ggml vs ONNX Runtime
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.
Open source · MIT
A small C/C++ library for building the math core of machine learning programs.
Open source · MIT
A cross-platform library for running and training machine learning models quickly.
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 ONNX Runtime if you need…
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
Side-by-side
| ggml | ONNX Runtime | |
|---|---|---|
| Type | Library | Library |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 15.6k | 22.2k |
| Setup difficulty | Advanced | Intermediate |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | C++ | C++ |
| Last code update | 9 days ago | 4 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.
ONNX Runtime
ONNX Runtime is a library that runs machine learning models on many kinds of hardware and operating systems. It supports models built in frameworks such as PyTorch and TensorFlow/Keras, as well as classical libraries such as scikit-learn, LightGBM and XGBoost. According to the README, it aims for faster performance and lower costs by using hardware accelerators and graph optimizations, and it also offers a training feature for transformer models on NVIDIA GPUs.
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
ONNX Runtime
- Developers who want to deploy a trained model inside an application or service
- Teams running the same model across Windows, Linux, macOS, or ARM devices
- Projects that need to speed up inference on available hardware accelerators
- Engineers with existing PyTorch training scripts who want to test the README's training acceleration
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
ONNX Runtime
- Supports many hardware and operating system targets, according to the README
- Works with models from several major frameworks through the ONNX format
- Open source under the MIT license with active development and published releases
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
ONNX Runtime
- People who only want to train or experiment with models in a notebook, since the README focuses on production inference and training acceleration rather than beginner workflows
- Readers without some programming background, because the README assumes familiarity with ML frameworks and model formats
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
ONNX Runtime
- The README is truncated and gives few concrete setup details, so getting started requires reading the linked documentation
- Performance benefits depend on hardware and model, and the README does not give specific numbers
- Training acceleration as described targets NVIDIA GPUs and transformer models
Getting started
ggml
Clone the repository
git clone https://github.com/ggml-org/ggmlEnter the directory and create a build folder
cd ggml && mkdir build && cd buildONNX Runtime
Clone the repository
git clone https://github.com/microsoft/onnxruntimeFrequently asked questions
What is the difference between ggml and ONNX Runtime?
ggml: A small C/C++ library for building the math core of machine learning programs. ONNX Runtime: A cross-platform library for running and training machine learning models quickly. ggml suits c/c++ developers who want to embed machine learning math into their own program, while ONNX Runtime suits developers who want to deploy a trained model inside an application or service.
Are ggml and ONNX Runtime free?
ggml: Free and open source. ONNX Runtime: Free and open source.
Which is easier to set up, ggml or ONNX Runtime?
ONNX Runtime is easier to get started with (rated intermediate); ggml is rated advanced and usually needs more setup.
Is ggml or ONNX Runtime more popular?
ONNX Runtime has more GitHub stars (22.2k) 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 ONNX Runtime?
Other options people compare them with include llama.cpp, Petals, Weights & Biases, Silero VAD, spaCy.
Keep comparing
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.