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head to head · open source

llama.cpp vs gpu-hot

llama.cpp has 128,581 GitHub stars, 23,334 forks, 2,464 open issues and last shipped yesterday. gpu-hot has 1,636 stars, 83 forks, 8 open issues and last shipped 24 days ago. llama.cpp leads on adoption by 7,759% (128,581 vs 1,636 stars). llama.cpp is written in C++ under MIT; gpu-hot is written in JavaScript under MIT. llama.cpp has attracted 18% 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 ★ 129K gpu-hot ★ 1.6K category AI & Machine Learning

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Side by side

llama.cpp gpu-hot
GitHub stars ★ 129K ★ 1.6K
License MIT MIT
Written in C++ JavaScript
Last push 2026-09-17 2026-08-25
Forks ⑂ 23K ⑂ 83
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 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 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 gpu-hot

gpu hot is a self hosted, real time NVIDIA GPU monitoring dashboard that ships as a Docker image and serves a live browser UI on port 1312 for a single machine or an entire cluster of GPU servers.

read the full gpu-hot overview →

More in AI & Machine Learning

OpenClaw ★ 390K Hermes Agent ★ 246K Ollama ★ 181K Dify ★ 156K Open WebUI ★ 152K langchain ★ 147K

Related comparisons

ollama vs llama-cpp llama-cpp vs vllm ollama vs vllm ollama vs gpt4all ollama vs llama-index ollama vs localai ollama vs pageindex ollama vs langfuse openclaw vs hermes-agent openclaw vs open-webui openclaw vs lobechat openclaw vs anythingllm openclaw vs cherry-studio openclaw vs nanobot openclaw vs jan openclaw vs librechat dify vs langchain dify vs ponytail langchain vs ponytail dify vs graphify langchain vs graphify ponytail vs graphify dify vs claude-mem dify vs ragflow

More Machine Learning Infrastructure projects

Compare either of these against the rest of the Machine Learning Infrastructure field.

llama.cpp vs Ollama llama.cpp vs vllm llama.cpp vs GPT4All llama.cpp vs llama_index llama.cpp vs LocalAI llama.cpp vs PageIndex llama.cpp vs Langfuse llama.cpp vs cognee llama.cpp vs taipy llama.cpp vs dagster llama.cpp vs zvec llama.cpp vs langchain4j

Frequently asked questions

Is llama.cpp or gpu-hot more popular?

llama.cpp has 128,581 GitHub stars and gpu-hot has 1,636. 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 (128,581 stars) or its MIT licence terms. Choose gpu-hot if its feature set, stack or MIT licence fits better. Both are self-hostable.