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Install lm-studio with Homebrew Cask

Discover, download, and run local LLMs. Version 0.4.28,2 via Homebrew Cask; verified from local package data.

install

Additional install commands

macOS

Homebrew Caskverified · 100%
brew install --cask lm-studio

local Homebrew cask metadata

overview

Package summary

Discover, download, and run local LLMs

Commands and aliases

No executable aliases were found in the local package database.

history

Project history and usage

LM Studio is a cross-platform desktop application and local-AI platform for discovering, downloading, chatting with, and serving open-weight language models on a user's own hardware.

Project history

LM Studio emerged in 2023 as a desktop application for finding, downloading, and running local language models. Its early design combined an offline-capable chat interface, Hugging Face model discovery, model management, and an OpenAI-compatible local server.

Version 0.3.0 in 2024 added document chat, internationalization, structured output support, and network serving. The project then expanded from a GUI application into a wider local-AI stack with lms, SDKs, open-source inference engines, and llmster for headless deployment.

Adoption history

LM Studio gained adoption during the rapid expansion of downloadable open-weight language models by packaging model discovery, quantized-model downloads, chat, and hardware-aware inference in a desktop interface. Its OpenAI-compatible local server made it useful beyond interactive chat because existing clients could be pointed at a local endpoint.

The project subsequently added an lms command-line tool, Apple MLX support, document chat, headless operation, Python and TypeScript SDKs, tool calling, MCP support, authentication, parallel serving, and a native REST API.

How it is used

Users search for compatible models, download quantized variants, load them into memory, and chat locally through the desktop interface. The application can also expose REST and OpenAI- or Anthropic-compatible endpoints for local applications.

The lms CLI supports model downloads, loading and unloading, chat, daemon management, and server control. Named configuration presets are JSON files stored under ~/.lmstudio/config-presets on macOS and Linux.

Local API authentication is optional. Tokens are created and managed in the Developer page and are not shown again after creation; official documentation does not expose a stable token-storage file, so credentials-file-location is null.

Why package nerds care

LM Studio is a prominent desktop distribution channel for local LLM runtimes and quantized model files. It lowers the setup cost of llama.cpp- and MLX-based inference while exposing enough CLI and API surface for scripting and application development.

For package enthusiasts, it sits at the intersection of heavyweight desktop apps, GPU-specific inference runtimes, very large model artifacts, local API compatibility, and reproducible model presets. The desktop application itself is not published as a single open-source repository, although several SDKs, engines, the CLI, and documentation are developed openly.

Timeline

  • 2023: LM Studio began its public life as a desktop local-LLM application.
  • 2024: The lms CLI and LM Studio 0.3 arrived, broadening automation, document chat, and local-server capabilities.
  • 2025: Official Python and TypeScript SDKs reached their initial stable releases.
  • 2026: LM Studio 0.4 added llmster, parallel inference, a native stateful REST API, and a refreshed interface.

Related projects

  • llama.cpp provides one of LM Studio's supported local GGUF inference backends.
  • LM Studio's mlx-engine supports Apple MLX models on Apple Silicon.
  • lms is LM Studio's open-source companion CLI.
  • lmstudio-python and lmstudio-js expose LM Studio capabilities to applications.

security posture

No protected-tool coverage found yet

No matching local secret-handling manifest was found for lm-studio. Package metadata is still published here so future coverage has a stable package URL.

Install behavior

  • No Homebrew post-install hook is recorded in formula metadata.
  • No Homebrew bottle metadata was recorded.

Recommended review

Before unattended agent use, check whether the tool reads plaintext credentials, writes remote state, publishes artifacts, or shells out to plugins.

local files

Configuration and credential file locations

These source-backed paths show where this package keeps local settings or durable credentials. Automic Vault can use them as review targets for secret scanning, migration, and command approval.

Configuration files

Config paths the tool may read or write during local use.

Unix
~/.lmstudio/config-presets/*.json

executables

Installed executables

CommandKindExposureNote
No executable data was present.

freshness

Version and freshness

These signals separate page generation age, package-manager activity, and upstream release comparison. Version lag is warned only when an evidence URL and comparable versions are present.

page generated2026-11-04
manager version0.4.28,2
manager updated
local dataok
upstreamnot checked
latest detectednot detected

https://lmstudio.ai/

  • infoNo package-manager update timestamp was available.low confidence
  • infoRelease/tag comparison is only available for GitHub repositories.https://lmstudio.ai/none confidence

install metadata

Package metadata

Package keycask:lm-studio
Version0.4.28,2
Package managerHomebrew Cask
Package manager pagehttps://formulae.brew.sh/cask/lm-studio
Homepagehttps://lmstudio.ai/
Upstream docshttps://lmstudio.ai/
Source archivehttps://installers.lmstudio.ai/darwin/arm64/0.4.28-2/LM-Studio-0.4.28-2-arm64.dmg
SHA-256f8be634f1eeabaa19d9004d6de8b8bf8cd805a22e34b1f9538d4d702cf884775
Bottlenot recorded
Homebrew post-installnot defined
Servicenone declared

registry facts

Source database details

Source DatabaseHomebrew cask API
Taphomebrew/cask
Full Tokenlm-studio
Names
  • LM Studio
Depends On
Artifacts
Auto Updatesyes
Deprecatedno
Disabledno

source trail

Generated from repository data

This page is generated by av-web from the private package SQLite artifact built by scripts/generate-pkg-sqlite.py.

Used sources

  • cross-ecosystem install command graph
  • curated configuration and credential file locations
  • curated package history
  • package relationship graph
  • package version freshness
  • package-page enrichment