What to do if the model does not fit on the GPU during fine-tuning?

If the model does not fit into GPU memory during fine-tuning, several approaches can be applied:

• Reduce the batch size — the simplest way to decrease memory consumption.
• Use gradient accumulation — split a large batch into several smaller ones, summing the gradients.
• Apply mixed precision training — use float16 instead of float32 to save memory.
• Model sharding or parallelism — split the model into parts and distribute across multiple GPUs.
• Model optimization — reduce the number of parameters, use more compact architectures.
• Use activation checkpointing techniques — save fewer intermediate data during the forward pass.

For example, reducing the batch size from 64 to 16 often helps fit the model into GPU memory without loss of training quality.