It will be used for both training and inference workloads. It has 1.3 Petabytes of solid-state storage.
The US Army paid $12 million for the 6 petaflop supercomputer in a shipping container.
The shipping container supercomputer system has:
* 22 nodes for machine learning training workloads, each with two IBM Power9 processors, 512GB of system memory, 8 Nvidia V100 GPUs with 32GB of high-bandwidth memory, and 15TB of local solid-state storage
* 128 nodes for inferencing workloads, each with two IBM Power9 processors, 256GB of system memory, 4 Nvidia T4 GPUs with 16GB of high-bandwidth memory, and 4TB of local solid state storage
* Three solid-state parallel file systems, totaling 1.3 PB
* A 100 Gigabit per second InfiniBand network, as well as dual 10 gigabit Ethernet networks
* Platform LSF HPC job scheduling integrated with a Kubernetes container orchestration solution
* Integrated support for TensorFlow, PyTorch, Caffe, in addition to traditional HPC libraries and toolsets including FFTW and Dakota