Why attend

• See what’s next: Live, on-stage pitches from AI startups pushing the boundaries of AI.
• Meet the builders: Direct access to technical teams and founders.
• Expand your network: Engage with leading VCs, industry partners, and recruiters.
• Get inspired: Discover breakthrough ideas at their earliest and most exciting stage.

Join us

Be there as innovative AI startups pitch live to a panel of top VCs. Whether you’re an engineer, an investor, or simply passionate about cutting-edge AI, this is a front-row seat to the future.

Save the date: Tuesday, October 21, 2025 – San Francisco

Next steps: Register for the PyTorch Conference and learn more about the Showcase.

Founders building the next game-changing AI tool or platform? Apply to pitch.

Calling All Startups

Pitch live to leading investors, connect with PyTorch engineers, and raise your visibility across the global AI community.

Selected startups receive:

• A live 5-minute pitch slot
• 2 PyTorch Conference Passes
• Promotion through PyTorch marketing channels
• Opportunity to network during the Startup Showcase Reception
• Ability to sponsor a booth in the Startup zone of the conference Expo

Learn more about the Startup Showcase and apply to pitch by September 14, 2025.

Startup Evaluation Criteria

1. Mission Alignment – Evaluates the extent to which the startup’s vision and focus resonate with the foundational values of innovation and community and evolving priorities of the PyTorch ecosystem.
2. Novelty and Differentiation – Considers the distinctiveness of the startup’s concept or technology, emphasizing original thought and the ability to challenge conventional approaches.
3. Technical Depth and Ecosystem Integration – Assesses the level of technical rigor and how deeply the startup’s solution integrates with AI and whether it leverages projects from the PyTorch ecosystem.
4. Strategic Viability and Growth Trajectory – Reviews the soundness of the startup’s business logic, market relevance, and potential to scale effectively.
5. Ecosystem Enrichment – Looks at the startup’s potential to positively influence the broader PyTorch and open-source communities – through contribution, accessibility, or capability expansion.

Calling all VCs

The PyTorch Startup Showcase offers a first look at high-potential startups and industry leading talent building the next wave of AI and ML innovations. As a sponsor, you’ll play an active role in spotlighting breakthrough technologies and connecting with founders before they scale.

Sponsor perks include:

• A seat on the judging panel
• Branding exposure in Startup Showcase marketing and signage
• Engage directly with startups during the reception
• Startup Showcase Application contact list

Last Year’s Startup Showcase

Finalists for the 2024 PyTorch Conference Startup Showcase represented some of the most innovative AI/ML startups in the industry including Remix Inc., Cartesia, OpenBabylon, Remyx AI, A2 Labs, Inc., QuicSnap, Iso AI, CTGT, and Creao.ai. The winner, CTGT empowers companies to create customized models using 500x less compute and went on to raise $7M to help enterprises break through the limits of AI compute.

Last year, the Showcase was moderated by Chappy Asel of The AI Collective and judges included investors and VCs from Felicis, GitHub, Vertex Ventures, Mayfield, Gradient Ventures, and Andreessen Horowitz.

More information about sponsoring the Startup Showcase is available on the PyTorch Conference website.

We’re looking forward to seeing you at the 2025 Startup Showcase!

What is Open Source AI Week?

Open Source AI Week is bringing together the best AI and ML conferences, hackathons, startup showcases, and networking opportunities exploring the intersection of artificial intelligence, machine learning, and open source technology. Taking place between October 18 – 26, 2025 in the San Francisco Bay Area. This week-long celebration is dedicated to fostering innovation, collaboration, and community-driven solutions in the rapidly evolving AI landscape, featuring the PyTorch Conference as the flagship event.

Schedule at a Glance

Below is a current snapshot of the Open Source AI Week lineup. More information about each is available on the Open Source AI Week website.

Tuesday, October 21

• Measuring Intelligence Day
• AI Infra Summit
• PyTorch Conference Startup Showcase
• AI Infra & Open Source Models Meetup

Wednesday, October 22

• PyTorch Conference (Day 1)

Thursday, October 23

• PyTorch Conference (Day 2)
• PyLadies SF at AutoKitteh

Friday, October 24

• dAGI Summit

Plus, stay tuned, as we’ll be adding more events to the lineup soon. 

Add Your Event to Open Source AI Week

If you’re organizing an AI + Open Source event, we welcome your submission to be a part of Open Source AI Week. Submit your event to be added to the Open Source AI Week lineup! 

