Meta AI’s READ Method for Fine-Tuning Large Transformers Cuts GPU Energy Costs by 84%

In the new paper READ: Recurrent Adaptation of Large Transformers, a Meta AI research team proposes REcurrent ADaption (READ), a lightweight and memory-efficient fine-tuning approach that achieves a 56 percent reduction in memory consumption and an 84 percent reduction in GPU use.

ai ai research artificial intelligence costs deep-neural-networks energy fine-tuning gpu large language model machine learning machine learning & data science memory meta meta ai meta ai research ml nature language tech paper research research team team technology transformers