Harnessing the Power of Sparsity for Large GPT AI Models

This post details how unstructured sparsity can be leveraged for both faster training and faster inference of large GPT models. For training, it demonstrates pre-training with high sparsity at a fraction of the training FLOPs while preserving downstream accuracy using dense fine-tuning. For inference, it shows iterative pruning can create extremely sparse models with significantly fewer FLOPs and memory footprint without loss of accuracy. The Cerebras CS-2's architecture, with its on-chip memory bandwidth and dataflow execution, is highlighted as a key enabler for accelerating unstructured sparsity.