KoboldCPP has scene-and-memory features the others lack.

Whichever route you choose, all five options run comfortably on Apple-silicon laptops and let you keep your data - and your GPU cycles - entirely on-device. Happy prompting!