llama.cpp vs Ollama
Two of the top model serving, side by side: score, setup, license, activity and what each review found.
llama.cpp
C/C++ inference engine serving GGUF models over an OpenAI-compatible API
Ollama
Runs open-weight models locally behind a CLI and REST API
| What we compare | llama.cpp | Ollama |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 94, widely used | 98, widely used |
| Freshness | 100, active | 100, active |
| Maintenance | 89, healthy | 82, healthy |
| Easy to run | 50, easy | 33, some setup |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 130.8k | 182.7k |
| License | MIT (permissive) | MIT (permissive) |
| Last commit | Oct 2026 | Oct 2026 |
| Last release | Oct 2026 | Oct 2026 |
| Language | Not stated | Not stated |
| Docker | Yes | Yes |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
llama.cpp
llama.cpp is a C/C++ inference engine for LLMs and VLMs with no dependencies, built on ggml. llama serve starts an OpenAI-compatible API server with a built-in web UI, pulling GGUF models straight from Hugging Face, with 1.5 to 8-bit quantization and CPU+GPU hybrid offload for models larger than VRAM. Backends cover CUDA, HIP, Metal, Vulkan, SYCL, OpenCL, CANN, MUSA and WebGPU.
Who it is for: engineers who want a lean local inference server
Strengths
- Plain C/C++ with no runtime dependencies; prebuilt binaries and Docker
- Backends for NVIDIA, AMD, Apple Metal, Intel SYCL, Vulkan, Ascend, Moore Threads
- Hybrid CPU+GPU offload runs models larger than available VRAM
- Built-in web UI and OpenAI-compatible server via llama serve
Weaknesses
- GGUF model format only
- README gives no port, auth or sizing guidance; see tools/server docs
- Install script is curl piped to sh; otherwise build from source
- OpenVINO backend still in progress
- GPU optional
- Docker
- Models: GGUF models from Hugging Face (e.g. Qwen3.5-0.8B-GGUF)
Ollama
Ollama runs open-weight models locally with a CLI and a REST API on port 11434, pulling models from its own library (for example gemma4) and using llama.cpp as the inference backend. Install scripts cover macOS, Windows and Linux, and an official Docker image exists. The ollama launch command wires it into coding agents such as Claude Code, Codex, Copilot CLI and OpenCode, or into OpenClaw as a chat assistant.
Who it is for: anyone wanting local models behind a simple API
Strengths
- One command pulls and runs a model; REST API on 11434
- Official Docker image plus Python and JavaScript libraries
- ollama launch integrates with Claude Code, Codex, Copilot CLI, OpenCode
- Broad ecosystem: dozens of web, desktop and IDE clients listed
Weaknesses
- Single inference backend: llama.cpp
- Install is a curl piped to sh script
- README gives no RAM or VRAM guidance per model size
- Models come from Ollama's own registry; others need import steps
- GPU optional
- Docker
- Models: Ollama library models (e.g. gemma4), GGUF via llama.cpp
- port 11434