TRANSIT (TRANsparent Scale-In for multi-node Training) is a runtime that makes unified virtual memory practical for large-scale LLM training, enabling models to train on up to 50% fewer GPUs without requiring modification to existing PyTorch training code.

Hyungyo Kim, Ph.D. Candidate in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign will present on TRANSIT as a poster at PyTorch Conference North America with Apoorve Mohan from IBM Research.

Learn how to train more with less at PyTorch Conference North America: https://hubs.la/Q04v4SL60

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Attendee

Super Early BirdMar 31–Apr 10, 2026

Early BirdApr 11–Jul 31, 2026

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