What is the difference between llama.cpp and Ollama?

llama.cpp is the low-level inference engine that actually runs the model, while Ollama is a tool built on top of llama.cpp that adds easy model downloads, automatic settings and an always-on API server. Ollama uses llama.cpp underneath, so the same model and quantization need the same memory in both.

Is Ollama built on llama.cpp?

Yes. Ollama wraps the llama.cpp inference engine with model management, sensible default settings and a REST API server. That is why a given model and quantization uses the same VRAM and RAM whether you run it through Ollama or llama.cpp directly.

Should I use llama.cpp or Ollama?

Use llama.cpp directly if you want maximum control over how a model runs, the newest features first, or to embed inference in your own software. Use Ollama if you want to run and serve models with minimal setup, since it handles downloads, settings and an API server for you.

Do llama.cpp and Ollama need the same amount of memory?

Yes. Because Ollama runs the llama.cpp engine underneath, the VRAM and RAM required for a given model and quantization are the same in both. The memory figures on AI Local Check apply to either.

llama.cpp vs Ollama: Engine vs Manager

By Lefi Abdelmonem

Author · AI Local Check

This comparison trips people up because the two aren't really rivals: Ollama is built on top of llama.cpp. One is the engine; the other is a friendly manager wrapped around it. Knowing which layer you're dealing with tells you exactly which to reach for. Both are free, open source, and run the same GGUF models on Windows, macOS and Linux.

The short answer

llama.cpp is the low-level inference engine — the C/C++ code that actually runs the model. Choose it when you want maximum control, the newest features first, or to embed inference in your own software.
Ollama is a tool that wraps llama.cpp with easy model downloads, automatic settings and a background API server. Choose it when you want to run and serve models without managing the details yourself.

If that sounds like the trade-off between a car engine and a car, that's the right mental model.

Side by side

	llama.cpp	Ollama
What it is	The inference engine	A manager built on llama.cpp
Best for	Control, embedding, latest features	Easy running & serving
Getting a model	Download the GGUF yourself, pass the file path	`ollama run <model>` pulls it for you
Settings	Manual flags (layers, context, threads)	Sensible defaults, optional overrides
API server	Yes (`llama-server`)	Yes (REST, always on)
Model management	You organise the files	Built-in pull / list / remove
Learning curve	Steeper	Gentle
Source	Open source	Open source

Because Ollama runs llama.cpp underneath, a given model and quantization needs the same memory in both — the figures on this site apply either way.

When llama.cpp is the right call

Reach for llama.cpp directly when you want to tune exactly how a model runs — how many layers to offload to the GPU, the precise context size, threads, batch size — or when you're building inference into your own application and want the smallest, most direct dependency. It also tends to get new model architectures and quantization formats first, since everything else is downstream of it. The cost is that you manage GGUF files and flags yourself.

When Ollama is the right call

Reach for Ollama when you'd rather not think about any of that. ollama run llama3.1 downloads the model, picks reasonable settings, and starts a server on localhost:11434 that other apps can talk to. Model management (pull, list, remove) is built in. For most people running models day to day — and for serving a model to a chat UI or script — Ollama is simply less friction.

What about LM Studio?

LM Studio is a third option: a full graphical app that also runs GGUF via llama.cpp. If you want a point-and-click interface with a built-in chat window, see Ollama vs LM Studio. Roughly: llama.cpp is the engine, Ollama is the command-line manager, and LM Studio is the desktop app — all three run the same models.

The bottom line

You're not really choosing between llama.cpp and Ollama — you're choosing how much of llama.cpp you want to handle yourself. Want control and the latest features? Use llama.cpp. Want to just run and serve models? Use Ollama, and know it's llama.cpp doing the work underneath. Either way, first check which models fit your hardware, then get started running one locally.

→ Check what your exact GPU can run