GGML is a machine learning tensor library and neural network engine written in C. It functions as a compute-focused runtime designed to execute transformer-based models and perform complex mathematical operations on multi-dimensional arrays directly on local consumer hardware.
The library distinguishes itself by enabling local inference for large language models and edge machine learning deployment without reliance on external cloud infrastructure. It achieves this through a tensor-based computation graph that organizes operations for efficient execution and memory management, alongside static memory allocation to minimize runtime overhead.
The engine supports high-performance tensor computing by utilizing hardware-agnostic kernel dispatch and processor-specific instruction sets for parallel arithmetic. It further optimizes resource usage through quantized weight representations, which reduce the memory footprint of models to facilitate execution on local devices.
