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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Faster streaming speech recognition on @Arm edge devices: see how ExecuTorch, SME2, FP16 and QC8W balance accuracy and latency—delivering up to 2.12× faster processing on Pixel 11.
Read our latest blog: pytorch.org/blog/optimizing-…
✍ Josiah Davis, Kshitij Sisodia
Join the @DeepSpeedAI team for virtual office hours on October 27!
At last week’s PyTorch Conference, the DeepSpeed team shared the project’s latest progress during the PyTorch Foundation keynote and a technical session. They highlighted advances including AutoEP (expert parallelism), AutoTP (tensor parallelism), AutoSP (sequence parallelism), and Ray-based pipeline parallelism, with benchmarks showing that DeepSpeed matches or outperforms other state-of-the-art frameworks on the workloads evaluated.
These virtual office hours will offer a deeper dive into the features and benchmark results presented at the conference, along with a discussion of DeepSpeed’s proposed Q4 roadmap.
Interested in learning more, asking the team questions, or contributing to these projects? Join the conversation and share your ideas for what DeepSpeed should prioritize next. Everyone is welcome!
📅 Tuesday, October 27
🕘 9 AM PT / 12 PM ET
🔗 Join: zoom-lfx.platform.linuxfound…
Thanks for joining us at PyTorch Conference @alanzhuly and @Qualcomm!
We brought two things to @PyTorch Conference.
At the @Qualcomm booth, we demoed GenieX, the open-source Gen AI runtime my team builds, running @liquidai's LFM-VL on @Innodisk_Corp's Dragonwing IQ9. It watched a live camera feed and described what it saw, all on the device.
Multimodal is the workload moving on-device in production. Small VLMs are good enough now for most of what a camera needs to understand, and you don't pay per frame or wait on the network.
We also ran a hands-on workshop with @awscloud : fine-tune on SageMaker, then run the model locally with GenieX. Most questions were some version of "how do I get my own model running on my own hardware?" That's sovereign AI at developer scale, and it's exactly what GenieX is for.
Learn more on the PyTorch Foundation blog! pytorch.org/blog/bringing-ex…
This is how we bring AI inference to microcontrollers, without the complexity of switching frameworks. The new @Executorch Cortex-M backend uses @Arm CMSIS-NN to accelerate @PyTorch models on Arm Cortex-M CPUs. Take a look at the report here: developer.arm.com/community/…
Smaller & specialized models are becoming dramatically more capable and new approaches to continual learning are enabling AI systems to improve from real-world experience after deployment. Jonathan Bryce, Executive Director of Cloud & Infrastructure at @linuxfoundation @CloudNativeFdn shares about how we're moving from bigger models to better systems during @AgenticAIFdn #AGNTCon + #MCPCon keynote.
The future of AI will depend not on any single model, but on an open set of technologies for building, optimizing, serving and continuously improving intelligence. The @PyTorch Foundation in bringing those technologies together, creating an open foundation that gives developers the freedom to build with any model, on any chip and across any cloud.
PyTorch retweeted
Bigger models? Cool. Better systems? Even better.
Jonathan Bryce of the @LinuxFoundation hit the AGNTCon + MCPCon NA keynote stage to talk about accelerating open intelligence beyond model scaling.
Day 2 is LIVE. Tune in: bit.ly/4dVkjdO
Most AI developers use PyTorch to build their models. And this week @AmazonScience announced a new PyTorch backend for AWS Trainium chips - TorchNeuron. Learn more about the science behind it.
TorchNeuron is now open source. Our new @PyTorch backend translates code into operations executable on AWS Trainium with minimal or no code changes. Learn how the team used AI agents and hundreds of thousands of models to validate it: amazon.science/blog/the-scie…
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…
