Best Local LLM Apps for Mac (2026)

Desktop app with a built-in model browser: search, download with one click and chat, no terminal needed. It runs models on llama.cpp or Apple’s MLX and serves an OpenAI-compatible local API. Apple Silicon only, macOS 14 or newer. Version 0.4.25 (19 September 2026) added the experimental Splash engine for Qwen 3.6-35B-A3B and Qwen3.8-27B on M3-or-newer Macs with macOS 26.4+ and at least 36 GB; the current release is 0.4.26 (13 October 2026), which can run alongside the Bionic agent app. MTP speculative decoding has been stable since 0.4.14.

Open-source desktop app designed to feel like ChatGPT: conversation threads, model management and full offline use. A good fit if you want a familiar chat experience with no cloud dependency. The current release is v0.8.6 (9 October 2026).

Nomic’s free desktop chatbot. Its standout feature, LocalDocs, lets you point it at a folder of PDFs or notes and ask questions about them, all processed locally. Note that its last release, v3.10.0, shipped on 25 February 2025, so newer model families may not load.

Desktop app that puts local models and cloud APIs in one window. It works with Ollama models and online providers and lets you compare answers side by side, with a prompt library built in.

Open-source web app you run locally that connects to Ollama, OpenAI, or any API. Beautiful ChatGPT-like interface with conversation history, prompt templates, and markdown rendering. Easy one-click deploy.

Lightweight tool that downloads and runs models with one command and serves a local API that many Mac apps use. v0.40.0 (25 September 2026) runs every model architecture its MLX engine supports on MLX by default on Apple Silicon, with llama.cpp for the rest; ollama ps shows which. The current release is v0.40.6 (16 October 2026). The MLX engine uses speculative decoding on models that ship a draft head, so its speeds are not always plain decode. Since v0.32, typing ollama on its own opens an agent launcher, and v0.35 added decision models such as Nimble that return choices and probabilities instead of text.

Open-source (Apache 2.0) MLX server with a native Swift menu-bar app. It keeps the KV cache in a hot RAM tier and a cold SSD tier, so long conversations and coding sessions reuse past context, even after a restart; serves several models with continuous batching; and speaks both the OpenAI and Anthropic APIs, which is why agent tools point at it. Its Lightning MTP speculative decoding is on by default for models that ship MTP heads (v0.7.1), so check that setting before comparing speeds. MacStories used oMLX for its M5 Ultra review.

SwiftUI app for Mac, iPhone and iPad that connects to Ollama running on your Mac and gives it a native chat window. Its developer now builds Jaz as the new iteration of the project, so Enchanted sees fewer updates, but it still works with an Ollama server.

Comprehensive platform combining local AI chat with document management, workspaces, and agents. Upload PDFs and web pages, then chat with them using your local models.

Apple’s open-source array framework for Apple silicon, running on the CPU and GPU over unified memory. Its mlx-lm package (pip install -U mlx-lm, Python 3.11+) generates, chats, converts and fine-tunes models from Hugging Face. On M5-family chips with macOS 26.2 or later, MLX also uses the GPU’s Neural Accelerators, which Apple measured cutting time to first token up to about 4x against an M4.

The C/C++ inference engine behind many local AI tools and the home of the GGUF format. Metal is on by default on Apple Silicon; install it with brew install llama.cpp or build it from source. It gives full control over quantization, context length, batching and speculative decoding, and new architectures often land here first.

Feature-rich web UI that connects to Ollama or any OpenAI-compatible API. Supports multiple users, RAG document chat, web browsing, image generation, and plugin extensions.

Revolutionary tool that lets you pool multiple Apple Silicon Macs together over your local network to run larger models. Two M4 Max 64GB Macs become a single 128GB inference cluster. Perfect for running 70B+ models without a single high-RAM machine.