To ensure that all the events are relevant to the Open Source AI Week and foster an open and inclusive exchange, all submissions will be reviewed against the following guidelines:

• Focus on Open Source AI: Events should center on open technologies within the AI ecosystem, including but not limited to open-source software and hardware for AI development, open standards, open data related to AI, and open benchmarks.
• Bay Area Location & Timing: Events must take place in the Bay Area between October 18–26, 2025, during Open Source AI Week.
• Commitment to Inclusion: Events, particularly those featuring speakers, should actively encourage diversity and be open to all attendees, regardless of race, gender, or background.

Open Source AI Week is your opportunity to get inspired, get involved, and help shape the future of AI.

Get ready, San Francisco. The keynote lineup for PyTorch Conference is officially here and it’s packed with some of the sharpest minds in open source AI and machine learning.

This October 22–23, join us to hear from leading researchers, developers, and engineers driving innovation across the PyTorch ecosystem. You’ll gain insight into how GenAI models are evolving, how frameworks are scaling, and how the open source community is pushing the limits of what’s possible.

Thought Leaders Taking the Stage 

• • Dr. Sharon Zhou, Vice President of Artificial Intelligence, AMD
  • Eric Xing, President and University Professor, MBZUAI; Professor of Computer Science, CMU; and Co-founder & Chief Scientist, Genbio AI
  • Nathan Lambert, Senior Research Scientist, Ai2
  • Sergey Levine, Associate Professor, Department of Electrical Engineering and Computer Sciences, UC Berkeley
  • Animashree (Anima) Anandkumar, Bren Professor of Computing and Mathematical Sciences, California Institute of Technology
  • Robert Nishihara, Co-Founder, Anyscale & Co-creator, Ray
  • Ion Stoica, Professor of Computer Science, UC Berkeley; Director of Sky Computing Lab; and Co-founder of Anyscale, Databricks, and Conviva Networks
  • Dylan Patel, Founder, CEO, and Chief Analyst, SemiAnalysis
  • Anush Elangovan, Corporate Vice President of AI Software, AMD
  • Matt White, Executive Director, PyTorch Foundation

See the KEYNOTE SPEAKERS.

VIEW FULL SCHEDULE

Register Now

Buy your pass today.

Learn more about our discounted academic rates available for students and faculty.

Need some financial help to get there? The application deadline for scholarships is coming up August 29. Get details & apply.

REGISTER NOW

Sharpen Your Skills

Boost your brainpower with co-located events on October 21. The Measuring Intelligence Summit and the AI Infra Summit offer focused deep dives into model evaluation and cutting-edge infrastructure.

Co-located events require an additional fee and can be added when you register for PyTorch Conference. Spots are limited so secure yours early!

CO-LOCATED EVENTS

Sponsor the Event

PyTorch is at the forefront of innovation, empowering rapid experimentation, flexible model development, and efficient deployment into production environments with its powerful, versatile ecosystem of tools and thriving community of dedicated users.

As a sponsor, you’ll be in the heart of the AI/ML ecosystem, connecting directly with 2,500+ expert attendees who are driving the next generation of AI advancements.

BECOME A SPONSOR

Nominations are now open for the 2025 PyTorch Contributor Awards! These awards shine a spotlight on the incredible individuals whose work and dedication are driving innovation, collaboration, and community-building within the PyTorch ecosystem.

Whether through code, documentation, mentoring, community leadership, or new ideas that push boundaries, contributors are at the heart of PyTorch’s success. Now is your chance to help us celebrate them.

Submit your nomination today.

The nomination phase runs from July 31st to August 22nd.

Awards Ceremony

Winners will be honored at the PyTorch Conference in San Francisco, October 22–23, 2025.  Each winner will receive a complimentary ticket to attend the conference.

Who Should You Nominate?

Anyone making a meaningful impact in the PyTorch ecosystem! We welcome and encourage self-nominations, and nominations for contributors across all backgrounds, geographies, and roles including:

• Open source developers
• Documentation writers
• Educators and content creators
• Community advocates
• Ecosystem builders
• Bug reporters and fixers
• Longtime contributors and rising newcomers

Award Categories

You’ll be asked to nominate someone for one of the following categories:

• PyTorch Superhero – Excellence in all aspects of community contributions
• PyTorchbearer – Excellence in long-term contributions across all modalities
• PyTorch Pace-Setter – Excellence in high-level activity and contributions
• PyTorch Newcomer – Excellence in new contributions
• PyTorch Ambassador – Excellence in bringing new users to the community
  (Only approved PyTorch Ambassadors are eligible)
• PyTorch Problem-Solver – Excellence in uncovering or resolving bugs
• PyTorch Innovator – Excellence in innovative new features or approaches
• PyTorch Trail Blazer – Excellence in documentation and knowledge sharing
• PyTorch Rock-Turner – Excellence in submitting interesting issues or bugs
• PyTorch Ecosystem Champion – Excellence in strengthening the broader ecosystem

How to Submit a Strong Nomination

Want your nominee to shine? Here’s how:

Be Specific

Describe what they did—not just that they were “great.” Examples matter.

