Increase the virtual machine's GPU memory by upgrading to a higher-tier GPU, ensuring the model can handle larger batch sizes without memory issues.

Decrease the batch size to reduce memory consumption during training, accepting a potential increase in training time.

Modify the optimizer settings to 'adam' from 'sgd' to better manage resources, as 'adam' is known for its memory efficiency.

Resize the input images to smaller dimensions to lessen memory load, which may slightly reduce the model's accuracy.

Implement gradient checkpointing to trade off computation time for memory usage, allowing for larger effective batch sizes.