Which challenge does distributed training with model parallelization address?

Fine-tuning models when data scientists are unsure which pretrained model will work best for their use case

Avoiding drift by training multiple different models that check each other ' s results

Training very large models that cannot fit on a single GPU

Accelerating experimentation on servers with multi-core processors