Inference Efficiency

Inference efficiency in large language models (LLMs) and other deep learning architectures focuses on minimizing computational cost and latency while maintaining accuracy. Current research emphasizes techniques like model transformation and distillation, early exiting strategies, and selective context compression, often applied within architectures such as Transformers and Mixture-of-Experts models. These advancements are crucial for deploying large models on resource-constrained devices and for improving the scalability and cost-effectiveness of AI applications across various domains, including question answering, document understanding, and real-time processing. Improved inference efficiency directly translates to reduced energy consumption and faster response times, making AI more accessible and practical.