Fix cudaErrorOutOfMemory: GPU Memory Exhausted Solutions

Overview

cudaErrorOutOfMemory occurs when GPU memory is completely exhausted. This comprehensive guide covers memory profiling, optimization strategies, and prevention techniques.

Error Messages

CUDA error: out of memory
cudaErrorOutOfMemory: out of memory

Common Causes

• •Model too large for GPU VRAM
• •Batch size too large
• •Memory leaks in training loop
• •Multiple processes sharing GPU

Solutions

nvidia-smi -l 1
# Or in Python:
import torch
print(torch.cuda.memory_summary())

# Gradient accumulation
for i, batch in enumerate(loader):
    loss = model(batch) / accumulation_steps
    loss.backward()
    if (i + 1) % accumulation_steps == 0:
        optimizer.step()
        optimizer.zero_grad()

from torch.cuda.amp import autocast, GradScaler
scaler = GradScaler()
with autocast():
    output = model(input)

torch.cuda.empty_cache()
import gc
gc.collect()

Prevention Tips

• ✓Monitor GPU memory during development
• ✓Use gradient checkpointing for large models
• ✓Implement memory-efficient data loading

Code Examples

Before (Problematic)

Loading entire model and large batch causes OOM.

model = LargeModel().cuda()
output = model(huge_batch)  # OOM!

After (Fixed)

Gradient checkpointing and mini-batches reduce memory.

model = LargeModel().cuda()
model.gradient_checkpointing_enable()
for mini_batch in split(batch, 4):
    output = model(mini_batch)

Frequently Asked Questions

Related Errors

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