LM Studio

What is LM Studio?

LM Studio is a user-friendly desktop application that allows individuals and organizations to run large language models (LLMs) locally, supporting offline AI workflows without sending data to the cloud. Its mission is to democratize access to powerful AI by making top open-source models available for private, personal, and professional use on Windows, macOS, and Linux. LM Studio provides a seamless interface for discovering, downloading, and managing AI models from repositories like Hugging Face, along with in-app chat and API access. The application prioritizes data privacy and security, storing all inputs and outputs locally and never requiring an internet connection after installation. Users can analyze documents, build custom AI chatbots, and serve models via OpenAI-compatible APIs, making it suitable for developers, researchers, and privacy-conscious users. Additional enterprise features include team collaboration, private artifact sharing, and admin controls for organizations.

How to use LM Studio?

To use LM Studio effectively, download the application for your operating system (Windows, macOS, or Linux) from the official website, then launch it to access a catalog of supported models. Select and download a model, after which you can interact with it through the intuitive in-app chat interface or serve it to external apps via the OpenAI-compatible local API. You can attach documents for offline analysis or leverage SDKs to integrate LM Studio's models into your own applications. For team or organizational use, create private or public hubs, invite collaborators, and manage access as needed—all without ever sending data off your device.

LM Studio's Core Features

• Run large language models entirely offline on your own computer.

• Supports a wide range of models including Llama, Gemma, Qwen, and DeepSeek.

• User-friendly chat interface for seamless model interaction.

• Direct model downloads from Hugging Face repositories.

• OpenAI-compatible local API for easy application integration.

• Document ingestion and retrieval-augmented generation (RAG) features.

• Cross-platform support: Windows, macOS (incl. Apple Silicon), and Linux.

• Local-only processing for maximum data privacy and security.

• No login required for basic operation.

• Support for team and organizational collaboration with private/public hubs.

• Customizable model parameters and management.

• Extensible via Python and JavaScript SDKs and CLI tools.

• Enterprise features: SSO integration, admin controls, and priority support.

• Automatic updates and support for latest model architectures.

LM Studio's Use Cases

• #1

  Running LLMs privately for research and experimentation

• #2

  Building and testing custom chatbots without cloud dependencies

• #3

  Processing and analyzing sensitive documents offline

• #4

  Developing and prototyping AI applications with local models

• #5

  Enabling AI workflows in data-secure corporate or educational environments

• #6

  Collaborating with team members on shared AI projects

• #7

  Serving local models via OpenAI-compatible APIs for custom solutions

• #8

  Testing new open-source models as soon as they are released