# Gradient Checkpointing and Activation Offloading – Axolotl

# Gradient Checkpointing and Activation Offloading

Gradient checkpointing and activation offloading are techniques used to optimize the performance of deep learning models by reducing the memory footprint and improving computational efficiency.

### Enabling Gradient Checkpointing

    gradient_checkpointing: true

### Enabling Activation Offloading

    gradient_checkpointing: true  # required for activation offloading
    activation_offloading: true

Activation offloading variants:

The default `activation_offloading: true` offloads activations to CPU and uses CUDA streams to overlap the communications and computations when offloading.

The `activation_offloading: legacy` naively offloads activations to CPU and without additional optimizations.

For resource constrained environments with limited CPU memory, `activation_offloading: disk` offloads activations to disk instead of CPU RAM so that much larger context lengths can be trained with minimal memory.