head to head · open source

llama.cpp vs LLPhant

llama.cpp has 128,619 GitHub stars, 23,351 forks, 2,464 open issues and last shipped today. LLPhant has 1,708 stars, 172 forks, 36 open issues and last shipped 11 days ago. llama.cpp leads on adoption by 7,430% (128,619 vs 1,708 stars). llama.cpp is written in C++ under MIT; LLPhant is written in PHP under MIT. llama.cpp has attracted 18% as many forks as stars, LLPhant 10%. 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 LLPhant ★ 1.7K category AI & Machine Learning

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Side by side

	llama.cpp	LLPhant
GitHub stars	★ 129K	★ 1.7K
License	MIT	MIT
Written in	C++	PHP
Last push	2026-09-18	2026-09-07
Forks	⑂ 23K	⑂ 172
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 LLPhant if

You want the LLPhant feature set and don't need the biggest community
You prefer the MIT license terms
Your stack matches PHP
You evaluated both and LLPhant fits your workflow better

full LLPhant 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 LLPhant

LLPhant The PHP library for Gen AI and Vector Databases We designed this framework to be as simple as possible, while still providing you with the tools you need to build powerful apps. It is compatible with Symfony and Laravel. We are working to expand the support of different LLMs. Right now, we are supporting OpenAI, Anthropic, Mistral, Ollama, llmman, LM Studio, Atlas Cloud and services compatible with the OpenAI API such as LocalAI. Ollama that can be used to run LLM locally such as Llama 2. We want to thank few amazing projects that we use here or inspired us: the learnings from using LangChain and LLamaInd…

read the full LLPhant overview →

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llama.cpp vs Ollama llama.cpp vs vllm llama.cpp vs GPT4All llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec llama.cpp vs langchain4j

Frequently asked questions

Is llama.cpp or LLPhant more popular?

llama.cpp has 128,619 GitHub stars and LLPhant has 1,708. llama.cpp has the larger community by that measure.

Are llama.cpp and LLPhant free?

Both are open source. llama.cpp is licensed under MIT and LLPhant under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and LLPhant?

llama.cpp is written in C++ and LLPhant in PHP. 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 LLPhant?

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