Highlight the Impact

Did their work:

• Improve performance or usability?
• Reach new users or communities?
• Help others adopt or learn PyTorch?

Provide Supporting Evidence

Include links to:

• GitHub issues, PRs, or repos
• Blog posts, talks, or tutorials
• Event listings or documentation sprints

Sample Strong Nomination Statements

• “Led a PyTorch documentation sprint, improving over 200 tutorials to support new users.”
• “Resolved critical bugs impacting model stability in production deployments.”
• “Ran workshops in underserved regions, expanding PyTorch’s reach to new users.”
• “Mentored dozens of first-time contributors through successful PRs and onboarding.”

Celebrating All Forms of Contribution

We welcome nominations from all parts of the community—across genders, geographies, institutions, and contribution types. Contributions may include advocacy, education, bug hunting, outreach, translation, and more.

Questions? Reach out to us at: contributor-award@pytorch.org

Nominate now by visiting the PyTorch Contributor Awards page. 

Let’s recognize the people making PyTorch better for everyone.

First Look at the Future of AI. The #PyTorchConf Schedule Is Here!

The wait is over! The PyTorch Conference schedule is live! Join us October 22–23 in San Francisco for 2⃣ days of cutting-edge sessions, hands-on technical content, and insights from the leaders shaping the future of AI. 

From soon-to-be-announced keynotes to breakout tracks, poster sessions, and the Flare Party, this year’s event delivers hands‑on workshops, real‑world scaling techniques, safety‑focused sessions, benchmark practices, and a startup showcase that empower researchers, developers, and engineers with both knowledge and community connections.

Check out the highlights:

• ExecuTorch 1.0: General Availability Status for Mobile and Embedded Application Developers – Mergen Nachin, Meta
• vLLM: Easy, Fast, and Cheap LLM Serving for Everyone – Simon Mo, vLLM
• Transformers: Standardizing Model Definitions Across the PyTorch Ecosystem – Lysandre Debut, Hugging Face
• Efficient MoE Pre-training at Scale on AMD GPUs With TorchTitan – Liz Li & Yanyuan Qin, AMD; Less Wright, Meta
• Maximizing Luck in Reinforcement Learning – Daniel Han, Unsloth
• Leveraging PyTorch for Generative AI in Distributed Edge Clouds – Tina Tsou, TikTok

Keep an eye out for keynote speaker names announced soon!

VIEW FULL SCHEDULE >>

Today, we stand at a similar inflection point with open AI models. These models, increasingly foundational to research and industry, lack an equivalent licensing standard. Existing open source software licenses weren’t designed with AI models in mind, while most model-specific licenses are either too complex, overly restrictive, or legally ambiguous.

To fully unlock the potential of open AI, we need a license purpose-built for the realities of machine learning. That’s where OpenMDW comes in.

Why AI Models Need a New License

AI models differ fundamentally from traditional software. They are:

• Composites of multiple types of components: including code, architecture, training data, weights, documentation, and evaluation protocols.
• Subject to overlapping IP regimes: such as copyright, database rights, and trade secrets, which vary across jurisdictions.
• Distributed without a consistent definition of “open”: resulting in a fragmented licensing landscape.

This complexity has led to a proliferation of bespoke, incompatible licenses that often:

• Limit redistribution, reuse, or modification.
• Fail to address legal nuances unique to models.
• Create uncertainty for developers and adopters alike.

The result? Friction in open ecosystems, legal ambiguity, and a significant barrier to collaboration and innovation.

The Origins of OpenMDW

OpenMDW,  short for Open Model, Data and Weights License  was born out of the effort to implement the Model Openness Framework (MOF). The MOF is a 3-tier classification system that defines what it means for a model to be truly “open”— not just available with limitations or use restrictions, but licensed openly across its code, architecture, parameters, training data, and documentation.

