How PyTorch Powers Open Foundational Models — From Language to Vision

PyTorch Conference North America 2026

Oct 20, 2026 · 11:10–11:35 · Timezone not confirmed

Training and open sourcing frontier foundational models presents unique challenges—scaling to trillions of tokens across thousands of GPUs, maintaining reproducibility, and enabling a global community to fine-tune and deploy models. In this session, we explore how PyTorch addresses key challenges in building and open sourcing foundational LLMs (Llama) and vision models (SAM). We cover three angles: 1) how PyTorch-native tools like TorchTitan power pre-training through composable parallelism, torch.compile, and Float8—applied to both LLMs and vision architectures, 2) the road to open sourcing foundational models—from internal research to community-ready releases across modalities, and 3) how this foundation enables adopters in healthcare, robotics, and autonomous systems to fine-tune Llama for domain NLP and SAM for zero-shot segmentation. We share hard-won lessons: scaling distributed training without sacrificing code clarity, designing checkpointing for interoperability, and building an open ecosystem where models train on PyTorch and millions customize them. Attendees leave with practical strategies for training, releasing, and sustaining large-scale open models with PyTorch.

Session status: scheduled

Room: 210AE

Track: Applications

Level: Beginner

Official session / agenda

Saving a session is not registration. Check the official agenda for availability, eligibility and any additional fees.

Speakers and their event affiliations

Parent event

In person · San Jose Convention Center, San Jose, California, United States

This describes the parent event. Confirm the session’s attendance requirements with the organizer.

Companies participating in the parent event

These are event-level relationships, not confirmed sponsorship of this session.

Showing 24 of 130 company relationships. View all on the event page.