Is llama.cpp faster than Ollama?

Not inherently — it is the same engine. A hand-tuned llama.cpp build can beat Ollama's defaults on specific hardware, mostly by offloading more layers than Ollama's estimate chose or by targeting your CPU's instruction set. That is a configuration difference, not an engine difference.

Can Ollama use a GGUF I already downloaded?

Yes. Write a short Modelfile with a FROM line pointing at the file path and run ollama create. It imports the file rather than downloading it again, which is worth doing before you pull a second copy of a large model.

Which should I use for an API?

Ollama, unless you need something specific. Its endpoint is always running and OpenAI-compatible. llama.cpp's llama-server does the same job but you configure and supervise it yourself.

Does Ollama support GPU offload automatically?

It estimates how many layers fit and offloads that many. The estimate is usually reasonable but conservative on machines with limited VRAM, and it is one of the few places where hand-tuning llama.cpp can win meaningfully.

Runners

llama.cpp vs Ollama

Ollama runs on llama.cpp. Comparing them is really a question of how much control you want and how much setup you are willing to do. Here is what you gain and give up in each direction.

The short answer

Ollama is built on llama.cpp, so this is not two engines competing — it is an engine and a layer over it. Ollama adds model management, a stable API and chosen defaults. llama.cpp gives you every flag, every new feature the day it lands, and a build tuned for your exact hardware. Start with Ollama. Move to llama.cpp when a default is in your way.

llama.cpp

The engine. Total control, manual setup, newest features first.

Ollama

The wrapper. Good defaults, model registry, an API, no flags to learn.

llama.cpp vs Ollama at a glance

Factor	llama.cpp	Ollama
What it is	The inference engine itself	A service wrapping that engine
Getting models	Download the GGUF yourself and pass a path	ollama pull by name from a registry
Control	Every flag: layer offload, context, rope scaling, sampling, grammars	A curated subset via Modelfile and environment variables
Setup	Compile it, or fetch a release build for your backend	One installer, runs as a service
New features	The day they merge	When the bundled engine is updated
API	llama-server provides one, configured by you	Always on, OpenAI-compatible, on a fixed port

What Ollama actually adds

Three things, all of them about friction. A registry, so a model is a name rather than a URL and a filename. A background service with a stable endpoint, so anything on your machine can reach the model without knowing how it is loaded. And defaults — context length, GPU offload, sampling — chosen so that the first run works.

That last one is underrated. llama.cpp's most consequential flag is the number of layers to put on the GPU, and getting it wrong means either leaving performance on the table or failing to load. Ollama estimates it from your hardware and the model size.

Modelfiles then let you freeze a configuration under a name — a system prompt, a temperature, a context window — and call it like any other model. For anyone building on top of local inference, that is the feature that makes the difference.

What you give up

Control over the flags Ollama does not expose, and timing. llama.cpp moves quickly; support for a new architecture, a new quantization type or a new sampling method appears there first and reaches Ollama when its bundled engine is updated. If you need something the week it lands, you need the engine.

You also give up build-level tuning. Compiling llama.cpp yourself lets you target your exact backend and CPU instruction set, which matters most on unusual hardware — an older AMD card, an Intel GPU, a machine doing significant CPU inference.

And you give up transparency. When output is wrong, being able to see the exact prompt the engine assembled, the exact sampler chain and the exact number of layers offloaded turns a mystery into a bug. llama.cpp shows you all of it.

They are not mutually exclusive

The common pattern is Ollama for everyday use and llama.cpp for the cases it does not cover — a model with an unusual architecture, an experiment with speculative decoding, a grammar that constrains output to valid JSON.

Both read the same GGUF files, though not from the same place: Ollama keeps models in its own store with content-addressed names, so a file you downloaded for llama.cpp is not automatically visible to it. You can import one with a Modelfile that points at the path rather than downloading it twice.

If you are running a model for other people rather than for yourself, neither is the right answer — that is what vLLM is for, and the deciding factor is concurrent requests rather than control.

Choose llama.cpp when

You need a flag Ollama does not expose
You want support for a model or feature the day it merges
You are building for unusual hardware and want a tuned build
You are debugging prompt assembly or sampling behaviour

Choose Ollama when

You want models to work without reading documentation
Something else on your machine needs to call the model over HTTP
You want to define named model configurations and reuse them
You would rather not compile anything

Work it out for your own setup

VRAM calculatorHow many layers will fit on your GPU.GGUF size calculatorPick the quantization level that leaves room for context.Hardware matrixWhat your specific machine can run.

Common questions

Is llama.cpp faster than Ollama?: Not inherently — it is the same engine. A hand-tuned llama.cpp build can beat Ollama's defaults on specific hardware, mostly by offloading more layers than Ollama's estimate chose or by targeting your CPU's instruction set. That is a configuration difference, not an engine difference.
Can Ollama use a GGUF I already downloaded?: Yes. Write a short Modelfile with a FROM line pointing at the file path and run ollama create. It imports the file rather than downloading it again, which is worth doing before you pull a second copy of a large model.
Which should I use for an API?: Ollama, unless you need something specific. Its endpoint is always running and OpenAI-compatible. llama.cpp's llama-server does the same job but you configure and supervise it yourself.
Does Ollama support GPU offload automatically?: It estimates how many layers fit and offloads that many. The estimate is usually reasonable but conservative on machines with limited VRAM, and it is one of the few places where hand-tuning llama.cpp can win meaningfully.

Guides that go deeper

Longer reads on the concepts behind this comparison.

Related comparisons

Ollama vs LM StudioRunners Ollama vs vLLMRunners GGUF vs SafetensorsModel formats