Pruning

The process of reducing the precision of a model's weights and activations to lower memory footprint and speed up inference.

A model optimization technique in which a smaller AI model, called the student model, is trained to mimic the behavior or outputs of a larger, more capable teacher model, reducing model size and inference cost while preserving much of the original performance.

The process of improving an AI model's performance, efficiency, or resource usage through techniques such as fine-tuning, pruning, quantization, or distillation.

The process of improving the speed, latency, throughput, memory usage, or cost-efficiency of AI model inference through techniques such as quantization, pruning, batching, caching, hardware acceleration, and optimized model serving.

The process of further training a pretrained AI model on a task-specific or domain-specific dataset to improve its performance, behavior, or specialization for particular use cases.