Example Concept: Model pruning involves removing redundant or less important parameters from a neural network to reduce its size and computational load. Quantization converts model weights and activations from floating-point to lower precision formats, like int8, to decrease memory usage and increase inference speed. Knowledge distillation transfers knowledge from a large, complex model (teacher) to a smaller, simpler model (student), retaining performance while reducing resource demands.