Day 0 of PyTorch Conference North America 2026: From Academia to Adoption Workshop 8 academic projects. Short lightning talks. Hands-on mentoring - helping researchers build sustainable, community-driven open source software. #PyTorchCon

PyTorch runs natively on @awscloud Tranium with no code changes. AWS Tranium is @amazon's portfolio of AI chips designed for next-generation deep learning training and inference workloads. You can use it with the tools and workflows you are already familiar with. No new frameworks to learn. No code to change. During PyTorch Conference North America keynote, Robert Hundt, VP Distinguished Engineer, Annapurna Labs, Amazon will share the story and the capabilities AWS provided to meet performance and quality goals. @AmazonScience #PyTorchCon

Squeeze extra performance out of GPU kernels that are already autotuned to the limit by diving below the kernel level into compiler controls. @nvidia CompileIQ and PyTorch Helion have united two distinct optimization layers into a single joint search space. While traditional autotuners search tile shapes, warps, and pipeline stages, ptxas compiler decisions like register allocation and fine-grained scheduling are typically left at default settings. With CompileIQ’s Advanced Control Files (ACFs) integrated directly into Helion’s autotuner, developers can jointly evaluate kernel configurations and compiler controls, unlocking significant speedups on highly optimized production kernels without requiring deep compiler expertise. Learn how to extract the last mile of performance: bit.ly/4i8Oxew

A huge milestone. We can't wait. 🙌

More hardware choice for PyTorch developers: TorchNeuron registers @awscloud Trainium as a native device, built on the same extension points the community created to make PyTorch hardware-agnostic. Open sourcing at PyTorch Conference on Oct. 20. bit.ly/4i3P9Ud

Ravi Gupta, Shiksha Patel, and Rishi Madduri (@AMD) will present “Efficient Inference of Masked Diffusion Language Models on AMD XDNA2 NPU” at #PyTorchCon North America 2026. Their poster presents a power-efficient deployment of LLaDA-8B-Instruct on an AMD NPU. On a Ryzen AI MAX+ 395 APU at sequence length 128, the NPU vendor path ran at 504 ms per forward at 36 W, compared with 843 ms and 110 W for CPU INT4, making it 1.67x faster and 8.9x better on energy. The poster will also cover the INT4 quantization and graph-partitioning pipeline, the CastAvx fix, and the runtime approach used for single-step prefill on the XDNA2 NPU. Register for PyTorch Conference North America 2026: hubs.la/Q04v4SL60

Looking for sessions to catch at #PyTorchCon North America 2026? Chris Hoge (@nvidia), co-chair of the PyTorch Ecosystem Working Group, highlights five talks for developers working on open source training and performance. Hoge will co-present “Ecosystem Working Group: Unlocking Community Impact Through the PyTorch Landscape,” covering what the PyTorch Ecosystem Landscape is, how projects can apply for ecosystem status, and how lifecycle management works for Landscape projects. He also highlights sessions featuring NVIDIA engineers on hiding expert parallel communication in Mixture-of-Experts training, parameterized dynamic-shaped CUDA graphs, building fused compute-communication kernels with PyTorch Symmetric Memory, and scaling PyTorch data loaders by a factor of 10. Register for PyTorch Conference North America 2026: hubs.la/Q04v4SL60

How do you deliver full API parity across heterogeneous accelerators with zero additional overhead? At PyTorch Conference North America 2026, Zesheng Zong (@Huawei), PyTorch Foundation Ambassador and Accelerator Integration Working Group Co-Chair, will share key technical updates on backend integration. The community is adapting over 600,000 upstream test cases to be generic across all backends, providing a scalable path toward complete API parity. Alongside this effort, Cross-Repository CI Relay connects every PyTorch pull request directly to accelerator repositories, enabling real-time bidirectional testing, early regression detection, and a lower barrier to entry for out-of-tree hardware maintainers. Join us in San Jose, California next week to see how these foundational updates simplify hardware enablement across the ecosystem: hubs.la/Q04v4SL60 #PyTorchCon @Zesheng_Zong

ExecuTorch is now an official @ZephyrIoT external module, bringing PyTorch model deployment into Zephyr’s firmware build workflow. Zephyr Project’s walkthrough covers setup and runs quantized MobileNetV2 on an Alif Ensemble E8 DevKit, with the convolutional graph on the Arm Ethos-U55 NPU and input quantization and output dequantization on the Cortex-M55 CPU. Read more: zephyrproject.org/executorch…

🤠 Hats off to our #PyTorchCon North America diamond sponsors. Connect with the people building the future of machine learning in San Jose, CA on October 20-21. Learn more: bit.ly/3TKs7bi

Zephon’s TorchTitan integration preserves identical global batches when training resumes on a different GPU count. In our latest blog’s 1B-parameter model test, training resumed from a 16-GPU checkpoint on 8, 32, and 64 GPUs, and the SHA-256 hash of every global batch matched the original run. Zephon tokenizes, packs, shuffles, and mixes data inside the training job, avoiding a preprocessed copy of the corpus. The data, tokenizer, seed, mixture, context length, and global batch size must stay unchanged when resuming. The lane count must divide evenly across data-parallel replicas, and each optimizer step’s batch must split evenly across lanes. Training losses can still differ because floating-point reduction order depends on GPU count. ✍️ @datologyai @josh_wills @MaxiBoether, Ties Robroek (@datologyai) Read more: pytorch.org/blog/bringing-el…

Ray All the Way Down: A Heterogeneous, Elastic PyTorch Training Stack at LinkedIn Join Tommy Li and Tao Huang from @LinkedIn at PyTorch Conference North America 2026 for their lightning talk, Ray All the Way Down, on October 20. They will share how Ray coordinates CPU preprocessing and GPU training across LinkedIn's distributed pipelines, maintaining high performance, resilient scaling, and flexible data loading for reinforcement learning workflows. Join the open source AI community in San Jose: hubs.la/Q04v4SL60 #PyTorchCon

Only the keynotes will be live-streamed this year.

High 5️⃣ to our #PyTorchCon North America gold sponsors. Top AI pioneers, researchers, and developers are gathering in San Jose, CA on October 20-21. Don't miss your chance to join! Learn more: bit.ly/3TKs7bi

Congrats on a great event!