Ollama vs llama.cpp.
TL;DR: this isn’t a rivalry — Ollama is built on top of llama.cpp. llama.cpp is the inference engine: maximum control, every flag, newest features first. Ollama is the packaging: model management, a background service, and an API, at the cost of some control and a version lag. Start with Ollama; go direct when you hit its walls.
The relationship, not the rivalry
llama.cpp (started by Georgi Gerganov) is the open-source C/C++ engine that actually computes tokens, and the home of the GGUF format and its quantization schemes. Ollama embeds that engine and wraps it with the things the engine deliberately doesn’t do: a model registry with one-command downloads, lifecycle management (load, unload, keep-alive), and a stable local API. Every token Ollama produces is produced by llama.cpp code.
What Ollama adds
One command from zero to chatting; models organized centrally instead of GGUF files scattered in downloads folders; a service that loads and unloads models on demand; Modelfiles to version your system prompts and parameters; and the port-11434 API the whole local-AI ecosystem plugs into. For 90% of users, 90% of the time, this is the right trade.
What going direct gets you
Every flag: exact layer offload (-ngl), context size, batch sizes, KV-cache quantization, speculative decoding — Ollama exposes a curated subset, llama.cpp exposes everything. Freshness: new architectures, quant formats and optimizations land in llama.cpp first and reach Ollama releases weeks later. Its own server: llama-server ships a web UI and OpenAI-compatible API with no wrapper in between. No compiling required either — prebuilt binaries exist for every platform.
Performance reality
Same engine ⇒ same ceiling. Benchmarks showing llama.cpp “beating” Ollama almost always compare different defaults — context length, offloaded layers, batch size — not different engines. Tuned identically, they are within noise. The honest version: llama.cpp lets you tune closer to your hardware’s ceiling; Ollama picks safe defaults so nothing breaks. Know your ceiling first with the VRAM calculator.
When to switch (and how)
Go direct when you need a flag Ollama doesn’t expose, a feature that hasn’t landed in it yet, or the last 10–15% of throughput on a serving box. The migration is gentle: it’s all GGUF — the model blobs Ollama already downloaded can be pointed at directly by llama.cpp, no re-download needed. And nothing stops you running both: llama.cpp for the tuned workhorse, Ollama for everything else.
Frequently asked questions
Is Ollama just a wrapper around llama.cpp?
Essentially yes — plus the parts that make it usable daily: a model registry, service management, Modelfiles, and a standard local API. "Just a wrapper" undersells how much friction those remove.
Is llama.cpp faster than Ollama?
Not inherently — Ollama uses llama.cpp inside. Configured identically they perform the same; llama.cpp simply lets you tune more parameters, which can buy 10-15% on a given setup.
Can I use my Ollama models with llama.cpp directly?
Yes. Ollama stores models as standard GGUF blobs on disk; llama.cpp can load those files directly, so you don't need to re-download anything.
Do I need to know C++ to use llama.cpp?
No. Prebuilt binaries are published for Windows, macOS and Linux — using llama.cpp means running command-line tools with flags, not writing or compiling C++ code.
By Mohamed Meguedmi — independent comparator of locally-runnable LLMs, benchmarked on a real RTX 5070 Ti (data CC BY 4.0). See the local LLM leaderboard and the best Ollama models.