Quantization is a technique used in machine learning to reduce the number of bits required to represent data, which in turn compresses models and enhances their efficiency. By performing computations and storing tensors at lower bitwidths than floating point precision, quantization allows for a more compact model representation. For instance, PyTorch supports INT8 quantization, which can lead to a 4x reduction in model size and memory bandwidth requirements compared to typical FP32 models ¹.

There are different types of quantization, including symmetric and asymmetric quantization. Symmetric quantization uses the same scale for both positive and negative values, while asymmetric quantization allows for different scales. Additionally, quantization can be performed in various ways, such as dynamic quantization, static quantization, and quantization-aware training, each with its own trade-offs and use cases ³⁴.

Overall, quantization is crucial for optimizing models, especially for deployment on resource-constrained devices, as it improves performance without significantly sacrificing accuracy.