Live from Jupyter Day - one of the co-located events at PyTorch Conference North America 2026 where data scientists, developers and researchers are coming together to discuss interactive computing and AI-assisted workflows. #PyTorchCon @ProjectJupyter

If you know PyTorch, you know Trainium. @awscloud is bringing multiple sessions to #PyTorchCon in San Jose. AWS Trainium is an AI chip designed from the ground up for next generation deep learning AI training and inference, and now PyTorch runs natively on AWS Trainium --> zero code changes. Same APIs, same debugging, same ecosystem. Markus Adhiwiyogo of AWS, previews how the Neuron team is showing it all: training, profiling, RL, kernel development, and more. @AmazonScience Join us at PyTorch Conference North America 2026: hubs.la/Q04v4SL60

Explore the sessions taking place today at LF AI & Data Summit 2026 where attendees are getting together to discuss open source technologies powering every stage of the AI lifecycle. #PyTorchCon @LFAIDataFdn

#PyTorchCon North America starts TOMORROW! 🎉 Join us in San Jose for two days of open source AI ideas, code, connections, and serious PyTorch power. Can’t make it in person? Sign up to watch the keynote livestream: bit.ly/4zEOQoq Still time to register: bit.ly/4sh3DSw

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

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