gpu-memorypytorchvram-leaksdebugginggpu-issues
Debugging VRAM Leaks in PyTorch: A Practical Guide to Fixing GPU Memory Issues
8 min read · 1,521 wordsBy Orandi Felix
This post is not about OOM errors caused by batch sizes that are too large. Those are obvious and easy to fix. We’re talking about *leaks*—memory that doesn’t get freed *even after the operation completes*, causing VRAM to accumulate over time.
Use `torch.cuda.memory_summary(max_entries=50)` inside the loop to see what’s piling up.
The fix: always zero gradients *before* calling `backward()`.
If VRAM *max_alloc* grows monotonically across epochs, there’s a leak. If it jumps and plateaus, that’s normal working memory.