To make MOF practical, model developers needed a simple, standard license they could drop into any repository,  just like Apache-2.0 or MIT is used in software. Something purpose-built for many types of content including models, not just code.

What Makes OpenMDW Different

OpenMDW is the first truly permissive license designed from the ground up for machine learning models. Here’s what sets it apart:

Covers the Entire Model Stack

It’s designed to apply to all components of a model release:

• Model architecture
• Parameters and checkpoints
• Training and inference code
• Preprocessing and evaluation data
• Documentation (e.g., model cards, data cards)

Importantly, OpenMDW does not require inclusion of all components. It applies only to what is distributed, while remaining compatible with many other licenses that may govern certain parts of the repository.

(OpenMDW users will of course have to continue to comply with any other third-party licenses that apply to other pre-existing materials in their repos, such as by providing license text and notices, source code where applicable, etc.)

Comprehensive and Legally Grounded

OpenMDW grants expansive permissions including under copyright, patent, database, and trade secret law, a broad legal spectrum of rights relevant to AI artifacts.

It also includes:

• A patent litigation termination clauses to deter patent assertions by users of the model’s materials
• Attribution requirements to maintain provenance and trust

Compatible with Policy and Open Source Principles

• Intended to be fully aligned with the EU AI Act’s references to “free and open-source licenses”
• Supports the Open Source Initiative (OSI) 10 principles, including free redistribution, source availability, derived works and no discrimination against persons or groups

Designed for Simplicity

• One license, one file, one place: a LICENSE file at the root of your repo
• No complex licensing matrix: no confusion for downstream users
• Easy integration into any repo: just like MIT or Apache-2.0.

Understanding the OpenMDW License

Definitions and Scope

Model Materials under OpenMDW include:

• Model architecture and trained parameters; and
• all other related materials provided under OpenMDW, which can include:
  • Preprocessing, training and inference code
  • Datasets and evaluation scripts
  • Documentation, metadata, and tools

This comprehensive scope maps directly to the Model Openness Framework (MOF), ensuring that all critical elements of a model are covered if they are included with the distribution.

The Model Materials are not intended to be a requirement of what has to be included in the distribution. It only specifies that what is included in the distribution is covered by the license, and excludes anything covered by other licenses in the distribution.

Grant of Rights

OpenMDW grants broad rights to “deal in the Model Materials without restriction,” including for example:

• Use, modify and distribute the Model Materials
• Operate under copyright, patent, database, and trade secret laws

These rights are granted free of charge, with no field-of-use restrictions,  removing ambiguity for developers and enterprises alike.

Attribution, Not Copyleft

OpenMDW imposes only minimal obligations:

• Retain the license text
• Preserve original copyright and attribution notices

There are no copyleft or share-alike conditions, meaning derivative models and integrations can remain fully permissive. This allows for maximum reuse and interoperability.

Patent Protection

To prevent misuse of the commons, OpenMDW includes a patent-litigation termination clause: if a licensee initiates offensive patent litigation over the Model Materials, their license is revoked.

This mirrors best practices in open source software and helps preserve a collaborative ecosystem.

Outputs Are Unrestricted

A major innovation: outputs generated by using a model under OpenMDW are completely free of licensing restrictions imposed by the provider of the Model Materials.

This eliminates confusion over whether generated text, images, code or predictions are encumbered  by the model provider— a common point of uncertainty in existing licenses.

How to Adopt OpenMDW

Adopting OpenMDW is straightforward:

1. Add the OpenMDW-1.0 license file to your repository: LICENSE
2. Clearly indicate that your release is under OpenMDW-1.0 in the README
3. Ensure all components of the model package are covered and disclosed, including prominently highlighting any components that are subject to other licenses

Why This Matters Now

The AI community is reaching an inflection point. Open models  from AI2’s Molmo to Mistral, and open reasoning models like DeepSeek’s R1 to multimodal agents  are reshaping what’s possible in the open. But their licensing status remains hard to characterize, since software licenses may not map cleanly onto AI models.

Some open weights models which use restrictive licenses have become gradually more permissive; but without a strong legal framework available for licensing, model producers have been forced to err towards the side of caution in designing their own licenses.

In his recent post, Nathan Lambert of AI2 rightly notes: “One of the long standing todo items for open-source AI is better licenses”, OpenMDW helps to fill that need.

Just as Apache-2.0 and MIT became foundational licenses for open source software, OpenMDW is positioned to become the standard for open models. Its clarity, scope, and permissiveness lower barriers for developers and create certainty for companies and researchers looking to build responsibly on open foundations.

