Tensors and neural networks in Python with strong hardware acceleration. PyTorch is an open source project at the Linux Foundation. #PyTorchFoundation
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Intel and AMD are working together to simplify AI development on x86.
By bringing @PyTorch CPU optimizations together through oneDNN, we’re creating a more consistent software path for developers across Intel and AMD processors.
Learn more: ms.spr.ly/6011avvs3
Huge news! Also read more about TorchTPU on the PyTorch blog: pytorch.org/blog/introducing…
Big day for the @PyTorch ecosystem at #PyTorchConf2026 🚀
We’re officially open-sourcing TorchTPU—enabling native, hardware-accelerated PyTorch execution across Cloud TPUs and custom infrastructure.
Here’s everything we launched to supercharge your training & inference workloads 🧵👇
goo.gle/4Bl0zdG
Powered by PyTorch 🔌
Your AI workflows shouldn't be tied to your desk. With AI Playground Home Agent and Slack/Telegram, you can launch @PyTorch-powered image generation and content creation from your phone while the compute stays local on your Intel-powered AI PC.
See it in action at PyTorch Conference or: ms.spr.ly/6010avx5o
Thanks for joining us at #PyTorchCon @CloudNativeFdn!
The collaboration between deep learning teams and cloud native engineers doesn't stop when the expo floor closes. We're wrapping up two great days at @PyTorch Conference North America and taking these shared insights straight into KubeCon + CloudNativeCon.
Register for KubeCon + CloudNativeCon North America: bit.ly/4hwqfuN
Openness Is AI’s Future. And PyTorch is leading the way.
PyTorch sits at the heart of AI development and is increasingly the software layer through which hardware vendors compete for workloads, and this year's conference showed that plainly, with Google's TorchTPU and Amazon's TorchNeuron adding native backends that port existing PyTorch work onto their own accelerators.
Behind the common modeling interface, though, the kernels, compilers and low-level system software are still fragmented, and with more than 600,000 GitHub projects and roughly 150 pull requests a day, success is straining the maintainers as much as it is propelling the ecosystem.
Read the full report on the state of PyTorch and the case that its open future is already here: buff.ly/7U6nTsq
#PyTorch #AI #OpenSource #MachineLearning #LLM
#PyTorchCon North America 2026 brought 3,500+ attendees to San Jose to accelerate open intelligence.
The PyTorch Foundation welcomed nine new members, including @Nebius, @Nscale, and @Shopify as Platinum members, deepening its role as the intelligence layer for open source AI development.
“Open systems learn faster when more people, projects, and organizations are working together, creating compounding gains,” said @sparkycollier, Executive Director of the PyTorch Foundation. “Open source ecosystems let breakthroughs travel between organizations instead of staying locked inside one company.”
@Google and @Meta introduced TorchTPU for native PyTorch execution on Google TPUs. @awscloud announced TorchNeuron for AWS Trainium. TorchTitan expanded model support, improved training performance, and introduced reinforcement learning through TitanRL.
The program featured more than 100 sessions and 144 poster sessions, alongside Day 0 training and workshops. In the Community Expo, attendees connected with sponsors, exhibitors, and core maintainers through booth conversations, demonstrations, and Meet the Developers.
The 2026 PyTorch Contributor Awards recognized innovation, collaboration, and impact across PyTorch, @vllm_project, @DeepSpeedAI, Ray, Helion, and Safetensors.
Explore the announcements, keynotes, technical updates, contributor recognition, and community highlights in our recap.
Thank you to the contributors, maintainers, speakers, trainers, program chairs and committee, sponsors, and attendees who made #PyTorchCon possible.
Read the recap: pytorch.org/blog/pytorch-con…
Stay tuned for session slides and recordings, and save the date: PyTorch Conference returns to San Jose, October 6–7, 2027!
PyTorch Foundation welcomes its 2026 cohort of 24 ambassadors, representing communities from Nigeria and Burkina Faso to Singapore, India, Romania, Spain, the United Kingdom, and the United States.
Each ambassador brings a unique perspective, technical background, and connection to their local community. Together, they are creating more opportunities for people to discover open source AI, develop practical skills, and contribute to the technologies shaping its future.
Meet our 2026 ambassadors:
Adesina Lekan Samuel (@limitless__100), Soumana Dama, Ronnie Atuhaire, Farah Abdou, Abraham Imani (@imanibahati0), Simanga Comfort Mchunu, Mark Gatere (@gatere_mark), Alpha Romer Cooma, Tsai Li Ming (@tsailiming), Mu Xiaohui, Wang Zhipeng, Sumantro Mukherjee (@BytesOfBinary), Parichay Das, Sudhir Dharanendraiah, Costin-Alexandru Deonise, Ricardo Noriega (@rickynds), Matthew Cossins, Johnny Kozman, Ali Tavallaie (@AliTavallaie), Amr Osama Abdellatif, Özge Çinko, Aditya Mehra, Ali Gowani, Samaresh Kumar Singh.
