bitsandbytes

bitsandbytes is an open-source library designed to make training and inference of large neural networks more efficient by dramatically reducing memory usage. Built primarily for the PyTorch ecosystem, the library introduces advanced quantization techniques that allow models to operate using reduced numerical precision while maintaining high accuracy. These optimizations enable large language models and other deep learning architectures to run on hardware with limited memory resources, including consumer-grade GPUs. The project includes specialized optimizers and quantized matrix operations that significantly reduce the memory footprint of training and inference workloads. By lowering the hardware requirements needed to work with large models, bitsandbytes helps make modern AI development more accessible to researchers and engineers. The library has become widely used in machine learning pipelines that rely on parameter-efficient training techniques and low-precision inference.