head to head · open source

llama.cpp vs Beam

llama.cpp has 133,101 GitHub stars, 25,042 forks, 2,464 open issues and last shipped yesterday. Beam has 1,841 stars, 175 forks, 21 open issues and last shipped yesterday. llama.cpp leads on adoption by 7,130% (133,101 vs 1,841 stars). llama.cpp is written in C++ under MIT; Beam is written in Go under AGPL-3.0. llama.cpp has attracted 19% as many forks as stars, Beam 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 ★ 133K Beam ★ 1.8K category AI & Machine Learning

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Side by side

	llama.cpp	Beam
GitHub stars	★ 133K	★ 1.8K
License	MIT	AGPL-3.0
Written in	C++	Go
Last push	2026-11-02	2026-11-02
Forks	⑂ 25K	⑂ 175
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 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 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 Beam

Run AI Workloads at Scale Beam is a fast, open source runtime for serverless AI workloads. It gives you a Pythonic interface to deploy and scale AI applications with zero infrastructure overhead. ✨ Features Fast Cold Starts : Launch containers in under a second using a custom container runtime, scheduler, and embedded caching Parallelization and Concurrency : Fan out workloads to 100s of containers First Class Developer Experience : Hot reloading, webhooks, and scheduled jobs Scale to Zero : Workloads are serverless by default Volume Storage : Mount distributed storage volumes GPU Support : Run on our cloud…

read the full Beam overview →

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Frequently asked questions

Is llama.cpp or Beam more popular?

llama.cpp has 133,101 GitHub stars and Beam has 1,841. 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 (133,101 stars) or its MIT licence terms. Choose Beam if its feature set, stack or AGPL-3.0 licence fits better. Both are self-hostable.