head to head · open source

llama.cpp vs vllm-omni

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. vllm-omni has 6,855 stars, 1,747 forks, 1,977 open issues and last shipped yesterday. llama.cpp leads on adoption by 1,776% (128,581 vs 6,855 stars). llama.cpp is written in C++ under MIT; vllm-omni is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, vllm-omni 25%. vllm-omni 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 vllm-omni ★ 6.9K category AI & Machine Learning

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Side by side

	llama.cpp	vllm-omni
GitHub stars	★ 129K	★ 6.9K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-17
Forks	⑂ 23K	⑂ 1.7K
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 vllm-omni if

You want the vllm-omni 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 vllm-omni fits your workflow better

full vllm-omni 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 vllm-omni

vLLM Omni is an Apache 2.0 Python framework from the vLLM project that extends vLLM's text only inference engine to serve omni modality models — text, image, audio, video, and action — for teams that need to run diffusion transformers, autoregressive models, and realtime duplex pipelines from a single serving stack.

read the full vllm-omni overview →

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Frequently asked questions

Is llama.cpp or vllm-omni more popular?

llama.cpp has 128,581 GitHub stars and vllm-omni has 6,855. llama.cpp has the larger community by that measure.

Are llama.cpp and vllm-omni free?

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

What is the difference between llama.cpp and vllm-omni?

llama.cpp is written in C++ and vllm-omni 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 vllm-omni?

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