Its pretty trivial to calculate the embeddings if you have a reasonable GPU with CUDA support, even for decent volumes of data, the problem is that each differe

Its pretty trivial to calculate the embeddings if you have a reasonable GPU with CUDA support, even for decent volumes of data, the problem is that each different model would produce different embeddings and as soon as a new one is released all your previous datasets aren't that useful. Also worth mentioning that vector databases are really useful in Resource Augmented Generation - aka find an answer from an existing corpus and utilize it to supplement the LLM.