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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Validate your real-world PyTorch skills with the PyTorch Certified Associate (PTCA) program.
Designed for early-stage practitioners, PTCA helps you demonstrate foundational ability across model design, training, and deployment while building credibility across the open source AI ecosystem.
Learn more and enroll here: bit.ly/4do6qEx
We ❤️ our ambassadors. The ambassador meetup at #PyTorchCon North America was a good time to connect and make plans for the year ahead.
PyTorch Foundation Ambassadors help grow our projects (PyTorch, vLLM, DeepSpeed, Ray, Helion, & Safetensors) around the world by organizing events, creating educational content, mentoring new users, and contributing to the open source ecosystem.
Meet all of your PyTorch Foundation ambassadors: pytorch.org/programs/ambassa…
🚀 Unlock frontier-level AI training performance with DeepSpeed!
bit.ly/4zLzUoS
Join Masahiro Tanaka of DeepSpeed at PyTorch Day Japan 2026 as he presents “DeepSpeed at the Frontier: Community-Driven Optimizations for State-of-the-Art Training Performance,” exploring the latest innovations advancing the performance and scalability of distributed AI training.
Register by November 11 and save ¥3,000. bit.ly/4zMCquV
PyTorch 2.15 is here, with 3,117 commits from 583 contributors and updates across distributed training, NVGEMM, Apple Silicon, and accelerator platform support.
The release makes the rewritten NCCL backend the default and adds one-sided transports with a NIXL backend. FSDP2 and pipelining gain finer memory and communication control. It also brings faster 4-bit (NVFP4) LLM decoding on NVIDIA Blackwell GPUs, FP8 and faster decode on Apple Silicon, and broader platform support.
On Thursday, November 5, at 11 AM PST, join Tristan Rice (@rice_fry), Natalia Gimelshein, Huy Do, and Jane Xu (@janeyx99) (@Meta) for a live PyTorch 2.15 Q&A, moderated by Chris Gottbrath (Gottbrath Tech). The speakers will share an overview of the release and answer community questions about PyTorch and the new capabilities in this release.
🔗 Read the release blog and register for the live Q&A: pytorch.org/blog/pytorch-2-1…
Thank you for sharing this bug @alexgarabt! We have forwarded this on to the PyTorch team. For future bugs, please log them at github.com/pytorch/pytorch/i…
On Oct 19 at PyTorch Conference North America 2026 in San Jose, attendees spent a full day in our instructor-led PyTorch Associate Training preparing for the PyTorch Certified Associate (PTCA) exam. Here’s what they had to say:
💬 “From basics to advanced, the instructors did a nice job of explaining.”
💬 “Everything was hands-on... with powerful GPUs.”
💬 “I used to think of PyTorch as training models, but it was so much more: benchmarking and profiling.”
💬 “Made me confident for the exam.”
💬 “Absolutely recommend to friends.”
Thank you to our teaching staff from @NVIDIAAI: Allison Ding, Krishna Kalyan, Lakshay Garg, Santosh Bahir, Ian Wang, and Faradawn Yang.
Learn more about getting certified: bit.ly/4do6qEx
Check out what the attendees had to say here: bit.ly/4Bvhb2j
#PyTorchCon
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
