head to head · open source

llama.cpp vs vllm-omni

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. vllm-omni has 7,494 stars, 2,037 forks, 1,977 open issues and last shipped yesterday. llama.cpp leads on adoption by 1,677% (133,181 vs 7,494 stars). llama.cpp is written in C++ under MIT; vllm-omni is written in Python under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, vllm-omni 27%. 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 vllm-omni ★ 7.5K category AI & Machine Learning

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Side by side

	llama.cpp	vllm-omni
GitHub stars	★ 133K	★ 7.5K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 2.0K
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 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 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 vllm-omni

Easy, fast, and cheap omni modality model serving for everyone Latest News 🔥 [2026/08] We released 0.28.0, featuring production ready MiniMax H3 serving on GPU and NPU, a unified AR/DiT paged KV cache runtime, and enhanced realtime full duplex serving for the MiniCPM o series. [2026/08] VeRL Omni v0.2.0 is released: faster diffusion RL powered by vLLM Omni (request level/step wise batching with FA3), rebuilt Qwen3 Omni multimodal training (DPO & GSPO), plus LTX 2.3, Qwen Image Edit support and more. See the release notes. [2026/08] We released 0.26.0 aligned with the vLLM 0.26 release line, featuring MiniMax H3…

read the full vllm-omni overview →

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Frequently asked questions

Is llama.cpp or vllm-omni more popular?

llama.cpp has 133,181 GitHub stars and vllm-omni has 7,494. 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 (133,181 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.