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

Three names come up whenever someone asks "how do I run an LLM on my own machine?" They are not exact rivals: one is an engine, the other two are friendly layers around it. Here is how to choose.

How they relate#

At a glance#

Ollama#

You install it, pull a model and run it:

ollama pull <model>
ollama run <model>

It also runs a local server. Hit it from code:

const res = await fetch("http://localhost:11434/api/chat", {
  method: "POST",
  body: JSON.stringify({
    model: "<model>",
    stream: false,
    messages: [{ role: "user", content: "Explain closures in one paragraph." }],
  }),
});
const data = await res.json();
console.log(data.message.content);

Pros: simplest developer workflow, scriptable, large library of models. Cons: fewer low-level knobs than raw llama.cpp.

LM Studio#

A desktop app. You search for a model, download it, and chat. It can also start a local server so your editor or app can use it. It is the easiest way to try models and compare them without touching a terminal. Cons: GUI-first, less natural for automation and servers.

llama.cpp#

The engine under the others. You choose the model file, the quantization, the number of layers on GPU, the context size, the threads. It runs on almost anything: NVIDIA, AMD, Apple Silicon, plain CPUs.

llama-server -m ./model-Q4_K_M.gguf -c 8192

Pros: maximum control and portability, great for edge devices. Cons: you manage files and flags yourself.

Which one should you pick?#

• "I want to build something with a local model." Ollama.
• "I want to try many models and chat with them." LM Studio.
• "I need to run on a Raspberry Pi, tune every flag, or embed it." llama.cpp.
• "I need many users hitting one model at high throughput." Look at a serving engine such as vLLM, which is a different class of tool.

You can use more than one. They all read the same kind of GGUF file.

Before you start#

New to this? Follow how to run an LLM locally first.

1. Estimate memory first: how much RAM do you need.
2. Pick a quantization, usually 4-bit.
3. Test the model on your own cases with a small eval. A local model that is fine for classification may not be fine for complex coding.