This isn’t just about legal clarity,  it’s about enabling an innovation-rich and open source AI ecosystem.

Visit openmdw.ai for more details including the FAQ.

PyTorch Day China 2025, proudly hosted by the PyTorch Foundation, will take place on June 7 in Beijing, China collocated with the BAAI Conference. This will be the second event in the new PyTorch Day series, following the inaugural PyTorch Day France last month in Paris. PyTorch Days are focused on regional communities and provide a forum for sharing technical advances, project updates, and tutorials, and showcasing impactful innovations across research and industry.

PyTorch Day China will highlight cutting-edge tools, frameworks, and practices across the PyTorch ecosystem. The full-day event will feature insightful talks across a multitude of domains and technical discussions on the most cutting-edge and relevant challenges and projects in the open source AI lifecycle. 

PyTorch Day China Featured Sessions:

Running Large Models on Any AI Chip: PyTorch + Open Source Stack (FlagOS)
Yonghua Lin, VP and Chief Engineer, BAAI
A deep dive into architecture-free deployment of large models using FlagOS and PyTorch—part of BAAI’s open source stack for cross-hardware model execution.

torch.accelerator: A Unified Runtime API for Accelerators
Yu Guangye, AI Framework Engineer, Intel
Learn how Intel is helping unify PyTorch’s runtime interface across diverse hardware accelerators, streamlining portable and scalable AI workloads.

vLLM: Easy, Fast, and Cheap LLM Serving for Everyone
Kaichao You, Tsinghua University
Explore the design and performance of vLLM, a popular open source project for efficient inference and serving of large language models.

PyTorch in Production: Boosting LLM Performance on Ascend NPU
Jiawei Li, Huawei
A look at how PyTorch is being deployed in Huawei’s large-scale heterogeneous environments, with a focus on performance tuning and production readiness.

This is just a sample of what PyTorch Day China will offer. To explore the full agenda, visit the BAAI Conference event page.

Whether you’re contributing to the PyTorch ecosystem or deploying it at scale, PyTorch Day China is an opportunity to connect with a growing community and shape the future of AI development.

The PyTorch Foundation is proud to support MLSys 2025 as a Gold Sponsor. Held May 12–15 in Santa Clara, CA, this premier conference sits at the intersection of machine learning and systems, bringing together researchers, engineers, and practitioners pushing the boundaries of scalable AI infrastructure.

Visit the PyTorch Booth
Stop by to connect with the PyTorch Foundation team, including Executive Director Matt White, and contributors from across the ecosystem. Learn more about PyTorch Foundation’s recent expansion into an umbrella foundation and the acceptance of two leading open source AI projects—vLLM and DeepSpeed.

Featured Sessions from the PyTorch Ecosystem

Extreme PyTorch: Inside the Most Demanding ML Workloads—and the Open Challenges in Building AI Agents to Democratize Them
Speaker: Soumith Chintala
Monday, May 12 | 9:30–10:30 a.m. PT | Mission City Ballroom

In this talk, Soumith Chintala will explore how cutting-edge users are pushing PyTorch to its limits, from planetary-scale training on interactive supercomputers to ultra-efficient, real-time inference on exotic hardware. These power users offer a unique window into today’s most demanding ML systems challenges. Chintala will also examine a bold idea that’s top of mind at this conference: using AI agents to automate a large portion of the work these users currently perform. He will outline the open challenges in building such agents and share concrete opportunities for open collaboration toward making SysML AI agents a reality.

An AI Stack: From Scaling AI Workloads to Evaluating LLMs
Speaker: Ion Stoica
Tuesday, May 13 | 10:30–11:30 a.m. PT | Mission City Ballroom

Ion Stoica will discuss how large language models (LLMs) are enabling new applications, intensifying GPU shortages, and raising concerns about output accuracy. He will present several projects developed to address these challenges, focusing on: (i) Ray, a distributed framework for scaling AI workloads; (ii) vLLM and SGLang, two high-throughput inference engines for LLMs; and (iii) Chatbot Arena, a platform for accurate LLM benchmarking. The session will conclude with key lessons learned and directions for future research.

Additional Highlight
PyTorch Foundation Executive Director Matt White will also deliver a lightning talk during a PhD-focused session at the conference on the value of open source AI and the mission and value of the PyTorch Foundation.

We look forward to an engaging week of learning, collaboration, and technical exchange with the systems and ML research communities.

Learn more and register at mlsys.org

The PyTorch Foundation is proud to launch the PyTorch Ambassador Program, an initiative that recognizes and supports individuals who are passionate about building, educating, and advocating for PyTorch in impactful ways.

From organizing local events to mentoring new users, creating technical tutorials, and speaking at global conferences, PyTorch Ambassadors play a critical role in growing and supporting the global PyTorch ecosystem. The first official cohort of Ambassadors will launch in June 2025, with nominations open from May 7 to June 7, 2025.

About the Program

The PyTorch Ambassador Program highlights independent, trusted voices in the PyTorch community. These leaders help others get started with PyTorch, contribute to the ecosystem, and promote its use across industry, academia, and research.

The program is designed to:

• Support local and regional PyTorch communities
  
• Recognize technical contributions and thought leadership
  
• Enable global knowledge sharing and collaboration
  

What PyTorch Ambassadors Do

Ambassadors are active contributors who:

• Organize PyTorch-focused events, both virtual and in-person
  
• Create technical tutorials, blog posts, and videos
  
• Mentor new users and encourage inclusive participation
  
• Represent PyTorch at conferences, meetups, and academic institutions
  

Ambassadors are expected to participate in at least one of these focus areas on a regular basis and commit to a one-year term.

Program Benefits

The Ambassador Program provides a range of resources and opportunities to help community leaders make a lasting impact:

Recognition and Visibility

• Official designation as a PyTorch Ambassador
  
• Featured profile on the PyTorch Foundation website
  
• Promotion through PyTorch social media and communications channels
  

Exclusive Access

• Private collaboration channels with fellow Ambassadors and Foundation staff
  
• Invitations to briefings, workshops, and leadership training
  
• Event planning toolkits and templates
  

Community and Event Support

• Reimbursement for approved community activities and travel
  
• Complimentary admission to PyTorch Conference
  
• PyTorch-branded materials and Ambassador kits
  

Professional Development

• Opportunities to speak at industry and Foundation events
  
• Recognition for top contributors
  
• Networking with machine learning leaders across the globe
  

Nomination Process

Nominations are open now through June 7, 2025. Individuals can nominate themselves or someone else. All applications will be reviewed by the PyTorch Foundation team, and selected Ambassadors will be invited to participate in onboarding and training sessions beginning in June.

To apply, visit the PyTorch Ambassador Program Application Page and click on the button that says Learn More and Apply.

Eligibility and Selection

To be eligible, nominees must:

• Be at least 18 years old
  
• Sign the PyTorch Ambassador Agreement and NDA
  
• Follow the PyTorch Foundation Code of Conduct and Linux Foundation Antitrust Policy
  
• Demonstrate technical knowledge of PyTorch through open source contributions, published content, or community leadership
  
• Commit to participating for a one-year term
  

Ambassador nominations will be evaluated on the following criteria:

• Community impact and engagement
  
• Technical expertise and thought leadership
  
• Consistent activity within the PyTorch ecosystem
  
• Commitment to openness, inclusion, and collaboration
  

A Global Community

The PyTorch Foundation is seeking Ambassadors from all regions to build a globally representative program. Nominees will be asked to share their location to help identify opportunities for regional engagement and support. 

The inaugural cohort of PyTorch Ambassadors will be announced in June 2025. Their stories, events, and contributions will be featured on the PyTorch Foundation website and shared across community channels.

The PyTorch Ambassador Program is an exciting new chapter in our community’s growth. We invite you to join us in building an even more connected, inclusive, and global ecosystem.

Expanded Foundation will Provide a Trusted and Vendor-Neutral Home for High-Impact and Innovative Open Source AI Projects

PyTorch Day France, Paris, France – May 7, 2025 – The PyTorch Foundation, a community-driven hub for open source AI, today announced its expansion into an umbrella foundation. As part of this milestone, two leading open source AI projects—vLLM and DeepSpeed—have been accepted into the foundation by the Technical Advisory Council. This expansion positions the PyTorch Foundation as the trusted home for a broad range of community-driven AI projects spanning the entire AI lifecycle—from training and inference and domain-specific applications to agentic frameworks.

As artificial intelligence becomes a critical driver of global innovation and competitive advantage, enterprises are under pressure to adopt scalable, secure, and future-focused AI solutions. With global GenAI spending forecasted to hit $644 billion this year, and demand growing for open source AI alternatives, the PyTorch Foundation is positioning itself as a vendor-neutral home for trusted and innovative new AI projects. The foundation will support the development of the next generation of open source AI tooling, ensuring interoperability, reducing vendor lock-in, and enabling faster integration of trusted, production-grade technologies. With transparent governance and broad industry collaboration, the PyTorch Foundation is playing a crucial role in shaping the infrastructure enterprises rely on to build and deploy responsible AI at scale.

