It depends on the time the job itself takes. If you're having the LLM handle a training run for another model, the LLM is probably spending most of its time waiting for iterations rather than consuming tokens.

For a task I left a local model running on overnight, only ~100k tokens were used because most of the time was just waiting on tests to finish, then waking up, tweaking a few settings and trying again.