Debugging GPU code presents unique challenges due to the asynchronous nature of operations. Common issues include tensor size mismatches and out-of-memory errors. Monitoring memory usage, using gradient checkpointing, and enabling anomaly detection in PyTorch can aid in effective debugging. Tools like NVIDIA's Nsight Systems enhance performance analysis.