head to head · open source

llama.cpp vs Beam

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. Beam has 1,780 stars, 165 forks, 21 open issues and last shipped 2 days ago. llama.cpp leads on adoption by 7,124% (128,581 vs 1,780 stars). llama.cpp is written in C++ under MIT; Beam is written in Go under AGPL-3.0. llama.cpp has attracted 18% as many forks as stars, Beam 9%. 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 Beam ★ 1.8K category AI & Machine Learning

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Side by side

	llama.cpp	Beam
GitHub stars	★ 129K	★ 1.8K
License	MIT	AGPL-3.0
Written in	C++	Go
Last push	2026-09-17	2026-09-16
Forks	⑂ 23K	⑂ 165
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 Beam if

You want the Beam feature set and don't need the biggest community
You prefer the AGPL-3.0 license terms
Your stack matches Go
You evaluated both and Beam fits your workflow better

full Beam 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 Beam

Beam is an open source runtime for serverless AI workloads, released under AGPL 3.0 and implemented in Go. It lives in the Python AI infrastructure ecosystem, where it gives developers a Pythonic interface for deploying and scaling GPU inference, sandboxes, and background jobs without managing infrastructure. The open source engine is Beta9, which powers the managed Beam cloud platform.

read the full Beam overview →

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Frequently asked questions

Is llama.cpp or Beam more popular?

llama.cpp has 128,581 GitHub stars and Beam has 1,780. llama.cpp has the larger community by that measure.

Are llama.cpp and Beam free?

Both are open source. llama.cpp is licensed under MIT and Beam under AGPL-3.0. Neither carries a licence fee.

What is the difference between llama.cpp and Beam?

llama.cpp is written in C++ and Beam in Go. 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 Beam?

Choose llama.cpp if you want the larger community (128,581 stars) or its MIT licence terms. Choose Beam if its feature set, stack or AGPL-3.0 licence fits better. Both are self-hostable.