Empowered, Inspired and Enabled by AI
Based on its highly responsive time-sharing task scheduler, Fire-Flyer II gives each researcher a smooth training experience. With the help of powerful software layer, the users can scale their models up to fully utilize all the GPUs at their fingertip with the help of a collection of highly optimized DL primitives for model acceleration (hfai.nn), a communication framework for distributed training (hfreduce), and a large-capacity high-throughput parallel file system for reading samples (3fs).
96 %
Cluster Util
85 %
GPU Util
8.0TB/sRead
500GB/sWrite
The above data is based on cluster usage statistics in Feb. 2022