Computational Overhead

Computational overhead, the excessive resource consumption of algorithms and models, is a major bottleneck in deploying advanced AI systems, particularly large language models and diffusion transformers. Current research focuses on mitigating this overhead through techniques like caching intermediate results (e.g., in diffusion models), developing parameter-efficient fine-tuning methods, and designing novel optimization algorithms that reduce the number of computations needed for training and inference. Reducing computational overhead is crucial for enabling real-time applications on resource-constrained devices and making large-scale AI more accessible and sustainable.