What method combines quantization, pruning, and knowledge distillation for maximum inference optimization?

Holistic Model Compression is a method that integrates quantization, pruning, and knowledge distillation to enhance inference optimization. Each of these techniques contributes to reducing the size and complexity of machine learning models while maintaining their performance.

Quantization reduces the precision of the model's numerical representation, which can decrease the model size and accelerate computation without a significant loss in accuracy. Pruning removes unnecessary weights or neurons from the model, resulting in a more efficient architecture. Knowledge distillation involves training a smaller model (the student) to replicate the behavior of a larger, more complex model (the teacher), thereby transferring the learned knowledge.

By combining these methods, Holistic Model Compression enables substantial reductions in model size and inference time, making it particularly effective for deployment in resource-constrained environments. This synergy ensures that the resulting model retains a high level of performance while being optimized for faster inference and lower resource consumption.