Digging into MLPerf Benchmark Suite to Inform AI Infrastructure Decisions
April 09, 2019 / hpcwire
With machine learning and deep learning storming into the datacenter, the new challenge is optimizing infrastructure choices to support diverse ML and DL workflows. How many accelerators do you (really) need? Which interconnect scheme works best? How do various frameworks compare on different compute architectures? When can you choose less expensive CPUs and rely on the GPUs to do the magic? How different are the needs of training versus inferencing?