05/14/2021 | Press release | Distributed by Public on 05/14/2021 21:33
Results show Qualcomm Cloud AI 100 provides the most efficient AI Inference acceleration across multiple AI frameworks and models.
May 14, 2021
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Introducing new metrics to measure power consumption, ML Commons is responding to the need to benchmark power efficiency. Datacenters need to not only be powerful but also provide high efficiency for the lowest Total Cost of Ownership (TCO). Products that can deliver highest performance at lowest power are most critical.
Qualcomm Technologies submitted its Qualcomm Cloud AI 100 accelerator and convincingly topped the Inferencing Datacenter and Edge charts with the highest Inference performance (at lowest power) among all MLPerf 1.0 submissions. The two images below illustrate the latest MLPerf 1.0 benchmark scores with performance on the Y-axis and power consumption on the X-axis. With the leading products occupying the top/right hand side of the chart, it is clear that the Qualcomm Cloud AI 100 is the most efficient AI accelerator on the MLPerf 1.0 list, beating out the competition.Here's what the media had to say:
'Nvidia's only defeats that Kharya acknowledged came in the new, separate MLPerf benchmarking for energy efficiency, in which it was narrowly bested by Qualcomm's [Cloud] AI 100 in two of six energy efficiency test categories on the basis of performance per watt.' -- Dan O'Shea, FierceElectronics
'The MLPerf V1.0 release is the first time to include power metrics, measured as total system power over at least a 10-minute run-time. While Qualcomm [Technologies] only submitted [Qualcomm Cloud] AI100 results for image classification and small object detection, the power efficiency looks good. The chip performs reasonably well, delivering up to 3X performance over the (aging) NVIDIA T4, while the more expensive and power hungry NVIDIA A100 roughly doubles the Qualcomm [Cloud AI] performance on a chip-to-chip basis, based on these limited benchmark submissions….The new Qualcomm Cloud AI100 platform delivers up to 70% better performance per watt for some data center inference workloads, at least on image classification. These submissions were run on the Gigabyte AMD EPYC server we recently mentioned.' -- Karl Freund, Forbes
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Mike Vildibill
Vice President, Product Management, Qualcomm Technologies
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John Kehrli
Senior Director, Product Management, Qualcomm Technologies
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