We look forward to seeing what the 2026 PyTorch Foundation Ambassadors will teach, organize, create, and contribute in the year ahead.
Read the announcement: pytorch.org/blog/meet-the-20…
The TorchTitan team landed massive improvements in the past year without losing its minimal, extensible ethos. Enhancements include support for SOTA models, dramatic improvements of out-the-box performance at scale, RL support, and new communication features.
Read our latest blog to learn more and to get started: pytorch.org/blog/torchtitan-…
We welcome LightSeek Foundation @lightseekorg as a Silver Member of PyTorch Foundation.
Its membership deepens LightSeek Foundation’s commitment to building open inference infrastructure with the community. LightSeek Foundation’s TokenSpeed and SMG are PyTorch Ecosystem projects.
Read the announcement: pytorch.org/blog/pytorch-fou…
LightSeek Foundation has joined the @PyTorch Foundation as a Silver Member.
TokenSpeed and SMG are both PyTorch Ecosystem projects. This membership deepens our commitment to building open inference infrastructure with the community.
As our president @zhyncs42 shared: “Efficient inference is essential to making AI accessible at global scale, and we believe the infrastructure behind it must be open.”
We look forward to working with the PyTorch community to improve performance, lower costs, and make AI more accessible.
Read the announcement: pytorch.org/blog/pytorch-fou…
Congratulations to the 2026 PyTorch Contributor Award winners, recognized today at #PyTorchCon 2026 for their innovation, collaboration, and impact across all PyTorch Foundation projects including PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors.
Outstanding Ambassador: Jiho Kim
Newcomer: Jiangyun Zhu (@jiangyun_zhu), Johannes Lennart Zeiser
Knowledge Enhancer: Abdulsalam Yazid Bande (@abdulsalambande), Suman Debnath (@_sumand)
Bug Buster: Guilherme Vieira Leobas, Vishal Goyal
Review Powerhouse: Wentao Ye
Catalyst: Jiahao Tan, Harry Mellor (@hmellor_)
Innovator: Masahiro Tanaka, Elfie Guo, Xinan Lin
Ecosystem Champion: Guangye Yu
Superhero: Thomas Ortner
Nominees: Chandra Mohan Singh Negi, Arun Bhandari, Masahiro Hiramori, Luka Govedič, Irakli Salia, Jiahao Chen, Kunshang Ji, Rohit Kumar Tiwari, Subin George, Xunzhuo Liu, and Aaron Gokaslan.
Thank you to Cara Delia (@RedHat) and Chris Gottbrath (Gottbrath Technologies) for presenting the awards, and to Red Hat (@RedHat) for sponsoring them.
And thank you to our 2026 Awards Committee: Chris Gottbrath (Gottbrath Technologies), Chris Hoge (@nvidia), Ashok Emani (@intel), Piotr Bialecki (@nvidia), Yashasvi Misra (@CloudNativeFdn), Sudhir Dharanendraiah (@RedHat), Zesheng Zong (@Huawei), Jiawei Li, and Kaichao You (@inferact).
Wednesday's Demo Theater at #PyTorchCon features 13 presentations on training, inference, hardware enablement, and agentic workflows. All sessions are in the Community Expo on the Concourse Level.
10:40–10:50 AM: Maen Suleiman (@awscloud) demos reinforcement learning on Trainium through TorchNeuron, the native PyTorch backend for AWS Trainium, with TorchTitan training, vLLM-Neuron generating rollouts, Monarch orchestrating, and TorchStore syncing weights.
10:55–11:05 AM: Ali Taha (@baseten) breaks down how Wan 2.2 generates a five-second video in under three seconds, a 53.6x improvement over baseline, using timestep distillation, custom kernel engineering, and four-bit quantization.
12:20–12:30 PM: Rain Jiang and Kevin Li (@verdacloud) demo WideEP for expert-parallel MoE inference on an NVIDIA NVL72 system, using a Rust-based frontend to run and monitor the workload.
12:35–12:45 PM: Tristan Webb (@dMatrix_AI) presents dmx-compressor, a PyTorch 2.0 based quantization framework that gives ML engineers numerical reference output to validate models running on ASIC hardware.
12:45–12:55 PM: Lin She (@clockworkio) demos surviving link and node failures in PyTorch training live, with LinkPass and TorchPass keeping the job running on the same training step.
1:00–1:10 PM: Ashok Emani and Qiacheng Li (@intel) show AI Playground Home Agent, which runs PyTorch AI workflows on an Intel AI PC and lets you use them from your phone.
1:15–1:25 PM: Zesheng Zong (@Huawei) presents CRCR (Cross-Repository CI Relay) for joint testing between PyTorch pull requests and accelerator backends, catching regressions at the PR phase.
1:30–1:40 PM: FNU Harsh and Sarah Ahmed (@LightningAI) demo an agent reproducing ML research on Lightning, showing where it succeeds autonomously and where it needs intervention.
