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Nvidia's Ian Buck: A100 GPU Will 'Future-Proof' Data Centers For AI

'By having one infrastructure that can be both used for training at scale as well as inference for scale out at the same time, it not only protects the investment, but it makes it future-proof as things move around,' says Buck, Nvidia's head of accelerated computing, in an interview with CRN.

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'Game Changing For The Data Center'

Nvidia executive Ian Buck said the chipmaker's new A100 GPU will future-proof data centers for artificial intelligence workloads thanks to the processor's unified training and inference capabilities that will pave the way for large-scale infrastructure investments.

Buck, general manager and vice president of accelerated computing at Nvidia and the creator of the company's CUDA parallel computing platform, told CRN that the A100, revealed in mid-May, continues the company's efforts to democratize AI. Key to those efforts is Nvidia's claim that five DGX A100 systems, the new AI system equipped with eight A100s, can perform the same level of training and inference work as 50 DGX-1 and 600 CPU systems at a tenth of the cost.

[Related: Nvidia Data Center Sales Soar As Hyperscalers Adopt A100 GPU ]

"By having one infrastructure that can be both used for training at scale as well as inference for scale out at the same time, it not only protects the investment, but it makes it future-proof as things move around, as networks change — you can configure your data center in any way possible well after you've purchased and physically built it," he said in a recent interview.

This is made possible in part by the A100's new multi-instance GPU feature, allowing the A100 to be partitioned into as many as seven separate GPU instances that can perform work in parallel. Alternatively, eight of the GPUs can be linked with Nvidia's third-generation NVLink interconnect to act as one giant GPU in the DGX A100 or a server using Nvidia's HGX A100 board.

But key to the A100's ability to future-proof data centers is the way it unifies training and interference capabilities into one chip, combining what was previously available in two separate GPUs, the V100 for training and the T4 for inference. By combining these capabilities, Buck said, the A100 can significantly increase the flexibility and utilization of data centers by shifting workloads on the fly.

"Ampere's flexibility to be both an amazing training as well as inference GPU makes it really game changing for the data center," he said.

Nvidia's channel partners will be critical to driving sales and integrating systems for the A100, according to Buck, which will be done through Nvidia's new DGX A100 and HGX-based servers coming from Atos, Dell Technologies, Gigabyte, Hewlett Packard Enterprise, Lenovo, and Supermicro, among others.

"Only through our partners can they help design systems and configure and get them understood and consumed and deployed for the problems that our customers are trying to solve in the broader scheme of things," Buck said.

In his interview with CRN, Buck talked about how Nvidia was able to build a GPU that combines inference and training capabilities, why Kubernetes is important for the future of data centers, how he thinks the A100 will shake up the AI accelerator market and how the coronavirus pandemic is impacting demand for AI. What follows is an edited transcript.

 
 
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