Supermicro is extending the selection of GPU systems with the NVIDIA HGX A100 8-GPU server to power applications from the Edge to the cloud. The company's entire portfolio includes 1U, 2U, 4U, and 10U rackmount GPU systems; Ultra, BigTwin, and embedded solutions powered by both AMD EPYC and Intel Xeon processors with Intel Deep Learning Boost technology.
"Supermicro has introduced the 4U system, implementing an NVIDIA HGX A100 8-GPU baseboard (formerly codenamed Delta), that delivers 6x AI training performance and 7x inference workload capacity when compared to current systems," said Charles Liang, CEO and president of Supermicro. "Also, the recently announced NVIDIA HGX A100 4-GPU board (formerly codenamed Redstone) is showing wide market acceptance, and we are excited by the ongoing global customer engagement. These new Supermicro systems significantly boost overall performance for accelerated workloads required for rapidly changing markets, including HPC, data analytics, deep learning training, and inference."
Leveraging Supermicro's advanced thermal design, including custom heatsinks and optional liquid cooling, the latest high-density 2U and 4U servers feature NVIDIA HGX A100 4-GPU 8-GPU baseboards, along with a new 4U server supporting eight NVIDIA A100 PCI-E GPUs.
The Supermicro's Advanced I/O Module (AIOM) form factor further enhances networking communication with high flexibility. The AIOM can be coupled with the latest high-speed, low latency PCI-E 4.0 storage and networking devices that support NVIDIA GPUDirect RDMA and GPUDirect Storage with NVME over Fabrics (NVMe-oF) on NVIDIA Mellanox InfiniBand that feeds the expandable multi-GPU system with a continuous stream of data flow without bottlenecks.
In addition, Supermicro's Titanium Level power supplies keep the system green to realise even greater cost savings with the industry's highest efficiency rating of 96%, while allowing redundant support for the GPUs.
"Supermicro systems powered by the NVIDIA A100 can quickly scale to thousands of GPUs or, using new multi-instance GPU technology, each A100 GPU can be partitioned into seven isolated GPU instances to run different jobs," said Paresh Kharya, senior director of Product Management and Marketing at NVIDIA. "NVIDIA A100 Tensor Core GPUs with TensorFloat 32 provides up to 20 times more compute capacity compared to the previous generation without requiring any code changes."
