LM Studio is a desktop client for local large language models: download models, manage versions and chat with them from one graphical interface, with a local server mode that exposes an endpoint your own code can call.
Where It Sits in the Local AI Stack
Running models locally usually splits into three parts: an engine (Ollama, llama.cpp, vLLM), an interface (Open WebUI, LM Studio) and the applications on top. LM Studio packages the engine and the interface into one app you can install and use without first understanding model formats, quantization levels and serving flags.
That makes it best suited to getting started and trying models: if you want to know how a 7B or 14B actually feels on your machine, this is the fastest path. Once the model is chosen and it needs to sit behind a product, moving to Ollama or vLLM is a reasonable next step.
The Real Limits
Local inference is bounded by hardware: VRAM and RAM decide how large a model you can load and how much context you can open. Don't carry expectations from a frontier cloud model over to a local 14B — they solve different problems.
LM Studio is also closed source, free for personal use. If your compliance requirements include an auditable toolchain, check that first; running offline is its strength, but offline is not the same as open source.
When to Switch Tools
- Sharing one interface across people: put Open WebUI in front.
- Needs to run persistently for other services to call: use a background server like Ollama.
- Production throughput: move to an inference engine such as vLLM.