Why does the model during training occupy more memory than during inference?

During the training phase, the model consumes more memory for several reasons:

1. Storing gradients: During training, it is necessary to save gradients for each parameter to update weights using the backpropagation algorithm. During inference, gradients are not needed.

2. Intermediate activations: To compute gradients, it is required to store the intermediate activation values of all layers, which increases memory consumption.

3. Optimizers: Some optimizers (e.g., Adam) store additional parameters such as first and second moments, which increases the memory footprint.

4. Batch data: Large batches of data are often used during training, which also requires additional memory.

In the end, during inference, the model simply performs a forward pass without storing gradients and intermediate values for backpropagation, significantly reducing memory usage.