Inference Speed

Inference speed, the time taken for a machine learning model to process input and produce output, is a critical factor limiting the deployment of powerful models in resource-constrained environments and real-time applications. Current research focuses on optimizing various model architectures, including transformers and diffusion models, through techniques like knowledge distillation, model pruning, parallel decoding, and early exiting, aiming to significantly reduce latency without sacrificing accuracy. These advancements are crucial for expanding the practical applications of large language models, computer vision systems, and other computationally intensive AI systems across diverse platforms, from smartphones to embedded devices.