Training compute refers to the aggregate computational power employed during the process of training an AI model. It plays a crucial role in influencing the speed and quality of the training phase in both neural networks and other ML models. With the increase in model complexity and dataset size, the demand for training compute has escalated, necessitating the use of specialized hardware such as GPUs and TPUs to facilitate efficient model training. The relationship between model performance and the amount of utilized training compute is well-documented; larger training compute budgets typically enable training of more sophisticated models capable of achieving higher accuracy on complex tasks. Additionally, training compute is a critical consideration in the research on AI scaling laws, where it helps in delineating the relationship between computational investment and resulting performance improvements.
The term "training compute" grew in prominence as a distinct concept in the early 2010s, paralleling the rise of large-scale deep learning implementations. It became more widely recognized around 2017 as advancements in neural network architectures, like Transformers, necessitated an exponential increase in the required computational overhead for training.
Key figures in the evolution of training compute include researchers and engineers within companies at the forefront of AI development, such as Google Brain, OpenAI, and DeepMind. Their work in developing and optimizing algorithms and hardware for large-scale neural network training has significantly contributed to effectively leveraging training compute to push the boundaries of AI capabilities.