head to head · open source

llama.cpp vs FastDeploy

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. FastDeploy has 3,716 stars, 756 forks, 649 open issues and last shipped 23 days ago. llama.cpp leads on adoption by 3,360% (128,581 vs 3,716 stars). llama.cpp is written in C++ under MIT; FastDeploy is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, FastDeploy 20%. 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 FastDeploy ★ 3.7K category AI & Machine Learning

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Side by side

	llama.cpp	FastDeploy
GitHub stars	★ 129K	★ 3.7K
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-08-26
Forks	⑂ 23K	⑂ 756
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 FastDeploy if

You want the FastDeploy 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 FastDeploy fits your workflow better

full FastDeploy 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 FastDeploy

FastDeploy is a Python inference and deployment toolkit for large language models and vision language models in the PaddlePaddle ecosystem. It serves models such as ERNIE, ERNIE 4.5, ERNIE 4.5 VL, DeepSeek V3, Qwen3 MoE, Qwen3 VL, and PaddleOCR VL 0.9B on accelerators.

read the full FastDeploy overview →

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Frequently asked questions

Is llama.cpp or FastDeploy more popular?

llama.cpp has 128,581 GitHub stars and FastDeploy has 3,716. llama.cpp has the larger community by that measure.

Are llama.cpp and FastDeploy free?

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

What is the difference between llama.cpp and FastDeploy?

llama.cpp is written in C++ and FastDeploy 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 FastDeploy?

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