Chinchilla Scaling Laws: How Compute-Optimal Pre-Training and Inference Economics Reshaped LLM Architecture
Before 2022, frontier artificial intelligence labs scaled large language models primarily by increasing parameter counts while keeping training dataset sizes relatively static. OpenAI's 2020 GPT-3 model trained 175 billion parameters on 300 billion tokens. DeepMind's 2021 Gopher trained 280 billion parameters on 300 billion tokens, and Megatron-Turing NLG scaled to 530 billion parameters on 270 billion tokens. These allocation decisions followed early empirical scaling laws that suggested parame
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