Thursday, July 30th 2020

NVIDIA A100 Ampere GPU Benchmarked on MLPerf

When NVIDIA announced its Ampere lineup of the graphics cards, the A100 GPU was there to represent the higher performance of the lineup. The GPU is optimized for heavy computing workloads as well as machine learning and AI tasks. Today, NVIDIA has submitted the MLPerf results on the A100 GPU to the MLPerf database. What is MLPerf and why it matters you might think? Well, MLPerf is a system benchmark designed to test the capability of a system for machine learning tasks and enable comparability between systems. The A100 GPU got benchmarked in the latest 0.7 version of the benchmark.

The baseline for the results was the previous generation king, V100 Volta GPU. The new A100 GPU is average 1.5 to 2.5 times faster compared to V100. So far A100 GPU system beats all offers available. It is worth pointing out that not all competing systems have been submitted, however, so far the A100 GPU is the fastest.
The performance results follow:

Source: Hardwareluxx.de
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6 Comments on NVIDIA A100 Ampere GPU Benchmarked on MLPerf

#1
Vayra86
Getting a strong 10 gigarays vibe from these numbers. Can anyone tell what we're really looking at? I also see different versions of software compared along with hardware. What does it compare to anyway?
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#2
Anymal
Vayra86
Getting a strong 10 gigarays vibe from these numbers. Can anyone tell what we're really looking at? I also see different versions of software compared along with hardware. What does it compare to anyway?
Ampere is faster than Volta, hello.
Posted on Reply
#3
Vayra86
Anymal
Ampere is faster than Volta, hello.
Hi. We got that, and my question is how much faster really given all the variables.
Posted on Reply
#4
dj-electric
Vayra86
Hi. We got that, and my question is how much faster really given all the variables.
Depending on type of application, seems to be anywhere between 20% and over double.
Its a bit hard to narrow it, since software is still mostly inexperienced with Ampere. My guess is that as the product becomes more widely available, its results and utilization will improve, much like how Turing did.
Posted on Reply
#5
jeremyshaw
Vayra86
Getting a strong 10 gigarays vibe from these numbers. Can anyone tell what we're really looking at? I also see different versions of software compared along with hardware. What does it compare to anyway?
mlperf is largely open, so you can see for yourself what loads are being run, at least the reference implementations.

mlperf.org/
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#6
Vya Domus
Vayra86
Hi. We got that, and my question is how much faster really given all the variables.
Generic shader performance hasn't increased that much, if that's what you are wondering.
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