Bigger is not always better for machine learning. Yet, deep learning models and the datasets on which they’re trained keep expanding, as researchers race to outdo one another while chasing state-of-the-art benchmarks. However groundbreaking they are, the consequences of bigger models are severe for both budgets and the environment alike. For example, GPT-3, this summer’s massive, buzzworthy model for natural language processing, reportedly cost $12 million to train. What’s worse, UMass Amherst researchers found that the computing power required to train a large AI model can produce over 600,000 pounds of CO2 emissions – that’s five times the amount of the typical car over its lifespan.

At the pace the machine learning industry is moving today, there are no signs of these compute-intensive efforts slowing down. Research from OpenAI showed that between 2012 and 2018, computing power for deep learning models grew a shocking 300,000x, outpacing Moore’s Law. The problem lies not only in training these algorithms, but also running them in production, or the inference phase. For many teams, practical use of deep learning models remains out of reach, due to sheer cost and resource constraints. Read More