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STAC-ML Pack for GroqWare™ with Python 3.8 on a GroqNode™ with 8 x GroqCard™ Accelerators

Audited

STAC-ML Markets Inference Benchmark

Stack under test:

  • STAC-ML Pack for GroqWare (Rev A)
  • GroqWare™ SDK 0.9.0.5 devtools and runtime
  • Python 3.8.15; NumPy 1.23.4,
  • Ubuntu Linux 22.04.1 LTS
  • GroqNode™ GN1-B8C-ES:
    • 8 x GroqCard™ 1 Accelerators (GC1-010B)
    • 2 x AMD EPYC™ 7413 24-core Processors @ 2650 MHz
    • 16 slots x 64GB DDR4 (Samsung 3200MHz) - 1024GiB Total

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The use of machine learning (ML) to develop models is now commonplace in trading and investment. Whether the business imperative is reducing time to market for new algorithms, improving model quality, or reducing costs, financial firms have to offload major aspects of model development to machines in order to continue competing in the markets.

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