head to head · open source

llama.cpp vs pegainfer

llama.cpp has 133,181 GitHub stars, 25,063 forks, 2,464 open issues and last shipped yesterday. pegainfer has 731 stars, 121 forks, 89 open issues and last shipped yesterday. llama.cpp leads on adoption by 18,119% (133,181 vs 731 stars). llama.cpp is written in C++ under MIT; pegainfer is written in Rust under Apache-2.0. llama.cpp has attracted 19% as many forks as stars, pegainfer 17%. 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 pegainfer ★ 731 category AI & Machine Learning

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Side by side

	llama.cpp	pegainfer
GitHub stars	★ 133K	★ 731
License	MIT	Apache-2.0
Written in	C++	Rust
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 121
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 pegainfer if

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

full pegainfer 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 pegainfer

Quickstart · Performance · Models · Architecture · API · Development PegaInfer serves LLMs through an OpenAI compatible API. Each model owns its scheduler, state, and kernels; serving and KV infrastructure are shared. No PyTorch or Python runtime. Quickstart Prebuilt binary · Qwen3 on Linux The Qwen3 only release bundles CUDA 13 and cuBLAS. It requires Linux x86 64 , an NVIDIA GPU with compute capability 8.x–12.x , driver 580+ , glibc 2.35+ , and OpenSSL 3 . Model weights are downloaded separately. Download Qwen3 4B into models/Qwen3 4B , then start the server: The server…

read the full pegainfer overview →

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Frequently asked questions

Is llama.cpp or pegainfer more popular?

llama.cpp has 133,181 GitHub stars and pegainfer has 731. llama.cpp has the larger community by that measure.

Are llama.cpp and pegainfer free?

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

What is the difference between llama.cpp and pegainfer?

llama.cpp is written in C++ and pegainfer in Rust. 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 pegainfer?

Choose llama.cpp if you want the larger community (133,181 stars) or its MIT licence terms. Choose pegainfer if its feature set, stack or Apache-2.0 licence fits better. Both are self-hostable.