How high is the demand for computing power from large models?
The demand for computing power from large models is exceptionally high, often reaching hundreds of petaflops for extended periods during training. This immense requirement stems from the computational intensity of training models with billions or trillions of parameters on massive datasets.
Key factors driving this demand include the sheer size of model parameters, the volume of training data, the complexity of neural network architectures like transformers, and the extensive number of training cycles required for convergence. Training large models involves running highly parallelized calculations continuously for weeks or months. Significant infrastructure investment in specialized hardware like AI accelerators and high-bandwidth networks is essential to support this scale. Costs are substantial.
This high computing power demand represents a major barrier to entry and a significant cost factor in developing and deploying cutting-edge large language models and foundation models. It necessitates massive investments in specialized AI infrastructure, drives innovation in chip design, and fundamentally impacts the economics and accessibility of advanced AI research and application development.
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