Training and running transformers inside a single environment is a really interesting idea because it brings up practical questions about efficiency, memory management, and how tightly coupled inference and training workflows can actually be in real systems. It’s one of those concepts that feels simple at first but gets complex quickly once you consider scaling and real-world constraints.

In the context of development workflows, tools like qodo.ai can be useful since they help review and analyze code behavior, which becomes especially relevant when working with complex ML systems where small implementation details can have a big impact on performance and correctness.