Fast Inference

Fast inference in machine learning aims to accelerate the process of obtaining predictions from complex models, addressing the computational bottleneck hindering the deployment of powerful models like large language models and vision transformers. Current research focuses on techniques such as speculative decoding, model compression (including pruning and quantization), and architectural innovations like mixture-of-experts and hierarchical attention mechanisms to achieve speedups. These advancements are crucial for deploying sophisticated AI models in resource-constrained environments and real-time applications, impacting fields ranging from natural language processing and computer vision to astrophysics and robotics.