head to head · open source

llama.cpp vs dstack

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. dstack has 2,299 stars, 272 forks, 68 open issues and last shipped yesterday. llama.cpp leads on adoption by 5,693% (133,181 vs 2,299 stars). llama.cpp is written in C++ under MIT; dstack is written in Python under MPL-2.0. llama.cpp has attracted 19% 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 ★ 133K dstack ★ 2.3K category AI & Machine Learning

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Side by side

	llama.cpp	dstack
GitHub stars	★ 133K	★ 2.3K
License	MIT	MPL-2.0
Written in	C++	Python
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 272
Self-hosting	Yes	Yes
Data ownership	Your server	Your server

pick llama.cpp if

You weight community size — 133K 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 LLM inference in C/C++ ggml / ops / maintainer PRs%20sort%3Aupdated desc) / dev stats / lib llama API / llama server REST API Quick start A few options to get llama.cpp installed on your machine: Visit and follow the instructions Run with Docker see our Docker documentation Download pre built binaries from the releases page Build from source by cloning this repository check out our build guide Once installed: VLM session with llama cli Built in web UI against llama serve Description The main goal of llama.cpp is to enable LLM (and VLM) inference with minimal setup and state of the art performance on a…

read the full llama.cpp overview →

About dstack

dstack is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on prem clusters. It streamlines development, training, and inference, and is compatible with any hardware, open source tools, and frameworks. Accelerators dstack supports NVIDIA , AMD , Google TPU , and Tenstorrent accelerators out of the box. Latest news ✨ [2026/08] dstack 0.21.0: Pydantic v2, Gateway replicas [2026/07] dstack 0.20.29: Presets — agent driven inference optimization (experimental) [2026/07] dstack 0.20.27: Slurm backend [2026/05] dstack 0.20.21: Kubernetes multiple clusters…

read the full dstack overview →

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Frequently asked questions

Is llama.cpp or dstack more popular?

llama.cpp has 133,181 GitHub stars and dstack has 2,299. 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 (133,181 stars) or its MIT licence terms. Choose dstack if its feature set, stack or MPL-2.0 licence fits better. Both are self-hostable.