NVIDIA, Microsoft Boost AI Cloud
March 8, 2017
NVIDIA with Microsoft unveiled blueprints for a new hyperscale GPU
accelerator to drive AI cloud computing.
Providing hyperscale data centers with a fast, flexible path for AI, the
new HGX-1 hyperscale GPU accelerator is an open-source design released
in conjunction with Microsoft's Project Olympus.
The new HGX-1 hyperscale GPU
accelerator provides hyperscale data centers with a fast, flexible path
HGX-1 does for cloud-based AI
workloads what ATX -- Advanced Technology eXtended -- did for PC
motherboards when it was introduced more than two decades ago. It
establishes an industry standard that can be rapidly and efficiently
embraced to help meet surging market demand.
The new architecture is designed to meet the exploding demand for AI
computing in the cloud -- in fields such as autonomous driving,
personalized healthcare, superhuman voice recognition, data and video
analytics, and molecular simulations.
"AI is a new computing model that requires a new architecture," said
Jen-Hsun Huang, founder and chief executive officer of NVIDIA. "The
HGX-1 hyperscale GPU accelerator will do for AI cloud computing what the
ATX standard did to make PCs pervasive today. It will enable
cloud-service providers to easily adopt NVIDIA GPUs to meet surging
demand for AI computing."
"The HGX-1 AI accelerator provides extreme performance scalability to
meet the demanding requirements of fast-growing machine learning
workloads, and its unique design allows it to be easily adopted into
existing data centers around the world," wrote Kushagra Vaid, general
manager and distinguished engineer, Azure Hardware Infrastructure,
Microsoft, in a blog post.
For the thousands of enterprises and startups worldwide that are
investing in AI and adopting AI-based approaches, the HGX-1 architecture
provides unprecedented configurability and performance in the cloud.
by eight NVIDIA® Tesla® P100 GPUs in each chassis, it features an
innovative switching design -- based on NVIDIA NVLink™ interconnect
technology and the PCIe standard -- enabling a CPU to dynamically
connect to any number of GPUs. This allows cloud service providers that
standardize on the HGX-1 infrastructure to offer customers a range of
CPU and GPU machine instance configurations.
Cloud workloads are more diverse and complex than ever. AI training,
inferencing and HPC workloads run optimally on different system
configurations, with a CPU attached to a varying number of GPUs. The
highly modular design of the HGX-1 allows for optimal performance no
matter the workload. It provides up to 100x faster deep learning
performance compared with legacy CPU-based servers, and is estimated at
one-fifth the cost for conducting AI training and one-tenth the cost for
With its flexibility to work with data centers across the globe, HGX-1
offers existing hyperscale data centers a quick, simple path to be ready
Collaboration to Bring Industry Standard to Hyperscale
Microsoft, NVIDIA and Ingrasys
(a Foxconn subsidiary) collaborated to architect and design the HGX-1
platform. The companies are sharing it widely as part of Microsoft's
Project Olympus contribution to the Open Compute Project, a consortium
whose mission is to apply the benefits of open source to hardware and
rapidly increase the pace of innovation in, near and around the data
center and beyond.
Sharing the reference design with the broader Open Compute Project
community means that enterprises can easily purchase and deploy the same
design in their own data centers.