llama.cpp vs Ollama vs LM Studio: Engine vs Wrapper Explained

The "llama.cpp or Ollama" debate is built on a false premise. These aren't two competing products, they're two layers of the same stack: llama.cpp does the actual computation, Ollama makes that convenient. Choosing between them makes about as much sense as choosing between an engine and a car.

Along the way it'll become clear why, on identical hardware, you're unlikely to notice a speed difference between them — whatever comparison headlines promise.

First, the confusion: Llama and llama.cpp are different things

This is the most common question on the topic, and a fair one — the names are nearly identical.

Llama is a family of models from Meta. That's what thinks: a file of weights, trained on text.

llama.cpp is a C/C++ program that runs those weights. That's what computes. The name is historical: the project started as a way to run the first Llama on an ordinary laptop. Since then it's long outgrown that original scope and runs almost anything that ships as GGUF — Qwen, DeepSeek, Gemma, Mistral, and dozens of other families.

So saying "I'm running llama.cpp" says nothing about which model, and "I downloaded Llama" says nothing about what you're running it with.

What llama.cpp is

An inference engine: it takes a .gguf file, lays it out in memory, and computes on the CPU, the GPU, or both at once. Underneath is the ggml tensor library, the low-level layer where the arithmetic actually happens.

llama.cpp exposes a couple of programs on top. The two main ones: llama-cli for running in a terminal, and llama-server, which starts an HTTP server with OpenAI-compatible endpoints and a simple browser web UI.

What GGUF is and what labels like Q4_K_M mean is covered in a separate article.

Who's built on top of it

Ollama is a background daemon controlled by commands. llama.cpp is listed among its supported backends. On top of it, Ollama adds a model registry (ollama pull qwen3), automatic unloading of unused models from memory, and its own REST API.

LM Studio is a desktop app with a window, a chat, and buttons. It runs llama.cpp directly, and on Apple Silicon it can also run MLX. On top, it adds Hugging Face model search right in the interface, a loading-settings panel, and a local server toggle.

The obvious conclusion follows: generation speed across all three is comparable, because they're computing the same thing. If someone claims "twice as fast as Ollama," that's almost always about different settings or a different quantization, not the engine itself.

Where the real difference is

It's in control, and the price of that control.

With llama.cpp, you set the context size, the number of layers on the GPU, sampling parameters, and the KV-cache type yourself. Wrappers hide some of those dials for simplicity and set others for you. That's also where new model architectures show up first — the day they're merged into the project. Wrappers pick them up when they update their own llama.cpp version, usually a gap of a few days to a couple of weeks.

You pay for that with setup time. Ollama installs with one command, LM Studio opens like a regular app, while llama.cpp requires picking a build for your hardware, putting a model next to it, and sorting out launch flags. Nothing terrible, but it eats an evening.

There's a less obvious cost too. With llama.cpp it's easy to accidentally run a model entirely on the CPU, see two tokens a second, and conclude local models just don't work. Wrappers handle that for you, and that's a big part of their value.

What to pick

Situation	Reasonable choice
Need a local API for your own code, minimal fuss	Ollama
Want to browse models and compare them in a chat	LM Studio
Need a new architecture wrappers haven't picked up yet	llama.cpp
Need rare launch flags and precise memory tuning	llama.cpp
Need a finished result, not a model	neither — see below

There's a detailed breakdown of the two wrappers, with endpoint addresses and licensing, in Ollama or LM Studio. Running llama.cpp on Windows without building from source is covered here.

There's a separate fourth engine, vLLM. It gets recommended a lot as the fastest one, but it's built for Linux and server-grade GPUs — why it's not the right pick at home is a different topic.

What none of the three actually do

All three tools solve one problem: get a model to answer. That's where the reason most people install a local model in the first place actually begins — and that's where this stack ends.

Neither llama.cpp, nor Ollama, nor LM Studio will open your folder of contracts, compile a spreadsheet out of them, run the tests in your project, or do it on a schedule at seven in the morning. They hand back text in response to text. Everything else — the client, the step logic, file handling, checking the result — is on you.

Doka sits one level above that. It's an agent: it works with files and the terminal, connects MCP servers, and carries a task through to a result instead of stopping at a chat answer. The model stays local — the GGUF file downloads right inside the app, no separate engine to stand up.

So the honest framing isn't "llama.cpp vs. Ollama" — it's this: if you're interested in inference itself, grab an engine and build the stack by hand. If you need a finished result, install Doka and skip assembling anything.