The Nvidia HGX-2 cloud server platform features multi-precision computing capabilities to provide “unprecedented versatility” to support the future of computing, says the company, meeting the requirements of the growing number of applications that combine HPC with AI. It allows high-precision calculations using FP64 and FP32 for scientific computing and simulations, while also enabling FP16 and Int8 for AI training and inference.
“The world of computing has changed,” says Jensen Huang, founder and chief executive officer of NVIDIA. “CPU scaling has slowed at a time when computing demand is skyrocketing. NVIDIA’s HGX-2 with Tensor Core GPUs gives the industry a powerful, versatile computing platform that fuses HPC and AI to solve the world’s grand challenges.”
The platform serves as a “building block” for manufacturers to create advanced systems for HPC and AI. According to the company, it has achieved record AI training speeds of 15,500 images per second on the ResNet-50 training benchmark, and can replace up to 300 CPU-only servers.
It incorporates features such as NVIDIA NVSwitch interconnect fabric, which links 16 NVIDIA Tesla V100 Tensor Core GPUs to work as a single, giant GPU delivering two petaflops of AI performance. The first system built using HGX-2 was the recently announced NVIDIA DGX-2.
Four leading server makers — Lenovo, QCT, Supermicro, and Wiwynn — have announced plans to bring their own HGX-2-based systems to market later this year. And, says the company, four top original design manufacturers — Foxconn, Inventec, Quanta, and Wistron — are designing HGX-2-based systems, also expected later this year, for use in some of the world’s largest cloud datacenters.
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