ggml is an open-source tensor library designed for efficient machine learning computation with a focus on running models locally and with minimal dependencies. Written primarily in C and C++, the library provides low-level tensor operations and automatic differentiation that allow developers to implement machine learning algorithms and neural networks efficiently. The project emphasizes portability and performance, enabling machine learning inference across a wide range of hardware environments including CPUs and specialized accelerators. It is widely used as a foundational component in projects that run large language models locally, including tools that perform inference for transformer-based models. The library also implements optimization algorithms and computation graph functionality so developers can build training and inference workflows directly on top of its tensor operations.

Features

  • Low-level tensor computation library for machine learning
  • Automatic differentiation for building computation graphs
  • Integer quantization support for efficient model inference
  • Cross-platform compatibility across different hardware environments
  • Implementation of optimization algorithms such as ADAM and L-BFGS
  • Minimal dependency design for lightweight AI deployments

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