This happens because increasing GPU memory usually leads people to increase batch size, and large batches change how neural networks learn. While each step processes more data, the model receives fewer gradient updates per epoch, which can slow down learning even if raw computation is faster.

Large batches tend to smooth out gradient noise, which reduces the regularizing effect that smaller batches naturally provide. This often causes the optimizer to take more conservative steps, requiring more epochs to reach the same level of performance. As a result, even though each batch runs faster, the model may need more total training time to converge.

To compensate, you usually need to scale the learning rate upward or use gradient accumulation strategies. Without these adjustments, more GPU memory simply changes the training dynamics instead of making the model better or faster.

Common mistakes:

• Increasing batch size without adjusting learning rate

• Assuming more VRAM always improves training

• Ignoring convergence behavior

The practical takeaway is that GPU memory changes how learning happens, not just how much data you can fit.