Ollama vs llama.cpp
Ollama — llama.cpp wrapped in a daemon and a model registry. Convenient, at the cost of some control and some lag. llama.cpp — the engine most other tools wrap. Widest format and hardware support, and where new architectures land first.
Side by side
| Ollama | llama.cpp | |
|---|---|---|
| Model formats | GGUF | GGUF |
| KV cache quantization | yes | yes |
| CPU offload | yes | yes |
| MoE expert offload | no | yes |
| Multi-GPU | Inherits llama.cpp's layer split; little direct control. | Layer split by default — capacity adds up, bandwidth does not. Row split available. |
| Concurrency | Fine for a handful of concurrent requests, not for a production workload. | Single-user focused. A server exists but is not built for heavy concurrency. |
| Platforms | Linux, macOS, Windows | Linux, macOS, Windows |
Choose Ollama if…
Getting started, and any application that wants a local OpenAI-compatible endpoint without managing the engine.
Its short model names map to specific quantizations that are not obvious — a bare tag is usually a 4-bit build, not the model's best available. It also lags upstream, so a very new architecture may not load yet even though llama.cpp supports it.
Choose llama.cpp if…
Anyone who wants the newest architectures, the most quantization choices, or the most control over memory.
It is a command-line tool with a lot of flags. The defaults have improved considerably — it now sizes offload automatically — but it expects you to know what you are asking for.
Why there is no speed comparison here
We have not benchmarked these against each other, so we will not rank them on speed. Most of them wrap the same engine, which makes the differences that matter capability rather than throughput — and where a real speed gap exists it usually comes from configuration, such as how much of the model fits on the GPU and whether the cache is quantized, rather than from the runtime itself.