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[N] HGX-2 Deep Learning Benchmarks: The 81,920 CUDA Core “Behemoth” GPU Server

[N] HGX-2 Deep Learning Benchmarks: The 81,920 CUDA Core “Behemoth” GPU Server

Deep learning benchmarks for TensorFlow on Exxact TensorEX HGX-2 Server.

Original Post from Exxact Here

Notable GPU Server Features

  • 16x NVIDIA Tesla V100 SXM3
  • 81,920 NVIDIA CUDA Cores
  • 10,240 NVIDIA Tensor Cores
  • .5TB Total GPU Memory
  • NVSwitch powered by NVLink 2.4TB/sec aggregate speed

Source: blog.exxactcorp.com

Source: blog.exxactcorp.com

Tests were run on ResNet-50, ResNet-152, Inception V3, VGG-16. Also compared FP16 to FP32 performance, and used batch size of 256 (except for ResNet152 FP32, the batch size was 64). Same tests run using 1,2,4,8 and 16 GPU configurations. All benchmarks were done using ‘vanilla’ TensorFlow settings for FP16 and FP32.

For the full write-up + tables and numbers visit: https://blog.exxactcorp.com/hgx2-benchmarks-for-deep-learning-in-tensorflow-16x-v100-exxact-tensorex-server/

submitted by /u/exxact-jm
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.