Compress the Activation, Spend the Memory on Rank
TL;DR for operators A LoRA fine-tuning job can fit its trainable parameters comfortably on a GPU and still run out of memory because backpropagation retains large intermediate activations. CARE-LoRA attacks that remaining buffer rather than shrinking the adapter itself. Zhang et al. store LoRA’s already-compressed activation plus a small reconstruction matrix, then use those tensors to approximate only the gradient needed for one of LoRA’s two trainable matrices.1 ...