Analyzing the performance of Tensorflow training on M1 Mac Mini and Nvidia V100

Re:Analyzing the performance of Tensorflow training on M1 Ma... by rrss on Thursday January 14, 2021 @11:55AM (#25778735)

No, that's not the only thing.

Other characteristics of V100 that may be interesting to people buying GPUs for data centers:

- higher capacity GPU memory. 1080 has 8 GB, V100 has 16 or 32 GB.

- higher bandwidth GPU memory. V100 has HBM2 with a peak of 900 GB/s, 1080 has G5X with a peak of ~300 GB/s.

- ECC support.

- data center certification + warranty

(The geforce warranty covers normal consumer usage, like gaming, and does not cover datacenter use)

- availability of enterprise support contracts.

(If you are buying a ton of GPUs to put in a datacenter, you probably don't want to end up on the normal consumer support line when something goes wrong)

- fast fp64

There are probably others

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Re:Analyzing the performance of Tensorflow training on M1 Ma... by Firadeoclus on Friday January 15, 2021 @05:47AM (#25789293)

A GTX1080 manages about ~9 TFLOPS(fp32) (and has terrible fp16 support), where V100 gets ~15 TFLOPS(fp16), ~30 TFLOPS(fp16), and ~120 TFLOPS(tensor cores).

Apart from one being a gaming product and the other being designed for computational tasks, they're a generation apart and have various small differences that may be quite relevant for individual tasks (such as V100 allowing twice the shared memory - 96 KiB - per thread block)

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Re:Analyzing the performance of Tensorflow training on M1 Ma... by littlestymaar on Thursday January 14, 2021 @05:25PM (#25783624)

Thanks, that makes much more sense!

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