Inference Latency

Inference latency, the time taken for a model to produce an output, is a critical bottleneck in deploying large language models (LLMs) and other deep learning models, particularly for real-time applications. Current research focuses on optimizing inference speed through techniques like speculative decoding (using faster "draft" models to predict outputs before verification), early exiting (stopping computation early if confidence is high), and model compression methods such as pruning, quantization, and knowledge distillation. Reducing inference latency is crucial for expanding the practical applications of these powerful models, enabling their use in resource-constrained environments and improving user experience in interactive systems.