Most use cases that actually require this much data are probably best solved by more traditional ML architectures (ie classification). LLM work best on use cas

Most use cases that actually require this much data are probably best solved by more traditional ML architectures (ie classification). LLM work best on use cases where the working context is the length of a short research paper (or less). Building with LLM is mostly a exercise in application engineering on how to get them the most relevant context at the right time and how to narrow its scope to produce reliable outputs. Fine tuning can help specialize the LLM model to perform better, but AFAIK, the training sets are relatively small (in big data terms)