How do transformer-based models handle memory constraints during training?

They use gradient checkpointing, mixed precision, and distributed sharding to manage memory.

Training transformers requires storing activations for backpropagation, which scales with depth and sequence length. Techniques like ZeRO optimization shard parameters, activation checkpointing recomputes intermediates, and FP16/BF16 reduce memory footprint.