1:45–1:55 PM: George Novack (@inferact) introduces TPU megakernels, with Kimi K3 megakernels running inside vLLM-TorchTPU.
2:00–2:10 PM: Jay Dawani (Lemurian Labs) examines why heterogeneous computing is so hard and what a hardware-agnostic stack has to get right.
2:15–2:25 PM: Minwook Ahn (@RebellionsAI) shares how extending PyTorch's device abstraction to support device-persistent tensors minimizes data movement between host and device.
2:30–2:40 PM: Jesse Treger (HPE) introduces OpenCLIC, an open inference communication framework for connectionless networks that is being integrated with vLLM.
3:55–4:05 PM: Mengjin Yan and Joshua Lee (@anyscalecompute) cover evolving Ray Core for post-training at scale, from scaling Ray's scheduling to 10,000-node clusters to moving PyTorch tensors directly between GPUs with Ray Direct Transport.
View the Demo Theater schedule: events.linuxfoundation.org/p…
@marksaroufim Fewer training steps don’t necessarily mean faster training. At #PyTorchCon 2026 (@coreauto) examines the linear algebra kernels behind better optimizers.
Mark focuses on QR, symmetric eigendecomposition, and Cholesky, drawing lessons from GPU MODE’s linear algebra competition.
He explains how sequential dependencies constrain GPU execution, and how implementations expose parallel work, overlap independent stages, and combine updates to reduce data movement.
PyTorch retweeted
vLLM 🤝 TPU
🚀 Native PyTorch serving on TPUs is here! Built with @Google and @inferact, vLLM-TorchTPU brings familiar PyTorch tools to TPU inference, with prefix caching, speculative decoding, and disaggregated serving.
🛠️ Excited to keep building with the community!
Native PyTorch on TPUs is here—and TorchTPU is officially open source.
Train and serve models using the tools you already know: @vllm_project, @sgl_project, @_llm_d_, TorchTitan, @huggingface, and @raydistributed.
Learn more from #PyTorchConf2026 → goo.gle/4hWxEVH
TorchTPU enables seamless out-of-the-box model execution and TPU migration while delivering industry-competitive inference and training performance through eager execution optimizations, torch.compile integration, & native hardware acceleration.
Learn more on the PyTorch blog: pytorch.org/blog/introducing…
Native PyTorch on TPUs is here—and TorchTPU is officially open source.
Train and serve models using the tools you already know: @vllm_project, @sgl_project, @_llm_d_, TorchTitan, @huggingface, and @raydistributed.
Learn more from #PyTorchConf2026 → goo.gle/4hWxEVH
Exciting news for a native and principled PyTorch backend for Google TPUs announced at #PyTorchCon this week!
Also learn more on the PyTorch blog: pytorch.org/blog/introducing…
PyTorch retweeted
Nebius is proud to join the @PyTorch Foundation as a Platinum Member.
Ryan Hanrui Wang (@hanrui_w), our SVP Applied AI, joins the Governing Board and Technical Advisory Council.
Many teams on Nebius train and serve models with @PyTorch and @vllm_project, and we're a compute sponsor of vLLM, supporting its development, testing, and benchmarking.
We look forward to working with the community on faster training, more efficient inference, and a shorter path from research to production!
Read more: prnewswire.com/news-releases…
Yiwei Song (@CrusoeAI) shares the vision and progress behind Crusoe’s LLM post-training platform at #PyTorchCon 2026.
The goal is to help customers customize open weights models for their business use cases to improve accuracy and reduce inference cost.
Yiwei outlines the customization lifecycle: start with a strong base model, inject domain knowledge, align to tasks and preferences, make the model smaller and faster to serve, and deploy it in production.
He presents Crusoe Managed RL as a Tinker API-compatible post-training platform, with customers deciding what to learn while Crusoe runs the GPUs.
Watch the keynote livestream: events.linuxfoundation.org/p…
Rachad Alao (@cohere) examines how open science can support transparent and secure AI foundation model development at #PyTorchCon 2026.
Rachad argues for publishing model architectures, training strategies, parameter choices, and evaluation methodologies, alongside an evidence-based risk framework, independent auditing, continuous monitoring, and dynamic safeguards.
He discusses how PyTorch, TorchTitan, and @vllm_project support model development and inference optimization when models are open and accessible, and how access to internal representations enables research in retrieval, reasoning, and multilingual AI.
Zhipeng Wang (@Google) shares technical advances from the @DeepSpeedAI team for large-scale model training at #PyTorchCon 2026.
Zhipeng highlights model-systems co-design and optimizations that enable DeepSpeed to deliver high performance across diverse model architectures, workloads, and accelerator platforms. Highlights include AutoSP for automatic sequence parallelism, Muon optimizer support, and AutoTP and AutoEP for Hugging Face models.
He discusses the continued development of the DeepSpeed open source community and collaboration with model developers, hardware vendors, and AI infrastructure partners to expand adoption and enable emerging large-scale AI workloads.
