head to head · open source

llama.cpp vs dstack

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. dstack has 2,252 stars, 262 forks, 68 open issues and last shipped yesterday. llama.cpp leads on adoption by 5,610% (128,581 vs 2,252 stars). llama.cpp is written in C++ under MIT; dstack is written in Python under MPL-2.0. llama.cpp has attracted 18% as many forks as stars, dstack 12%. 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 dstack ★ 2.3K category AI & Machine Learning

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Side by side

	llama.cpp	dstack
GitHub stars	★ 129K	★ 2.3K
License	MIT	MPL-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 262
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 dstack if

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

full dstack 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 dstack

dstack is an open source Python project in the AI and machine learning infrastructure ecosystem. It is a unified control plane for GPU provisioning and orchestration that works with GPU clouds, Kubernetes, and on prem clusters. The project supports NVIDIA, AMD, Google TPU, and Tenstorrent accelerators, and it is compatible with open source tools and frameworks.

read the full dstack overview →

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Frequently asked questions

Is llama.cpp or dstack more popular?

llama.cpp has 128,581 GitHub stars and dstack has 2,252. llama.cpp has the larger community by that measure.

Are llama.cpp and dstack free?

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

What is the difference between llama.cpp and dstack?

llama.cpp is written in C++ and dstack 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 dstack?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose dstack if its feature set, stack or MPL-2.0 licence fits better. Both are self-hostable.