How To Optimize Memory Usage For Training LLMs In PyTorch

Deep learning models often face memory bottlenecks during training. Several techniques can significantly reduce GPU memory consumption without sacrificing performance, including automatic mixed-precision training, gradient accumulation, and gradient checkpointing. These strategies can be combined to achieve up to 20x memory savings, making large model training more accessible on limited hardware.