Efficient LLM

Efficient Large Language Model (LLM) research focuses on reducing the computational cost and memory footprint of LLMs while maintaining or improving performance. Current efforts concentrate on optimizing model architectures (e.g., exploring Mixture of Experts and novel attention mechanisms), improving inference serving systems through dynamic scheduling and efficient memory management (like virtual tensor management), and developing more efficient training and fine-tuning strategies. These advancements are crucial for broadening LLM accessibility, enabling deployment on resource-constrained devices, and reducing the environmental impact of large-scale AI.