Large model training FP8-LLM Don’t let your H card be bought in vain: the correct way to open H800

Content introduction

This article discusses NVIDIA’s H100 GPU and its support for FP8 data types, a major advancement in large language model (LLM) training. The H100’s high price has been compared to the cost of gold, emphasizing its high-end status. A key highlight is the launch of FP8-LLM, an enhancement to NVIDIA’s TransformerEngine (TE) that enables FP8 acceleration in DNN training and inference. This development is noteworthy because it promises to reduce memory requirements and communication costs, potentially revolutionizing the efficiency of large-scale language model training. The guide also touches on the industry’s shift to mixed-precision training, and how FP8-LLM’s autoscaling strategy and optimized optimizer states may result in performance comparable to BF16. For those interested in the cutting-edge of AI hardware and the future of model training, this content provides valuable insights into FP8’s potential impact on the industry.

Automatic summary

– FP8-LLM is a method to use FP8 accuracy in large language model training.
– FP8-LLM uses FP8 format to store gradients and uses FP8 format during communication to reduce memory requirements and communication costs.
– The optimizer of FP8-LLM uses FP8 to store momentum and FP16 to store variance and master weight, thereby reducing video memory requirements.
– FP8-LLM adapts the parallel strategy and uses FP8 format for tensor parallel calculation and communication to reduce communication volume.
– FP8-LLM has comparable performance compared to BF16 on pre-training and downstream evaluation tasks.
– The advent of FP8-LLM marks NVIDIA’s progress in FP8 support, but more experiments and attempts are still needed to verify its stability and effectiveness.

Original link: https://zhuanlan.zhihu.com/p/664972481