Overview
Use Ollama to pull and run models with one command and a stable local API; use llama.cpp when the runtime itself is the thing you need to control or ship. Ollama manages model downloads, chat templates, and serving, and exposes compatibility with OpenAI and Anthropic client libraries on port 11434, plus optional cloud-hosted models. llama.cpp is a dependency-free C/C++ inference library and llama-server that loads GGUF files, with a built-in web UI, an OpenAI-compatible API, and multi-model router support.
Comparison
| Dimension | Ollama | llama.cpp |
|---|---|---|
| Install | Installer or package; ollama run <model> | Prebuilt binaries or build from source |
| Model handling | Model library, ollama pull, Modelfile for prompts and parameters | You supply GGUF files, usually from Hugging Face; you pick the quantization |
| API | Native API plus OpenAI and Anthropic client compatibility | OpenAI-compatible HTTP from llama-server; C and C++ library API |
| Hardware backends | Handled for you by the installer | Metal, CUDA, HIP, Vulkan, SYCL, CPU, and others; you choose build flags |
| Quantization | Curated per model | 1.5-bit to 8-bit integer formats and your own conversions |
| Multiple models | Loads and unloads models on demand | llama-server supports multi-model routing |
| Embedding in an app | Separate daemon | Link the library into your process |
| Tuning | Few parameters exposed | Threads, batch sizes, mmap, mlock, context, and backend options |
Pick Ollama when
Pick Ollama for development machines and small self-hosted services.
- Laptop loops for RAG, agents, and evals: pull a model and point an OpenAI-compatible client at
http://localhost:11434/v1. See ollama and rag. - Teams that want one supported way to download and update models.
- Running a model beyond local hardware through Ollama’s cloud models, with the same API.
- Tune prompts and parameters in a
Modelfilerather than rebuilding anything.
Pick llama.cpp when
Pick llama.cpp when you need the engine, not the manager.
- Shipping inference inside a desktop app, CLI, or service by linking the library.
- A quantization, backend, or build option Ollama does not expose, such as Vulkan on non-CUDA GPUs.
- Production hosting with tuned flags, your own load balancer, and a pinned binary.
- Tracking new model architectures directly in the engine.
Use Ollama in development and llama-server in production if your clients only speak the OpenAI-compatible API; verify chat templates and sampling defaults match before comparing outputs.
Migrate
Ollama to llama.cpp: locate the GGUF Ollama stored locally, point llama-server at it, and reproduce the chat template and parameters from the Modelfile. llama.cpp to Ollama: write a Modelfile that mirrors your template, then ollama create; a custom quantization may not be reproducible. For hosted APIs instead of local inference, see claude-vs-gpt; for embeddings workloads see embeddings.
FAQ
Is llama.cpp harder to set up than Ollama?
Yes; Ollama runs a model with an installer and ollama run <model>, while llama.cpp needs prebuilt binaries or a source build plus GGUF files you supply. In exchange llama.cpp exposes threads, batch sizes, mmap, mlock, context, and backend options.
Does Ollama have an OpenAI-compatible API?
Yes; point an OpenAI-compatible client at http://localhost:11434/v1. Ollama also exposes compatibility with Anthropic client libraries, and llama-server offers an OpenAI-compatible HTTP API too.
Should I use Ollama or llama.cpp in production?
Use llama.cpp for production hosting with tuned flags, your own load balancer, and a pinned binary. Ollama in development and llama-server in production works if clients only speak the OpenAI-compatible API and chat templates match.