llama.cpp vs ollama: a low-level engine and a managed runtime that builds on it
llama.cpp is the C/C++ inference engine itself, while ollama is a Go-based runtime that uses llama.cpp as its backend and wraps it in a model registry, a REST API and launcher integrations. They are complementary layers, so the real choice is between running the engine directly and taking the managed stack on top of it.
At a glance
| Project | ggml-org/llama.cpp | ollama/ollama |
|---|---|---|
| Licence | MITPermissive: commercial use allowed | MITPermissive: commercial use allowed |
| Maintenance | Commits in the last dayLast push September 29, 2026 | Commits in the last six monthsLast push September 27, 2026 |
| Language | C++ | Go |
| GitHub stars | 129,881 | 181,861 |
| Read more | Our analysisGitHub | Our analysisGitHub |
Which one to choose
Choose llama.cpp if you want direct control over inference: pick your own quantization, target an unusual backend, run CPU and GPU together on models that exceed VRAM, or embed inference in your own C/C++ or other application.
Choose ollama if you want a single command to pull and run a model, a REST API on port 11434, and ready-made integrations with coding agents and chat apps, and if you can accept the model library and the default runtime as they are.
Two layers, not two rivals
Comparing llama.cpp and ollama as competitors misses how the two projects relate. Ollama's README lists llama.cpp as its supported backend. Ollama does not reimplement inference; it is a Go-based distribution and management layer that downloads models, keeps them in a local registry, serves them over HTTP, and hands the actual token generation to llama.cpp. That means the engineering question is not which engine is better, because in the common case ollama uses the same engine. The question is how much of the stack you want to own. Run llama.cpp directly and you configure and update the engine yourself. Run ollama and you trade that control for a runtime that handles model downloads, the API surface and default settings for you.
What llama.cpp gives you when you take the engine directly
llama.cpp is a dependency-free C/C++ implementation of LLM and VLM inference, and its README lists backends for CUDA on NVIDIA GPUs, HIP on AMD GPUs, MUSA on Moore Threads GPUs, Metal on Apple Silicon, Vulkan, SYCL, OpenCL and WebGPU, among others, plus CPU paths with AVX, AVX2, AVX512 and AMX on x86 and RVV on RISC-V. The README also documents 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit and 8-bit integer quantization, and CPU plus GPU hybrid inference that runs models larger than total VRAM across both. For a team that needs a specific quantization level, a specific accelerator, or a model that does not fit in memory, those options are the point. The trade-off is that using them is your job: the project publishes pre-built binaries, a Docker image and a build guide, but you choose the backend at build time, keep the binary updated across its release cadence, and follow the CLI as it evolves.
What ollama gives you on top
ollama's README centers on a short workflow: install with a one-line script on macOS, Windows or Linux, or use the official Docker image, then run a model by name, for example ollama run gemma4. The runtime exposes a REST API on localhost:11434 with chat and API endpoints, and official Python and JavaScript clients. A newer part of the project is the launcher: the README shows ollama launch claude, ollama launch openclaw and related commands that connect the runtime to coding agents such as Claude Code, Codex, Copilot CLI and OpenCode, and to assistants such as OpenClaw. The launcher integrations are documented per tool on docs.ollama.com. For a developer who wants a local model behind a stable HTTP endpoint in a few minutes, this is the whole point of the project: model discovery, download and serving are handled for you, including where to find models on the ollama library.
Getting each one running
llama.cpp offers four entry points in its README: the llama.app installer, Docker, pre-built binaries from the releases page, and building from source. Once installed, the quick start is llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF to download a GGUF model from Hugging Face and run it, or llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF for an OpenAI-compatible API server with a built-in web UI. The GGUF format matters here: llama.cpp expects models in that format, so you verify the target model has a GGUF conversion before you commit. ollama hides that step. Its README quick start is ollama run gemma4, with the model pulled from the ollama library, and the library becomes both a convenience and a boundary: anything you want to run must exist there or be imported through the documented importing mechanism. Both projects are MIT licensed, so only your own time is at stake when you try either.
Operations: one process you own, one stack that owns defaults
In daily operation the differences show up in what you configure. With llama.cpp you pick the backend, the quantization, the batch and context settings and the server options, and you run the binary the way you run any service. The server is OpenAI-compatible, which lets existing OpenAI clients point at it by changing the base URL. With ollama, the runtime manages the model lifecycle: the CLI reference, REST API reference and Modelfile reference are the surfaces you work with, and the launcher handles wiring models into agents and chat apps. For scaling, llama.cpp documents multi-GPU usage and hybrid CPU plus GPU execution, which matters for models larger than available video memory. ollama's README does not document equivalent hybrid execution controls, so teams that need that specific capability should plan to work with llama.cpp or accept the default behavior. Neither project's README section seen here documents a clustered horizontal scaling story in detail, so a fleet of servers is a topic to verify against current docs before relying on it.
Where each falls short
llama.cpp's weakness is its breadth. The release history shows a rapid cadence, and our earlier analysis of the repository notes that the evolving CLI and API can break workflows; teams that need a stable interface for years should pin a release tag and test upgrades deliberately, because no long-term API guarantee is documented. The README also frames some backends as in progress, such as Snapdragon and OpenVINO, so a hardware target named in the table is not automatically a finished backend. ollama's weakness is the opposite: it hides the controls that llama.cpp exposes. The analysis of the ollama repository calls out that fine-grained control over quantization and custom sampling is limited, that low-memory devices are a weak spot because you cannot reach past the runtime into the engine's settings, and that the launcher integrations are newer and less documented than the core runtime, so verify them with the actual agent or chat tool before depending on them. On maintenance, both repos are healthy: llama.cpp's last push was September 10, 2026 and ollama's was September 11, 2026, and neither is archived.
Licence and maintenance implications
Both projects ship under the MIT licence, which puts no meaningful constraint on commercial use. The maintenance situation is also fine on both sides: llama.cpp pushed last on September 10, 2026 and ollama on September 11, 2026, and neither repository is archived. The real maintenance cost differs by layer. With llama.cpp you inherit a fast-moving upstream that publishes frequent releases, so you need a policy for pinning and upgrading the engine inside whatever wraps it. With ollama you depend on one more layer, the runtime and its registry, and on the launcher integrations, which the project's own docs present as per-tool pages; upgrade risk sits there rather than in the inference kernel, which stays inside llama.cpp.
Bottom line
Pick ollama if you want local models behind a REST API and agent integrations without managing an inference engine, provided your models are in the library and your hardware meets their memory needs. Pick llama.cpp if you need specific quantization, an unusual backend, hybrid CPU and GPU execution, or an engine embedded in your own software, and if you can budget for tracking its releases. For most single-machine setups that just want a model running today, starting with ollama is the faster path, since it wraps the same engine; teams with unusual hardware or a need to tune the engine itself should verify their backend and model format against llama.cpp's supported backends table and GGUF requirements first.