llama.cpp vs Ollama

First, the naming: “llama” here means llama.cpp (the software that runs models), not Meta’s Llama model family. Ollama’s name is a play on it, which is fitting because Ollama is built on top of llama.cpp, they’re not really competitors so much as different layers of the same stack.

llama.cpp is the low-level C/C++ engine (powered by GGML ) that actually runs GGUF models. It’s a library plus command-line tools (llama-cli, llama-server) and ships the GPU backends: CUDA , Metal , Vulkan , for handing work to the GPU. It’s maximally flexible and fast, but you manage everything yourself: find the right .gguf, pick the quantization (how much the model is shrunk), set the context length and GPU flags, wire up the chat template.

Ollama wraps that engine to make it easy to use. It adds a model catalog (ollama pull llama3), automatic download and caching, sensible defaults baked into a Modelfile, and a background server that other apps can talk to over a simple web API (including an OpenAI-compatible one). You give up some fine control in exchange for not having to think about any of the plumbing.

Rule of thumb: reach for Ollama when you want a model running in one command with a clean local API; drop down to llama.cpp when you need the newest features, custom build options, or to squeeze out maximum performance. (LM Studio is a third option, a point-and-click app over the same llama.cpp core.)