Ollama vs llama.cpp vs LM Studio: which should you use?

All three run the same models from the same kind of files (GGUF), on the same hardware, at similar speeds. They differ in how much they do for you, and how much they let you control.

The short version

	Ollama	LM Studio	llama.cpp
What it is	Background service + command line	Desktop app with a chat window	The engine itself
Best for	Most people, and anything that needs an API	People who prefer clicking to typing	Full control and the newest features
Finding models	`ollama run name` from its library	Built-in Hugging Face search	Any GGUF file, or `-hf` to download
API	OpenAI-compatible, port 11434	OpenAI-compatible, port 1234	OpenAI-compatible, port 8080
Open source	Yes	No (free to use)	Yes

Ollama: start here

ollama run qwen3:8b

Ollama is built on llama.cpp and hides almost all of its settings. It picks a file size, uses your GPU, unloads idle models, and runs as a service so other apps can use it. Most local AI apps, including Open WebUI and coding extensions, expect it.

Pick it if: you want it to just work, or you're connecting other tools to a local model.

Outgrow it when: you want exact control over offloading, quantization or new model architectures the day they come out.

LM Studio: the friendliest window

LM Studio is a polished desktop app. You search Hugging Face from inside it, it shows which files fit your memory, and you chat in a clean window. It can also run a local server for other apps.

On Macs it supports MLX, Apple's own format, which is often a little faster than GGUF.

Pick it if: you'd rather not use a terminal, or you want to try lots of different models and quants quickly.

llama.cpp: the engine

llama.cpp is the open-source engine under Ollama, LM Studio and much of local AI. Using it directly gives you every setting and the newest features first. To serve a model to other apps:

llama-server -hf ggml-org/gemma-3-4b-it-GGUF -ngl 99 -c 16384

That downloads the model, puts all layers on the GPU (-ngl 99), sets a 16K context (-c), and serves a chat page and API at http://localhost:8080.

Pick it if: you're tuning a rig, splitting a model across GPUs or between GPU and CPU, running older cards like the Tesla P100, or benchmarking. Our benchmark uses it.

What about vLLM?

vLLM is a server engine built for many users at once on newer NVIDIA cards. It's much faster than the three above when serving a team, and harder to set up. It's not the place to start.

Our advice

Start with Ollama.
Add LM Studio if you want to browse and compare models in a window.
Move to llama.cpp when you want to squeeze the most out of your hardware.

They all read the same model files, so nothing you download is wasted.