(*) Per Accelerator performance for A100 computed using NVIDIA 8xA100 server time-to-train and multiplying it by 8 | Per Chip Performance comparisons to others arrived at by comparing performance at the closest similar scale. Object detection heavyweight (Mask R-CNN) We set 16 performance records with eight on a per-chip basis and eight at-scale training in the commercially available solutions category. In fact, systems built upon the NVIDIA AI platform are the only commercially available systems to make submissions across the board.Ĭompared to our previous MLPerf v0.7 submissions, we improved up to 2.1x on a chip-to-chip basis and up to 3.5x at scale. NVIDIA submitted MLPerf v1.0 training results for all eight benchmarks, as is our tradition. It is continually evolving to reflect the state-of-the-art AI applications. The latest MLPerf v1.0 training round includes vision, language and recommender systems, and reinforcement learning tasks. MLPerf is an industry-wide AI consortium tasked with developing a suite of performance benchmarks that cover a range of leading AI workloads widely in use.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |