PyTorch Foundation Welcomes Nine New Members As Momentum Builds Around Open Source AI Center of Gravity

New Platinum members include Nebius, Nscale and Shopify, deepening the PyTorch Foundation's role as the intelligence layer for open source AI development

SAN JOSE, Calif., Oct. 20, 2026 /PRNewswire/ -- The PyTorch Foundation, a community-driven hub for open source AI under the Linux Foundation, today announced the addition of nine new members at PyTorch Conference North America 2026. Expansion of the PyTorch Foundation ecosystem signals increased advancements to come in open source AI, with the foundation serving as a compounding hub for innovation.

The Foundation welcomes the following new members:

• Platinum: Nebius, Nscale, and Shopify Inc.
• Silver: LightSeek Foundation and Lablup Inc.
• Associate: SKOPOS INDIA FOUNDATION, Siksha 'O' Anusandhan, Messiah University, and Valencia College

AI innovation now happens across all stack layers simultaneously, with research, models, infrastructure, hardware, and production continuously driving each other forward. The PyTorch Foundation serves as the central hub of this interconnected ecosystem, uniting researchers, developers, and companies building open AI. Membership growth advances PyTorch as the critical enabler of AI hardware, with the future of AI innovation built on projects like PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors.

"Open systems learn faster when more people, projects, and organizations are working together, creating compounding gains," said Mark Collier, executive director of the PyTorch Foundation. "Open source ecosystems let breakthroughs travel between organizations instead of staying locked inside one company. We are thrilled that these new members will share their advancements with the foundation in the pursuit of accelerated, open intelligence."

Ecosystem Growth: PyTorch Foundation Member News 

Recent milestones across the PyTorch ecosystem highlight how AI itself is joining the learning loop to improve code, kernels, and research workflows. Updates include:

• Bringing ExecuTorch to Arm Cortex-M CPUs – ExecuTorch now provides a direct path from PyTorch training to on-device inference. The Cortex-M backend enables PyTorch models to run directly on Arm Cortex-M CPUs with Arm CMSIS-NN acceleration. It supports all capability tiers (Scalar, DSP, and MVE/Helium) from a single workflow.
• The announcement of TorchNeuron –TorchNeuron is an open source native PyTorch backend for AWS Trainium (and corresponding Amazon EC2 instances: Trn2 and Trn3), enabling AI researchers and practitioners to experiment and run PyTorch workloads with a single configuration change. With eager execution, torch.compile, and native support for PyTorch APIs (DTensor, torch.distributed) and libraries like TorchTitan, TorchNeuron enables workflows from research experimentation to production deployment.
• Native PyTorch is now publicly available for TPUs – Google is proud to announce TorchTPU, native PyTorch for TPU developers, enabling developers to deploy models on TPUs with minimal code changes and access popular ecosystem projects (vLLM, SGLang, Ray, LLM-d).
• Extending Inductor for the IBM Spyre Accelerator – In a recent blog, IBM's AI-native Spyre software stack extends PyTorch's Inductor compiler and native runtime, reducing Spyre-specific compiler code by 7x while maintaining performance parity on tested workloads. AI-assisted development enabled support for thousands of models with just 44 adapters and approximately 10,000 lines of code, complemented by hardware-agnostic vLLM integration and continuous testing against upstream PyTorch updates.
• A new case study in which Shopify built a continual learning loop with PyTorch and vLLM – Using a continual learning loop with PyTorch and vLLM, Shopify compresses production failures into model weights every day, beating frontier-model quality, and cutting serving costs by 96%.

The open source community is gathering in San Jose for PyTorch Conference North America this week. From bare-metal infrastructure to applications and agents, the conference covers the entire AI stack, exploring the interconnected system building AI in the open. The foundation brings together practitioners focused on training, inference, kernel engineering, and responsible AI work, creating a hub for the future of open intelligence.

To learn more about the next breakthrough in open source AI, visit https://pytorch.org/foundation.

Supporting Quotes

"Lablup has built Backend.AI as an open-source project from the beginning, and PyTorch workloads run on our platform every day. We have benefited greatly from the PyTorch ecosystem over the years. Joining the PyTorch Foundation is our way of supporting the community that has helped us grow and contributing what we have learned from operating AI infrastructure at scale."
– Jeongkyu Shin, Co-founder and CEO, Lablup Inc.

"Efficient inference is essential to making AI accessible at global scale, and we believe the infrastructure behind it must be open. PyTorch brings developers, researchers, and hardware innovators together around a shared foundation. Joining the PyTorch Foundation allows us to contribute directly to that ecosystem and help turn advances in models and hardware into practical improvements in performance, cost, and accessibility for the next generation of AI applications."
– Yineng Zhang, President and Governing Board, LightSeek Foundation

"At Nebius, we believe the future of AI depends on a strong, connected ecosystem, from training to deployment. Joining the PyTorch Foundation as a Platinum member allows us to collaborate more deeply with the community and contribute our expertise in high-performance infrastructure and applied AI. Together, we can help developers and enterprises move more easily from experimentation to production and scale their AI applications."
– Ryan Hanrui Wang, Senior Vice President, Applied AI, Nebius

"Most infrastructure providers treat the framework and the data center as two separate problems. We treat them as one. PyTorch has remained the base layer of AI because it kept the Pythonic style researchers know, while getting serious about performance. Ray, also part of the PyTorch Foundation, does something different: getting that work to run across thousands of GPUs. With Anyscale, the creators of Ray, set to join Nscale, we're not just using that ecosystem, we're building the infrastructure underneath it. Joining the PyTorch Foundation as a Platinum member is us backing that directly, not just benefiting from it."
– Nick Jones, VP of Cloud Native Platform Engineering, Nscale

About the PyTorch Foundation
The PyTorch Foundation is the vendor-neutral home for the open source intelligence layer developers use for training, optimizing, serving, and running models on any chip in any cloud for any agent. As a community-driven hub hosted by the Linux Foundation, the PyTorch Foundation supports the core PyTorch framework alongside a growing portfolio of innovative projects including vLLM, DeepSpeed, Ray, Helion, and Safetensors. Through open governance, strategic support, and a global contributor community, the PyTorch Foundation empowers developers, researchers, and enterprises to build and deploy AI at scale. Learn more at https://pytorch.org/foundation. 

About the Linux Foundation
The Linux Foundation is the world's leading home for collaboration on open source software, hardware, standards, and data. Linux Foundation projects, including Linux, Kubernetes, Model Context Protocol (MCP), OpenChain, OpenSearch, OpenSSF, OpenStack, PyTorch, Ray, RISC-V, SPDX and Zephyr, provide the foundation for global infrastructure. The Linux Foundation is focused on leveraging best practices and addressing the needs of contributors, users, and solution providers to create sustainable models for open collaboration. For more information, please visit us at linuxfoundation.org.

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see its trademark usage page: www.linuxfoundation.org/trademark-usage. Linux is a registered trademark of Linus Torvalds.

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SOURCE PyTorch Foundation