The Nvidia DGX-1 supercomputer however is said to offer the throughput of 250 x86 servers to meet the increasing tough computing demands posed by artificial intelligence applications.
The DGX-1 is designed specifically for deep learning, and will work with neural networks.“Artificial intelligence is the most far-reaching technological advancement in our lifetime,” said Jen-Hsun Huang, CEO and co-founder of NVIDIA. “It changes every industry, every company, everything. It will open up markets to benefit everyone. Data scientists and AI researchers today spend far too much time on home-brewed high performance computing solutions. The DGX-1 is easy to deploy and was created for one purpose: to unlock the powers of superhuman capabilities and apply them to problems that were once unsolvable.”
Nvidia has also squeezed out performance improvements thanks to the NVLink high-speed interconnect for maximum application scalability. The company claims this 16nm FinFET fabrication technology allows for unprecedented energy efficiency. Additionally, the chip on wafer on substrate with HBM2 is geared for big data workloads; and new half-precision instructions delivers more than 21 teraflops of peak performance for deep learning.
“NVIDIA GPU is accelerating progress in AI,” said Yann LeCun, director of AI Research at Facebook. “As neural nets become larger and larger, we not only need faster GPUs with larger and faster memory, but also much faster GPU-to-GPU communication, as well as hardware that can take advantage of reduced-precision arithmetic. This is precisely what Pascal delivers.”
The specs of the Nvidia DGX-1 are impressive. It offers uup to 170 teraflops of half-precision (FP16) peak performance. This is thanks to eight Tesla P100 GPU accelerators, 16GB memory per GPU, as well as NVLink Hybrid Cube Mesh, 7TB SSD DL Cache, and Dual 10GbE, Quad InfiniBand 100Gb networking.
The Nvidia DGX-1 supercomputer also comes equipped with a complete suite of deep learning software that is designed to help researchers and data scientists to quickly and easily train deep neural networks.
For more information, visit NVIDIA’s click here