“This is an exciting new chapter for the PyTorch Foundation and the broader open source AI ecosystem,” said Matt White, Executive Director of the PyTorch Foundation. “By transitioning to an umbrella foundation, we’re not only formalizing the momentum we’ve built across the PyTorch ecosystem—we’re creating space for new projects and innovators to thrive within a vendor-neutral, open governance environment.”

The decision to expand to an umbrella foundation is a natural evolution of the PyTorch Foundation’s rapid growth and global momentum. In just two and a half years, the organization has grown to include over 30 member companies and 120 vibrant ecosystem projects, and PyTorch itself has become the preferred framework for AI research and deployment. The new umbrella structure will support a broader portfolio of high-impact projects, foster deeper collaboration across domains, and help scale innovation throughout the AI lifecycle.

The PyTorch Foundation’s expanded scope allows it to host two new categories of projects:

• Platform Projects – Solutions that support multiple stages of the AI lifecycle, including training, inference, model optimization, deployment, and agentic systems.
• Vertical Projects – Tools tailored for specific industries and applications, such as bioinformatics, geospatial intelligence, and protein folding.

Projects accepted under the PyTorch Foundation benefit from neutral IP governance, strategic support, increased visibility, and a global community of contributors. The PyTorch Foundation distinguishes between ecosystem projects, which remain independently governed, and foundation-hosted projects, which adopt the foundation’s open governance model and receive comprehensive operational support.

The first two projects accepted into the PyTorch Foundation as hosted projects are:

• vLLM – An open and efficient inference engine for large language models. vLLM enables high-throughput, low-latency LLM serving through optimized memory management and scheduling techniques, including PagedAttention. It supports popular model architectures and is designed to maximize hardware utilization, making LLM inference more scalable and cost-effective across a range of deployments. Learn more about the contribution of vLLM to the PyTorch Foundation here.
• DeepSpeed – A distributed training library that simplifies scaling AI workloads. DeepSpeed provides a suite of optimization techniques—such as ZeRO (Zero Redundancy Optimizer), 3D parallelism, and inference acceleration—to enable training of extremely large models efficiently. It is used extensively in both academic research and production environments to push the limits of model size, speed, and efficiency. Learn more about the contribution of DeepSpeed to the PyTorch Foundation here.
  

The PyTorch Foundation is committed to fostering the growth and adoption of open source AI solutions and tooling. Communities interested in joining the PyTorch Foundation’s expanding ecosystem can learn more about the process for becoming a project here.

Supporting Quotes

“AMD has been a consistent supporter of open source software and the community of open source AI projects. We are excited about this expansion of the PyTorch Foundation, which provides a great opportunity for important AI projects to mature in an open and vendor-neutral ecosystem.”

– Ramine Roane, Corporate Vice President of AI Product Management, AMD

“At Arm, we believe collaboration is essential to empower developers and accelerate AI innovation from cloud to edge. The expansion of the PyTorch Foundation is a major milestone for the open source AI community—by providing a trusted home for projects like vLLM and DeepSpeed, the PyTorch Foundation is helping to unlock scalable, efficient AI and we’re proud to support this important evolution.”

– Alex Spinelli, Senior Vice President, AI and Developer Platforms and Services, Arm

“Open source frameworks are essential to advancing AI development, which is why AWS has been committed to the long-term success of the PyTorch ecosystem since its early days and through our continued support of the PyTorch Foundation. Expanding to an umbrella foundation highlights the rapid growth of this community and will make it easier to support a broader portfolio of high-impact projects, foster deeper collaboration across domains, and help scale innovation throughout the AI lifecycle.”

– Brian Granger, Senior Principal Technologist of AI Platforms, Amazon Web Services

“DeepSpeed is delighted to become a hosted Platform project in the PyTorch Foundation. From inception, DeepSpeed has built on PyTorch, with critical dependencies on features such as Module, Tensor, Distributed, and Compiler. We are eager to leverage this closer integration with the PyTorch ecosystem to achieve our goal of providing open and democratized access to state-of-the-art AI technologies for all.“

– Olatunji Ruwase, Project Lead, DeepSpeed

“Google congratulates the PyTorch Foundation on its expansion into an umbrella foundation. This evolution is poised to not only champion important open-source AI projects like vLLM and DeepSpeed, but is also a significant step forward in cultivating deeper collaboration and driving innovation within the AI community. We look forward to continuing to collaborate with the foundation and contributing to the expanded ecosystem.”

