This is a significant concern as large models require substantial energy consumption and resources, contributing to pollution and electronic waste. The benefits of machine learning are often overshadowed by the environmental impact of its growing size. However, researchers are exploring various techniques to mitigate this issue, such as model distillation, quantization, and self-supervised learning. These approaches aim to reduce the computational requirements and data usage associated with large models. Additionally, fine-tuning pre-trained models can help smaller organizations access powerful ML capabilities without the need for massive resources. By focusing on efficiency and developing new methods that are less data, hardware, or parameter hungry, researchers can make a significant impact in this field. Ultimately, machine learning must consider its environmental footprint and strive to create models that benefit humanity while minimizing harm.