Advanced Lessons

Our Journey With TorchTitan - Linsong Chu & Garrett Goon, IBM Research

Panel Discussion: The Future of Agents and Human-Agent Collaboration

Sponsored Session: Architecting AI: Efficient Models, Everywhere - Sharbani Roy, Arm

Generating State-of-the-Art GEMMs for Heterogeneous Hardware with... - Michael Lazos & Henry Tsang

Sponsored Session: Lightning Talk: Accelerated Software for a Post-Moore World - Jay Dawani

No GPU Left Behind: Scaling Online LLM Training With... - Mert Toslali & Yu Chin Fabian Lim

Lightning Talk: FlexCP: FlexAttention for Long Sequence Training... - Xilun Wu & Chien-Chin Huang

The AI Scientist-v2: Workshop-Level Automated Scientific Discovery via Agentic... - Yutaro Yamada

Lightning Talk: Flex Attention for Inference - Boyuan Feng & Driss Guessous, Meta

Sponsored Session: Amazingly Fast and Incredibly Scalable Inference... - Harry Kim & Laikh Tewari

Mapping the Open Model Landscape in 2025 - Nathan Lambert, Ai2

Thunder: Distribute and Optimize Your PyTorch Models With Zero... - Luca Antiga & Thomas Viehmann

The Four Pillars of Large-Scale Decentralized Pre-Training: Methods, Bottlenecks... - Bowen Peng

Panel Discussion: Are We Measuring Intelligence or Just Benchmarks?

Scaling an Open Environments Ecosystem for Reinforcement Learning - Will Brown, Prime Intellect

Accelerating Open-Source RL and Agentic Inference with vLLM - Michael Goin, Red Hat | vLLM

Panel Discussion: The AI Kernel... - Robert Lange, Simran Arora, Nathan Lambert, Mark Saroufim

Scaling Agentic Intelligence from Pre-Training to RL - Aakanksha Chowdery

The Future Is Tiled: Using CuTile & TileIR To Write Portable, High-performance GPU...- Jared Roesch

Keynote: Noam Brown, Research Scientist at OpenAI in Conversation with Joe Spisak, Meta

End-to-End Optimizing Multi-Turn RL and High-Performance Inference in Agents with... - Chenyang Zhao

Panel Discussion: Training and Inference at Planet Scale

Sponsored Session: Everything Everywhere all at Once: vLLM...- Brittany Rockwell & Shireen Kheradpey

Keynote: Olmo-Thinking: Training a Fully Open Reasoning Model - Nathan Lambert

Keynote: Jeremy Howard, Founding Researcher at fast.ai in Conversation with Anna Tong, Forbes

Keynote: PyTorch Technical Deep Dive - Alban Desmaison, Peng Wu, Mark Saroufim & Edward Yang, Meta

Keynote: The Physical Turing Test: Solving General Purpose Robotics - Jim Fan, NVIDIA

The Building Blocks of Today’s and Tomorrow’s Language Models - Sebastian Raschka, RAIR Lab

Keynote: Marin: An Open Lab for Frontier AI - Percy Liang, Together

Keynote Panel Discussion: Signal vs. Noise: Betting on AI’s Next Wave

Keynote Panel Discussion: Agentic AI - A. Dhinakaran, J. Liu, A. Jones, S. Colvin & B. Lorica

PyTorch APIs for High Performance MoE Training and Inference - D. Vega-Myhre; Ke Wen & N. Gimelshein

Sponsored Session: Beyond the Node: Scaling Inference with Cluster-Wide KVCache... - Alon Yariv

Leveraging PyTorch for Generative AI in Distributed Edge Clouds - Tina Tsou

DeepSpeed: Efficient Training Scalability for Deep Learning - Tunji Ruwase, Snowflake

Lightning Talk: Unlock the Future of Generative AI: TorchTitan's Latest... - Tianyu Liu & Jiani Wang

Sponsored Session: Arctic Inference: Breaking the Speed-Cost Tradeoff in LLM Serving - Aurick Qiao

Efficient MoE Pre-training at Scale on AMD GPUs With TorchTitan -Liz Li & Yanyuan Qin, Matthias Reso

Enabling VLLM V1 on AMD GPUs With Triton - Thomas Parnell, IBM Research & Aleksandr Malyshev, AMD

Sponsored Session: Lightning Talk: Per-iteration PyTorch Visibility... - Shruti Verma & Yi-Ting Wu

Lightning Talk: Vllm-triton-backend: How To Get State-of-the-art Performance on... - B. Ringlein

Sponsored Session: Lightning Talk: PyTorch and Democratization of AI Accelerators - Hong-Seok Kim

Sponsored Session: PyTorch Distributed and Fault Tolerance - Tristan Rice, Meta

Transformers: Standardizing Model Definitions Across the PyTorch Ecosystem - L. Debut & A. Zucker

Enabling Lightweight, High-Performance FSDP With NVIDIA GPU - J. Chang CN, C. Ye, X. Chen & S. Lym

Verl: A Flexible and Efficient RL Framework for LLMs - Hongpeng Guo & Ziheng Jiang, ByteDance Seed

Lifecycle of a Parameter - Philip Bontrager, Meta

Fault Tolerance Training at Pinterest - Chia-Wei Chen & Karthik Anantha Padmanabhan, Pinterest