– Joe Pamer, Senior Director, ML Frameworks and Compilers, Google

“As a significant contributor to vLLM, DeepSpeed and PyTorch, Huawei welcomes their move to the foundation. We believe the professional services offered under the umbrella model will foster continued growth and value for users and developers.”

– Li Yongle, General Manager of Open Source Development, Huawei’s Computing Product Line

“Super excited for vLLM and DeepSpeed to join the PyTorch Foundation as it becomes an umbrella foundation. These packages are essential tools in the deep learning stack and integrate seamlessly with PyTorch. This is a strategic move that ensures future growth and maintenance for them.”

– Lysandre Debut, Chief Open-Source Officer, Hugging Face

“As a pivotal member of the PyTorch community for years, IBM applauds the expansion of the PyTorch Foundation to an umbrella foundation. This shift provides opportunities to support projects such as vLLM and others across the entire AI model lifecycle, from training to tuning to inference. An umbrella organization structure will support new workstreams underpinned by essential AI governance principles, accelerating performance in a new era of open, responsible AI.”

– Sriram Raghavan, VP, IBM Research AI

“As a premier member of the PyTorch Foundation, Intel is excited about the foundation’s expansion into an umbrella model. This strategy empowers developers with essential resources and support, enabling them to create innovative, community-driven AI projects that tackle real-world challenges.”

– Kismat Singh, VP, Engineering for AI Frameworks, Intel Corporation

“PyTorch sits at the very core of AI today. Meanwhile, the depth of the AI stack has grown dramatically—evolving from enabling accelerated compute to powering fully autonomous systems. Broadening the PyTorch Foundation is a key step in keeping the AI revolution open and accessible to all, across the stack and aligned with the principles PyTorch was built on.”

– Luca Antiga, CTO, Lightning AI

“Today PyTorch plays such a fundamental role in the AI space underpinning Llama, ChatGPT and so many other influential projects. This move to create an umbrella foundation enables PyTorch to significantly expand its ecosystem both horizontally and vertically in this era of agentic systems. I really believe this will usher in a new wave of innovation, and I’m especially excited about vLLM and DeepSpeed joining. These projects have a strong history of being critical to AI’s advances and it’s exciting that we are joining forces to grow this amazing community!”

– Joe Spisak, Product Director for PyTorch, Meta

“The PyTorch Foundation plays a vital role in advancing the PyTorch ecosystem by driving innovation, supporting education, and fostering community collaboration. Its expansion to an umbrella foundation helps ensure the long-term success of open source tools and creates the conditions necessary to welcome new projects that are essential to the future of open source AI.”

– Ujval Kapasi, VP of Deep Learning Software, NVIDIA

“At Snowflake, we believe that empowering the AI community is fundamental, and strengthening the open, vendor-neutral foundation around pivotal projects like these is crucial for progress. It’s truly exciting to witness the PyTorch Foundation evolve into an umbrella organization and welcome essential projects like DeepSpeed and vLLM. Having been part of the PyTorch ecosystem, I deeply appreciate the significance of this strategic move. We eagerly anticipate the accelerated innovation this closer collaboration within the PyTorch Foundation will bring.”

– Dwarak Rajagopal, VP of AI Engineering and Research, Snowflake

“We’re excited that vLLM is one of the first Platform Projects joining the PyTorch Foundation. vLLM is built on top of PyTorch with deep integration such as Torch Compile and multi-hardware support. We look forward to further collaborating with the ecosystem that powers innovations in open source and vendor neural technologies for AI.“

– Simon Mo, Project Co-Lead, vLLM

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About the PyTorch Foundation

The PyTorch Foundation is a community-driven hub supporting the open source PyTorch framework and a broader portfolio of innovative open source AI projects. Hosted by the Linux Foundation, the PyTorch Foundation provides a vendor-neutral, trusted home for collaboration across the AI lifecycle—from model training and inference, to domain-specific applications. Through open governance, strategic support, and a global contributor community, the PyTorch Foundation empowers developers, researchers, and enterprises to build and deploy responsible AI at scale. Learn more at https://pytorch.org/foundation. 

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page. Linux is a registered trademark of Linus Torvalds.

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Natasha Woods
The Linux Foundation
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