AI Language Models Take Up too Much Computing Power, Researchers Say
Main Ideas:
- With the growing popularity of generative AI applications, there is a need to reduce the perceived latency and increase throughput.
- Foundation models (FMs) and large language models (LLMs) are pre-trained on massive amounts of data, which leads to high computing power requirements.
- Researchers argue that the power consumption and environmental impact of training and running these models at scale are substantial.
- Efforts are being made to improve efficiency and reduce the computing power usage of AI language models.
- One proposed solution is the use of smaller models that maintain good performance while reducing energy consumption.
Author’s Take:
The rapid growth of generative AI applications has brought attention to the significant computing power required to train and run these models at scale. Researchers are highlighting the power consumption and environmental impact of these large language models. While efforts are underway to improve efficiency, the focus on developing smaller models that maintain good performance is an important step towards reducing energy consumption in AI applications.