Cover Image for GPU MODE IRL Hackathon - PyTorch Conference Europe side event
Cover Image for GPU MODE IRL Hackathon - PyTorch Conference Europe side event
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The European provider of sovereign, scalable GPU infrastructure – pushing the boundaries of efficient AI.
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GPU MODE IRL Hackathon - PyTorch Conference Europe side event

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Paris, Île-de-France
Registration
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About Event

Join us to wrap up the PyTorch Conference with a GPU MODE IRL hackathon. You can expect:

  • Access to NVIDIA Blackwell Ultra from Verda and other generations from Sesterce

  • Tasks on systems optimizations for large-scale, distributed training

  • Talks from PyTorch (Helion), Prime Intellect, and more

Tasks and prizes TBA very soon!

Schedule

  • 9:30 - Doors open

  • 10:00 - Kick off

  • 11:00 - Tasks start

  • 19:00 - Tasks end

  • 20:00 - Closing ceremony

  • 00:00 - Doors close

Talks

Topics and schedules TBA soon:

  • Erik Schultheis, ISTA-DASLab (low-precision training)

  • Will Feng, Helion team, PyTorch

  • Matej Sirovatka, Prime Intellect (TBA)

  • Paul Chang, Verda (TBA)

  • Youssef El Manssouri, Sesterce (TBA)

Tracks & prizes

Each of the tracks will have 3 winners, with 1 grand-prize winner.

Track #1 by Verda: Blackwell Ultra

  • 1st place: 48 hours on GB300 NVL72 + 1,500 EUR in cloud credits from Verda

  • 2nd place: 1,000 EUR in cloud credits from Verda

  • 3rd place: 500 EUR in cloud credits from Verda

Pretrain an LLM from scratch in a limited time on a Blackwell Ultra Cluster. In this track, you will participate as a team, trying to make optimal use of 360 PFLOP/s (BF16). Will this require asynchronous optimizer steps, large-batch size training, models much larger than chinchilla, or something else entirely?

Track #2 by Sesterce: TBA soon


Sponsors

  • Prime Intellect

  • Verda

  • Sesterce

  • SemiAnalysis

Location
Please register to see the exact location of this event.
Paris, Île-de-France
Avatar for Verda (formerly DataCrunch)
The European provider of sovereign, scalable GPU infrastructure – pushing the boundaries of efficient AI.
6 Going