Yes, compute-optimal for training only. The purpose of the paper wasn’t to determine the most economically practical model one could build, the most “intelligent” model one could build given some amount of compute.
Meanwhile, people noticed that for deployed models, inference cost often outweighs the initial training costs. It's sometimes better to train a smaller, faster model longer on more data, because it has lower overall cost (including environmental impact) if you're expecting to run the model a few million or billion times (e.g., [1]). So training past the Chinchilla optimum point became a lot more common, particularly after Llama.