LM Studio

Desktop app for running local LLMs offline with full data privacy

4.3 (6)

Évalué par Daniel Nikulshyn·Mis à jour mai 2026

Privacy Local LLM Desktop App Offline API Cross-Platform Developer Tools Open Source Models

Aperçu

LM Studio is a desktop application that lets users download, run, and chat with open-source large language models directly on their own computer. It supports a wide range of models from Hugging Face, including Llama, Mistral, Gemma, and Qwen variants, and works across Windows, macOS, and Linux. The app provides a built-in chat interface, model discovery tools, and a local server that mimics the OpenAI API, making it easy to integrate local models into existing applications and workflows. Because everything runs on-device, conversations and documents never leave the user's machine. LM Studio is free for personal use and aimed at developers, researchers, and privacy-conscious users who want to experiment with or deploy LLMs without relying on cloud services.

Fonctionnalités clés

In-app model browser and downloader
Local chat interface for any installed model
OpenAI-compatible local API server
GPU acceleration and configurable inference settings
Support for GGUF and MLX model formats
Document chat with local retrieval

Cas d’usage

Private Offline AI Chat

Chat with open-source LLMs like Llama, Mistral, or Qwen entirely on your own machine, keeping conversations and sensitive data fully private without any cloud dependency.

Local OpenAI-Compatible API

Spin up a local server that mimics the OpenAI API to power apps and workflows with on-device models, swapping out cloud endpoints without changing existing code.

Document Q&A on Sensitive Files

Use built-in document chat to query private files locally, ideal for researchers or professionals who can't upload confidential material to external services.

Experimenting with Open-Source Models

Browse, download, and test a wide range of GGUF and MLX models from Hugging Face with configurable GPU acceleration, useful for developers evaluating model performance.

Pour & contre

Pour

Fully offline, keeping data private on the user's device
Free for personal use with no account required
Supports many open-source models in GGUF format
Includes a local OpenAI-compatible API server
Cross-platform support for Windows, macOS, and Linux

Contre

Performance depends heavily on local hardware
Large models require significant RAM and disk space
Lacks the capabilities of top-tier proprietary cloud models
Commercial use may require a separate license

Avis

4.3

Moyenne sur 6 avis.

Connecte-toi pour laisser un avis.

Hiroshi Tanaka

Solid for our team

We rolled this out across the team last quarter and cross-platform support for Windows, macOS, and Linux. Local chat interface for any installed model fits neatly into how we already work, and gPU acceleration and configurable inference settings removed a step we used to do by hand. Large models require significant RAM and disk space, which is the main caveat, but it has held up under daily use.

Naomi Suzuki

Compared a few options

Evaluated this against two competitors. Where it wins: document chat with local retrieval and supports many open-source models in GGUF format. On balance the feature set — especially local chat interface for any installed model — justifies the 5 stars for our use case.

Mei-Ling Wong

Does the job

Pretty happy overall. OpenAI-compatible local API server just works and includes a local OpenAI-compatible API server. but no dealbreakers — I'd recommend it to a friend without hesitating.

Aisha Khan

Use it every day

Honestly didn't expect to like it this much. Document chat with local retrieval is exactly what I needed, and includes a local OpenAI-compatible API server. I do wish performance depends heavily on local hardware, but I reach for it almost every day now and it just clicks.

Ahmed Saleh

Use it every day

Honestly didn't expect to like it this much. GPU acceleration and configurable inference settings is exactly what I needed, and free for personal use with no account required. I do wish performance depends heavily on local hardware, but I reach for it almost every day now and it just clicks.

Robert Ainsworth

Solid for our team

We rolled this out across the team last quarter and fully offline, keeping data private on the user's device. GPU acceleration and configurable inference settings fits neatly into how we already work, and support for GGUF and MLX model formats removed a step we used to do by hand. Commercial use may require a separate license, which is the main caveat, but it has held up under daily use.