RuntimeError
torch.cuda.OutOfMemoryError
Stack trace
Traceback (most recent call last):
File "app.py", line 42, in <module>
outputs = model(input_tensor)
File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "/usr/local/lib/python3.9/site-packages/torch/nn/modules/module.py", line 1190, in forward
...
torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 512.00 MiB (GPU 0; 8.00 GiB total capacity; 6.50 GiB already allocated; 256.00 MiB free; 6.70 GiB reserved in total by PyTorch) Why it happens
This error happens because the GPU memory is insufficient to hold the model parameters, intermediate tensors, or batch data during training or inference. RunPod instances have fixed GPU memory limits, and large models or batch sizes can exceed this capacity, causing CUDA to throw an out of memory error.
Detection
Monitor GPU memory usage with tools like nvidia-smi or RunPod dashboard metrics before running your workload to detect memory saturation and prevent OOM crashes.
Causes & fixes
Batch size is too large for the available GPU memory
Reduce the batch size in your training or inference code to fit within the GPU memory limits.
Model size or architecture requires more memory than the RunPod GPU instance provides
Switch to a RunPod instance with a larger GPU memory capacity or optimize the model to be smaller.
Memory fragmentation or leftover tensors not freed during iterative runs
Explicitly call torch.cuda.empty_cache() between iterations and delete unused tensors to free memory.
Loading multiple large models or data on the same GPU simultaneously
Load only one model at a time or distribute models across multiple GPUs if available.
Code: broken vs fixed
import torch
model = MyLargeModel().cuda()
input_tensor = torch.randn(64, 3, 224, 224).cuda() # Large batch size
outputs = model(input_tensor) # This line triggers OOM errorimport os
import torch
os.environ['RUNPOD_API_KEY'] = os.environ.get('RUNPOD_API_KEY') # Use env var for API key
model = MyLargeModel().cuda()
input_tensor = torch.randn(16, 3, 224, 224).cuda() # Reduced batch size to fix OOM
outputs = model(input_tensor) # Runs without OOM
print('Inference completed successfully')Workaround
Catch the RuntimeError for CUDA OOM, clear cache with torch.cuda.empty_cache(), reduce batch size dynamically, and retry the operation to avoid crashes.
Prevention
Design your workloads to monitor GPU memory usage and use adaptive batch sizing or model parallelism. Prefer RunPod instances with sufficient GPU memory for your model size and workload.