head to head · open source

llama.cpp vs Compartment

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped yesterday. Compartment has 581 stars, 4 forks, 1 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 22,038% (128,619 vs 581 stars). llama.cpp is written in C++ under MIT; Compartment is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, Compartment 1%. llama.cpp was the more recently maintained of the two, and both are self-hostable with no licence fee.

Two open source projects, one decision. Both are free and self-hostable — the differences are community size, license terms, language stack and release pace.

llama.cpp ★ 129K Compartment ★ 581 category AI & Machine Learning

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Side by side

	llama.cpp	Compartment
GitHub stars	★ 129K	★ 581
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-18	2026-09-17
Forks	⑂ 23K	⑂ 4
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 129K stars and counting
You want the MIT license terms
Your stack matches C++
You value the larger contributor base for long-term maintenance

full llama.cpp profile →

pick Compartment if

You want the Compartment feature set and don't need the biggest community
You prefer the Apache-2.0 license terms
Your stack matches Python
You evaluated both and Compartment fits your workflow better

full Compartment profile →

About llama.cpp

llama.cpp is a C/C++ library and set of command line tools for running large language models (LLMs) and vision language models (VLMs) locally. It enables inference without external dependencies, targeting diverse hardware including Apple Silicon, x86 CPUs, NVIDIA GPUs (via CUDA), AMD GPUs (via HIP), and other accelerators. The project lives in the ggml ecosystem, leveraging the ggml tensor computation library for low level operations and quantized model execution.

read the full llama.cpp overview →

About Compartment

Compartment is an Apache 2.0 Python MCP server that gives AI agents encrypted, fully offline long term memory in a single vault on the user's own computer, built for people running Claude Code, Claude Desktop, Cursor, Codex, Hermes Agent, OpenClaw or any other MCP client who want persistent memory with no account, no API key, no network and no telemetry.

read the full Compartment overview →

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Frequently asked questions

Is llama.cpp or Compartment more popular?

llama.cpp has 128,619 GitHub stars and Compartment has 581. llama.cpp has the larger community by that measure.

Are llama.cpp and Compartment free?

Both are open source. llama.cpp is licensed under MIT and Compartment under Apache-2.0. Neither carries a licence fee.

What is the difference between llama.cpp and Compartment?

llama.cpp is written in C++ and Compartment in Python. The practical differences are community size, licence terms, language stack and release cadence — all compared in the table above.

Which should I choose, llama.cpp or Compartment?

Choose llama.cpp if you want the larger community (128,619 stars) or its MIT licence terms. Choose Compartment if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.