llama.cpp vs Ollama
Short answer
llama.cpp: Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. Ollama: The simplest way to download and run open-weight LLMs locally behind an API. Both are in our Local model runtimes category.
Our data does not separate them on cost, hardware, license, activity or deploy method (or one of them does not publish the figure). Decide on features: read the descriptions on each app page and try both.
llama.cpp and Ollama publish no official RAM figure, so we cannot compare hosting cost directly.
Last updated · computed from each project's official docs and live GitHub data · sources
llama.cpp vs Ollama side by side
| llama.cpp | Ollama | |
|---|---|---|
| Category | Local model runtimes | Local model runtimes |
| What it is | Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. | The simplest way to download and run open-weight LLMs locally behind an API. |
| License | MIT | MIT |
| License type | Permissive | Permissive |
| License conditions | Permissive. Internal and commercial use allowed. Individual models have their own licenses. | Permissive. Internal and commercial use allowed. Individual models have their own licenses. |
| Status | Active | Active |
| GitHub stars | 132k | 184k |
| Last commit | Oct 22, 2026 | Oct 22, 2026 |
| Latest release | v0.7.0 | v0.40.8 |
| Minimum hardware | Not published | Not published |
| Recommended hardware | Not published | Not published |
| RAM measured by us | 437 MiB idle, 489 MiB working (our test, vbuild 11804 (c7fc50941), Oct 22, 2026) | 85 MiB idle, 1.9 GiB working (our test, v0.40.8, Oct 22, 2026) |
| GPU | Optional. Runs on CPU (AVX/AVX2/AVX512/AMX, ARM NEON) and supports CPU+GPU hybrid inference for models larger than VRAM. Docker images exist for CUDA, ROCm, SYCL, Vulkan, MUSA and OpenVINO. | NVIDIA GPUs need compute capability 5.0+ and driver 550+ (570+ for compute capability 5.0–6.2). CPU-only also works. |
| Est. server cost | from €11.99/mo (based on our test, not official) | from €19.99/mo (based on our test, not official) |
| Deploy method | Docker (or install script / prebuilt binaries) | Docker (or install script) |
| Known gotchas | 3 listed | 2 listed |
| Telemetry | Not documented | Not documented |
| Calls home by default | Not documented | Yes, by default |
| SSO (OIDC / SAML / LDAP) | Not documented | Not documented |
| Audit log | Not documented | Not documented |
| RBAC | Not documented | Not documented |
| Details | Full llama.cpp guide → | Full Ollama guide → |
Key differences
- Deploy: llama.cpp: Docker (or install script / prebuilt binaries) · multi-container stack. Ollama: Docker (or install script) · multi-container stack.
- GitHub stars: llama.cpp: 132k. Ollama: 184k.
- Latest release: llama.cpp: v0.7.0 (Oct 15, 2026). Ollama: v0.40.8 (Oct 21, 2026).
Same for both: Category: Local model runtimes; License: MIT (Permissive); Official hardware: not published; GPU: optional / see notes; Est. server cost: not estimated (no official RAM figure); Status: Active; Last commit: Oct 22, 2026.
The two projects share too few published figures for a data-based recommendation. We do not fill gaps with guesses.
llama.cpp vs Ollama: FAQ
llama.cpp or Ollama: which should a small business choose?
Our data does not separate them on cost, hardware, license, activity or deploy method (or one of them does not publish the figure). Decide on features: read the descriptions on each app page and try both. llama.cpp and Ollama publish no official RAM figure, so we cannot compare hosting cost directly.
Which is cheaper to self-host, llama.cpp or Ollama?
llama.cpp: not estimated (no official RAM figure). Ollama: not estimated (no official RAM figure). Our estimate is the cheapest Hetzner Cloud plan that meets each project's official RAM and vCPU figure; it covers the server only.
How much hardware do llama.cpp and Ollama need?
llama.cpp: not published. Ollama: not published.
What is the license difference between llama.cpp and Ollama?
llama.cpp: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. Ollama: MIT (Permissive). Permissive. Internal and commercial use allowed. Individual models have their own licenses. This is a summary, not legal advice.
Is llama.cpp or Ollama more actively maintained?
llama.cpp: Active, last commit Oct 22, 2026. Ollama: Active, last commit Oct 22, 2026. GitHub data refreshed Oct 22, 2026.
Which is easier to deploy, llama.cpp or Ollama?
llama.cpp installs via Docker (or install script / prebuilt binaries) (multi-container stack); Ollama via Docker (or install script) (multi-container stack). llama.cpp lists 3 known gotchas and Ollama lists 2; see each app page.
Related
Sources
- llama.cpp GitHub repository: github.com/ggml-org/llama.cpp
- llama.cpp documentation: github.com/ggml-org/llama.cpp/blob/master/docs/docker.md
- llama.cpp license: github.com/ggml-org/llama.cpp/blob/master/LICENSE
- llama.cpp hardware requirements: github.com/ggml-org/llama.cpp#description
- llama.cpp deploy instructions: github.com/ggml-org/llama.cpp/blob/master/tools/server/README.md#docker
- Ollama GitHub repository: github.com/ollama/ollama
- Ollama documentation, Ollama deploy instructions: docs.ollama.com/docker
- Ollama license: github.com/ollama/ollama/blob/main/LICENSE
- Ollama hardware requirements: docs.ollama.com/gpu
- Hetzner Cloud pricing used for cost estimates (read 2026-10-22): www.hetzner.com/cloud/regular-performance
- GitHub REST API (stars, commits, releases): docs.github.com/en/rest
Every value is generated from the same verified data as the app pages. Spotted something wrong? Tell us.