🤖 • Run LLMs on your laptop, entirely offline 📚 • Chat with your local documents 👾 • Use models through the in-app Chat UI or an OpenAI compatible local server

Integrated for local, private LLM processing. LM Studio helps VoiSistant users leverage advanced on-device AI features with zero data compromise or cloud dependency.

LM Studio makes it really easy to deploy LLMs locally and makes it brilliantly simple to harvest the power of Apple Silicone chips with their built in Neural Cores and benefit from Unified memory which allows you to dedicate previously unimaginable amount of VRAM for the purposes of running LLMs. You can finally have a really powerful AI running on your laptop that helps you to use leverage to achieve more when dealing with sensitive information on your device.

Its ease of use and time to deploy.

It’s one of the most convenient and lightweight tools for working with LLMs locally. The interface is clean and thoughtfully designed. New models are added almost immediately after release. It can run in server mode with remote access, and it automatically selects the largest model variant that fully fits into available memory. It runs stably and natively on macOS and is well suited for use as a remote server on a MacStudio. An excellent choice for everyday users who want to run models locally.

Absolutely beautiful User Interface. It's super easy to setup and start using. I've used ollama, but since the UI is a separate project, it's a bit difficult to setup. LM Studio also have very good collection of models available compared to Ollama. My favourite thing about LMS is it shows the model size upfront and I don't have to dig through to find it. CUDA runtime is also a great plus.

LM Studio

Discover, download, and run local LLMs

Discover, download, and run local LLMs

LM Studio Reviews

The community submitted 4 reviews to tell us what they like about LM Studio, what LM Studio can do better, and more.

What's great

vs Alternatives

Can it index and chat with large local document sets?

Does it support model quantization choices and guidance?

What's great

What's great