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Introduction Over the past five years, the performance of large language models (LLMs) has improved dramatically, driven largely by rapid scaling in training compute budgets to handle larger models and training datasets. Our own estimates suggest that the training compute used by frontier AI models has grown by 4-5 times every year from 2010 to 2024.
Introduction Scaling has been a key factor driving progress in AI. Models are growing in parameters and being trained on increasingly enormous datasets, leading to exponential growth in training compute, and dramatic increases in performance. For example, five years and four orders of magnitude of compute separate the barely coherent GPT-2 with the powerful GPT-4.
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