Skip to content
Get packs

ggml vs llama.cpp

Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.

ggml

Open source · MIT

A small C/C++ library for building the math core of machine learning programs.

Full review Website
llama.cpp

Open source · MIT

Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs.

Full review Website

The short answer

Choose ggml if you need…

  • C/C++ developers who want to embed machine learning math into their own program
  • Engineers running models on modest or unusual hardware, such as ARM, RISC-V, or WebAssembly targets
  • People who need low-level control over memory and quantized (smaller, lower-precision) number formats
  • Developers exploring how tensor libraries work under the hood

Choose llama.cpp if you need…

  • Developers who want to run open-weight models locally without a cloud API
  • People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
  • Teams that want a self-hosted OpenAI-compatible endpoint
  • Researchers experimenting with quantized models on limited memory

Side-by-side

ggmlllama.cpp
TypeLibraryLibrary
PriceFree and open sourceFree and open source
LicenceMITMIT
GitHub stars15.6k133k
Setup difficultyAdvancedIntermediate
How you get itClone and build from sourceSee the README
Main languageC++C++
Last code update9 days ago4 days ago

What each one is

ggml

ggml is a tensor library: it provides the low-level building blocks for doing the number crunching behind machine learning models, written in plain C and C++ with no outside dependencies. It is designed to be simple, portable, and efficient, and it runs on many CPU types and some GPUs, NPUs, and browsers. It is mainly a foundation for other software rather than a finished end-user tool.

llama.cpp

llama.cpp is an open-source project for running large language models (LLMs), and vision-language models, on your own hardware. It is written in C/C++ with no required dependencies and supports many chip types, including Apple silicon, x86 and RISC-V CPUs, and NVIDIA, AMD and other GPUs. People use it to run models locally or on their own servers, with a command-line tool, a built-in web UI, and an OpenAI-compatible API server.

Best for

ggml

  • C/C++ developers who want to embed machine learning math into their own program
  • Engineers running models on modest or unusual hardware, such as ARM, RISC-V, or WebAssembly targets
  • People who need low-level control over memory and quantized (smaller, lower-precision) number formats
  • Developers exploring how tensor libraries work under the hood

llama.cpp

  • Developers who want to run open-weight models locally without a cloud API
  • People with Apple silicon or other non-NVIDIA hardware who want GPU-accelerated inference
  • Teams that want a self-hosted OpenAI-compatible endpoint
  • Researchers experimenting with quantized models on limited memory

Strengths

ggml

  • No external dependencies, in plain C/C++, which keeps setup and deployment simple
  • Broad platform support across CPU architectures plus GPU, NPU, and browser backends
  • Designed for efficiency, with SIMD-optimized kernels and no memory allocations at runtime

llama.cpp

  • Plain C/C++ with no required dependencies, and a wide range of supported hardware backends
  • Quantization options from 1.5-bit to 8-bit reduce memory needs
  • Large, active open-source project under the MIT license

Not ideal for

ggml

  • Beginners who want to run a chatbot or model without writing code: the README describes a library, not an application
  • Python-first data scientists: the README shows only C/C++ build steps and no Python interface
  • Teams needing a stable high-level API: the README does not describe API guarantees, and core changes are routed through another project

llama.cpp

  • Users who want a polished consumer chat app with no setup; the README points to command-line and build steps
  • People who need a managed cloud service, since this is software you run yourself
  • Users with very limited RAM or VRAM, since performance depends heavily on hardware and model size

Trade-offs

ggml

  • The README is short and gives little API documentation, so using it directly requires reading the source and examples
  • Core library changes are directed to the llama.cpp project, so this repo is not the place for most contributions
  • Latest release lists no prebuilt binaries, so you must build from source

llama.cpp

  • Setup can require building from source or choosing the right backend for your hardware
  • Performance and supported features vary by backend and device, and some backends are marked in progress
  • Results depend on local hardware, so the README's performance claims are hard to verify for your machine

Getting started

ggml

Clone the repository

git clone https://github.com/ggml-org/ggml

Enter the directory and create a build folder

cd ggml && mkdir build && cd build
Step-by-step guide: how to use ggml

llama.cpp

Install with curl (macOS/Linux installer from README)

curl -LsSf https://llama.app/install.sh | sh

Install with PowerShell (Windows installer from README)

irm https://llama.app/install.ps1 | iex
Step-by-step guide: how to use llama.cpp

Frequently asked questions

What is the difference between ggml and llama.cpp?

ggml: A small C/C++ library for building the math core of machine learning programs. llama.cpp: Runs large language models locally in plain C/C++, on CPUs and many kinds of GPUs. ggml suits c/c++ developers who want to embed machine learning math into their own program, while llama.cpp suits developers who want to run open-weight models locally without a cloud api.

Are ggml and llama.cpp free?

ggml: Free and open source. llama.cpp: Free and open source.

Which is easier to set up, ggml or llama.cpp?

llama.cpp is easier to get started with (rated intermediate); ggml is rated advanced and usually needs more setup.

Is ggml or llama.cpp more popular?

llama.cpp has more GitHub stars (133k) than ggml (15.6k). Popularity isn't the same as fit — check which one matches your use case above.

What are other alternatives to ggml and llama.cpp?

Other options people compare them with include ONNX Runtime, Ollama.

Keep comparing

ggml vs ONNX Runtimellama.cpp vs OllamaGPT4All vs llama.cppllama.cpp vs LocalAIbitnet.cpp vs llama.cppllama.cpp vs Stratallama.cpp vs llm.cDwarfStar vs llama.cppllama.cpp vs llama2.cllama.cpp vs Nano-vLLM

Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.