ggml

Tensor library for machine learning [](https://opensource.org/licenses/MIT) [](https://github.com/ggml-org/ggml/releases) [](https://github.com/ggml-org/ggml/actions/workflows/build-cpu.yml)

Quick start

Build from source:

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

mkdir build && cd build
cmake ..
cmake --build . --config Release -j 8

For a minimal, fully commented example (matrix multiplication), see examples/simple.

Description

The main goal of ggml is to be a simple, portable, and efficient tensor library for machine learning with minimal setup.

• Plain C/C++ implementation without any dependencies
• Cross-platform - x86, ARM, RISC-V, LoongArch, PowerPC, s390x, and WebAssembly
• SIMD-optimized kernels for x86, ARM, and RISC-V
• Broad backend support - CPU, GPU, NPU, and browser
• 2- to 8-bit integer quantization, plus MXFP4 and NVFP4 microscaling formats
• Zero memory allocations during runtime

Documentation

• The GGUF file format
• Introduction to ggml
• GGML tips & tricks

Contributing

• For changes to the core ggml library (including to the CMake build system), please open a PR in llama.cpp - doing so will make your PR more visible, better tested, and more likely to be reviewed