Inference optimization

Topic

Inference optimization refers to the process of improving the speed, throughput, and resource efficiency of a trained machine learning model during its deployment phase. By utilizing techniques such as quantization, pruning, knowledge distillation, and hardware acceleration, it aims to reduce computational costs and latency when generating predictions. This practice is critical for scaling deep learning models and large language models (LLMs) in production environments.