head to head · open source

llama.cpp vs gpu-hot

llama.cpp has 133,101 GitHub stars, 25,042 forks, 2,464 open issues and last shipped yesterday. gpu-hot has 1,642 stars, 86 forks, 8 open issues and last shipped 2 months ago. llama.cpp leads on adoption by 8,006% (133,101 vs 1,642 stars). llama.cpp is written in C++ under MIT; gpu-hot is written in JavaScript under MIT. llama.cpp has attracted 19% as many forks as stars, gpu-hot 5%. 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 gpu-hot ★ 1.6K category AI & Machine Learning

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Side by side

	llama.cpp	gpu-hot
GitHub stars	★ 133K	★ 1.6K
License	MIT	MIT
Written in	C++	JavaScript
Last push	2026-11-02	2026-08-25
Forks	⑂ 25K	⑂ 86
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 gpu-hot if

You want the gpu-hot feature set and don't need the biggest community
You prefer the MIT license terms
Your stack matches JavaScript
You evaluated both and gpu-hot fits your workflow better

full gpu-hot 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 gpu-hot

GPU Hot Real time NVIDIA GPU monitoring dashboard. Lightweight, web based, and self hosted. Usage Monitor a single machine or an entire cluster with the same Docker image. Single machine: Multiple machines: Open Older GPUs: Add e NVIDIA SMI=true if metrics don't appear. Process monitoring: Add init pid=host to see process names. Note: This allows the container to access host process information. From source: Requirements: Docker + NVIDIA Container Toolkit Features Real time metrics (sub second) Automatic multi GPU detection Process monitoring (PID, memory usage) Historical charts (utilization, temperature…

read the full gpu-hot overview →

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Frequently asked questions

Is llama.cpp or gpu-hot more popular?

llama.cpp has 133,101 GitHub stars and gpu-hot has 1,642. llama.cpp has the larger community by that measure.

Are llama.cpp and gpu-hot free?

Both are open source. llama.cpp is licensed under MIT and gpu-hot under MIT. Neither carries a licence fee.

What is the difference between llama.cpp and gpu-hot?

llama.cpp is written in C++ and gpu-hot in JavaScript. 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 gpu-hot?

Choose llama.cpp if you want the larger community (133,101 stars) or its MIT licence terms. Choose gpu-hot if its feature set, stack or MIT licence fits better. Both are self-hostable.