The Linux Foundation | 22 October 2025
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Ray joins leading open source AI projects including PyTorch and vLLM to minimize AI computing complexity and speed production
- The PyTorch Foundation has welcomed Ray as its newest foundation-hosted project.
- This news comes alongside sustained investment in AI programs across the industry, elevating the importance of speed and efficiency as businesses across verticals are building and scaling AI programs
- Engineering teams building AI with Ray can seamlessly move workloads from a single machine to thousands of nodes without distributed systems complexity, saving time and removing the need for disparate systems and solutions.
- Ray has already been adopted widely with 237 million downloads to date and, as part of the PyTorch Foundation, it completes a critical layer in the open source AI stack, alongside vLLM and PyTorch.
SAN FRANCISCO – PyTorch Conference – October 22, 2025 — The PyTorch Foundation, a community-driven hub for open source AI under the Linux Foundation, today announced that it has welcomed Ray as its newest foundation-hosted project. Originally developed by Anyscale, Ray is an open source distributed computing framework for AI workloads, including data processing, model training and inference at scale.
In the race to make AI a key differentiator, engineering teams are often hampered by complexity and wasted compute due to hand-built, fragmented systems, steepening the path to production. Created to eliminate distributed computing bottlenecks, which slow down AI innovation, Ray complements PyTorch and vLLM and offers teams a seamless way to execute data processing, model training, and serving workloads from a single machine to thousands of nodes. Since its development at UC Berkeley, Ray now has over 39,000 GitHub stars and more than 237 million downloads.
“The PyTorch Foundation is committed to fostering an open, interoperable, and production-ready AI ecosystem,” said Matt White, GM of AI at the Linux Foundation and Executive Director of the PyTorch Foundation. “By bringing Ray under the PyTorch Foundation umbrella, alongside projects like vLLM and DeepSpeed, we are uniting the critical components needed to build next-generation AI systems. Ray’s inclusion strengthens our collective mission to support developers with the tools to efficiently train, serve, and deploy AI models at scale.”
Figure 1: Ray within the AI Compute Software Stack.
Ray addresses the unique computational demands of modern AI by providing a compute framework that executes distributed workloads including:
- Multimodal data processing: handles massive, diverse datasets (text, images, audio, video) in parallel, with efficiency.
- Pre-training and post-tuning: scales PyTorch and other ML frameworks across thousands of GPUs for both pre-training and post-training tasks.
- Distributed inference: serves models in production with high throughput and low latency, orchestrating bursts of dynamic, heterogeneous workloads across clusters.
By contributing Ray to the PyTorch Foundation, Anyscale reinforces its commitment to open governance and long-term sustainability for Ray and open source AI.
"With Ray, our goal is to make distributed computing as straightforward as writing Python code," said Robert Nishihara, co-founder of Anyscale. "Joining the PyTorch Foundation helps us stay true to that mission, ensuring Ray continues to be an open, community-driven backbone for developers and their organizations."
With PyTorch for model development, vLLM for inference, and Ray for distributed execution, these projects now form an integrated open source foundation for AI. These unified layers allow teams to build and scale applications more efficiently without fighting disparate infrastructure or being locked into proprietary tools.
Developers and contributors that are interested in participating in the project are encouraged to attend Ray Summit 2025 in San Francisco from November 3-5, 2025. To follow Ray’s development, and join the growing ecosystem, visit https://github.com/ray-project/ray, or join the Ray community on Slack.
Supporting Quotes
“Ray and Kubernetes are naturally complementary in an open source AI stack — combining Kubernetes’ orchestration strengths with Ray’s distributed compute model gives the community a powerful foundation for scaling next-generation AI systems. We are thrilled to see Ray join the PyTorch Foundation and hope for further technical collaboration with CNCF projects.”
– Chris Aniszczyk, Chief Technology Officer, Cloud Native Computing Foundation (CNCF)
“Ray has become a core part of our AI platform at Uber, powering large-scale model training, hyperparameter tuning, and distributed data processing. We’re excited to see Ray join the Linux Foundation, ensuring its continued growth under open governance and enabling even deeper collaboration on next-generation distributed computing.”
– Zhitao Li, Director of Engineering, Uber
“Ray joining the PyTorch Foundation is a significant milestone for open source AI. With PyTorch, vLLM, and Ray working side by side under one neutral home, developers can have a single source for a unified, community-driven compute stack that can scale from strategy to production, without the friction of proprietary systems. We are enthusiastic about what’s ahead for the foundation and are excited to bring our community a hub of flexible, fast, and open AI infrastructure.”
– Joe Spisak, Board Member of the PyTorch Foundation and Director of Product Management, Meta Super Intelligence Labs
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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 AI at scale. Learn more at https://pytorch.org/foundation.
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