Your question is Memory-Efficient Training for Large Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
What methods would you use to save memory footprint when training large models?
Discuss the methods you would prioritize in practice, how they affect training speed and model quality, and how you would choose among them under infrastructure and time constraints. Quantify the main sources of memory usage where possible, and explain how you would validate that the chosen approach works.