LM Studio
A desktop app for discovering, downloading, and running open-weight LLMs locally with a GUI and a local OpenAI-compatible server.
LM Studio is a desktop app for running open-weight LLMs locally through a GUI: browse and download models, chat and tune parameters visually, then flip on a local OpenAI-compatible server for development. It runs GGUF (and MLX on Apple Silicon) models on macOS, Windows, and Linux — free for personal and work use, with no data leaving your machine.
LM Studio is a desktop application for running open-weight LLMs locally through a graphical interface. Where a CLI tool asks you to know the model name and flags, LM Studio lets you browse and download models, chat with them in a built-in UI, and tune parameters with sliders — then, when you're ready to build, flip on a local server that exposes an OpenAI-compatible API. It's the most approachable on-ramp to local models for people who'd rather not live in the terminal.
It is aimed at developers, researchers, and power users who want to experiment with local models, keep data on their own machine, and develop against a local endpoint — all without managing a Python environment. It runs GGUF (and on Apple Silicon, MLX) models on CPU or GPU.
Highlights
- Model discovery & download — browse and pull open models from within the app, with guidance on what fits your hardware.
- Built-in chat UI — converse with a local model and adjust parameters visually, no code required.
- Local OpenAI-compatible server — serve the loaded model on localhost so your app's OpenAI client works unchanged.
- GGUF & MLX — runs quantized models efficiently on CPU/GPU, with native Apple Silicon (MLX) support.
- Private by default — everything runs locally; no account needed and no data leaves your machine.
In an AI-assisted workflow
Download a model in the GUI, start the local server, and point your OpenAI client at it:
# in LM Studio: pick a model → "Local Server" → Start
# base_url="http://localhost:1234/v1" (any OpenAI client)TIP
LM Studio (GUI) and Ollama (CLI) solve the same problem — running models locally — from opposite ends. Choose by preference: a visual app for exploring and tuning, a command line for scripting and automation.
Good to know
LM Studio is free to download and use for both personal and commercial/work use, and runs on macOS, Windows, and Linux; organizations can buy an optional Enterprise tier (SSO, governance). Like other local runners it's built for single-machine development and privacy, not high-concurrency production serving — for that, see vLLM and the Self-Host vs API trade-offs.
Frequently asked questions
- What is LM Studio?
- LM Studio is a desktop application for discovering, downloading, and running open-weight LLMs locally through a graphical interface. It includes a built-in chat UI for experimenting with models and a local server that exposes an OpenAI-compatible API, so your app code can target a local model without changes.
- Is LM Studio free?
- Yes — free to download and use for both personal and commercial/work use, on macOS, Windows, and Linux. Organizations can buy an optional Enterprise tier that adds SSO and governance.
- LM Studio vs Ollama?
- Both run open-weight models locally; they differ in interface. LM Studio is a GUI app for browsing models, chatting, and tuning parameters visually, while Ollama is CLI-first and suited to scripting and automation. Pick by preference — both expose a local OpenAI-compatible API.
- How do I use LM Studio with my app?
- Download a model in the GUI, open Local Server, and click Start. Then point any OpenAI client at http://localhost:1234/v1 — existing OpenAI-format code works unchanged against the local model.
Related
- OllamaAn open-source tool to run open-weight LLMs locally with a single command, including a local OpenAI-compatible API.
- vLLMA high-throughput, memory-efficient inference and serving engine for LLMs, with PagedAttention, continuous batching, and an OpenAI-compatible API server.
- Self-Host vs API: When Does Running Your Own LLM Actually Pay Off?The real economics of self-hosting an LLM vs. calling a hosted API — GPU utilization, privacy, latency, and the hidden ops costs that decide the crossover.
- LLM Inference EngineerUse this agent to serve and optimize self-hosted LLM inference — sizing GPUs, configuring a serving engine like vLLM (continuous batching, PagedAttention, tensor parallelism), applying quantization, and tuning throughput and tail latency against a cost and p95 budget. Examples — "serve Llama-3-70B at p95 under 2s on our GPUs", "our self-hosted model is slow and the GPUs sit half-idle — raise throughput", "quantize this model to fit one GPU without wrecking quality".
- Best Tools for Running LLMs Locally in 2026The local LLM stack, ranked by job: Ollama for serving tools, LM Studio and Jan for desktop exploration, llama.cpp for control, vLLM when it's real serving.
- Ollama vs LM Studio: Running LLMs Locally (2026)Ollama vs LM Studio compared — CLI-first server for developers vs polished desktop app for exploring local models. Which local LLM tool fits how you work.
- JanAn open-source ChatGPT alternative that runs fully offline — a polished desktop app over llama.cpp with a model hub, MCP support, and a local API server.
- Llama CppThe C/C++ inference engine that made local LLMs possible — GGUF quantization, every GPU backend, and an OpenAI-compatible server, with no dependencies.