How does PyTorch handle graph-level memory deallocation?

Updated May 17, 2026

Short answer

Intermediate tensors are freed once they are no longer needed in backward graph traversal.

Autograd tracks dependencies and releases saved tensors once all downstream gradients are computed. This minimizes peak memory usage during training.

Unlock with a Pro subscription to view this section.

No real-world example available yet.

Unlock with a Pro subscription to view this section.

No common mistakes listed yet.

Unlock with a Pro subscription to view this section.

No follow-up questions available yet.

Unlock with a Pro subscription to view this section.