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ggml

> 编程语言
Open source

Tensor library for machine learning

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Tensor library for machine learning

ggml

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

GitHub Issues· 0 open

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Highlights

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

> Tags

C++automatic-differentiationlarge-language-modelsmachine-learningtensor-algebra

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> Details

PublishedAug 1, 2026
UpdatedSep 17, 2026
Category编程语言
PricingOpen source

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