head to head · open source

llama.cpp vs ServerlessLLM

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. ServerlessLLM has 715 stars, 76 forks, 45 open issues and last shipped 14 days ago. llama.cpp leads on adoption by 17,883% (128,581 vs 715 stars). llama.cpp is written in C++ under MIT; ServerlessLLM is written in Python under Apache-2.0. llama.cpp has attracted 18% as many forks as stars, ServerlessLLM 11%. 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 ServerlessLLM ★ 715 category AI & Machine Learning

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Side by side

	llama.cpp	ServerlessLLM
GitHub stars	★ 129K	★ 715
License	MIT	Apache-2.0
Written in	C++	Python
Last push	2026-09-17	2026-09-04
Forks	⑂ 23K	⑂ 76
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 ServerlessLLM if

You want the ServerlessLLM 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 ServerlessLLM fits your workflow better

full ServerlessLLM 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 ServerlessLLM

ServerlessLLM is an Apache 2.0, Python based system for running many large language models on shared GPUs, aimed at teams that want serverless style multi model serving without provisioning a separate GPU for every model.

read the full ServerlessLLM overview →

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Frequently asked questions

Is llama.cpp or ServerlessLLM more popular?

llama.cpp has 128,581 GitHub stars and ServerlessLLM has 715. llama.cpp has the larger community by that measure.

Are llama.cpp and ServerlessLLM free?

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

What is the difference between llama.cpp and ServerlessLLM?

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

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