PyTorch Foundation Integrates Ray, a Distributed Computing Framework, to Build a Unified AI Infrastructure Stack

The open source AI ecosystem took a decisive leap forward today as the PyTorch Foundation announced that Ray, the distributed computing framework originally developed by Anyscale, has officially joined its ranks. The move marks a significant step toward a unified, interoperable, and production-ready AI compute stack—one that ties together the foundational layers of model development (PyTorch), distributed inference (vLLM), and large-scale execution (Ray).

A Unified Foundation for Open Source AI

Hosted under the Linux Foundation, the PyTorch Foundation acts as a central hub for some of the most important open source AI technologies. Its mission is to reduce fragmentation and foster collaboration across every phase of AI development. By integrating Ray alongside PyTorch and vLLM, the foundation is delivering what the industry has long needed—a cohesive, end-to-end stack for building, training, and deploying AI at scale.

Ray’s inclusion also represents the culmination of years of academic and industrial evolution. Born at UC Berkeley’s RISELab, Ray was designed to simplify distributed computing for AI and machine learning workloads. It enables developers to scale jobs seamlessly from a single laptop to thousands of machines without rewriting code or managing complex systems. As of today, Ray boasts more than 39,000 GitHub stars and over 214 million downloads, making it one of the most widely adopted distributed computing frameworks in the world.

How Ray Complements PyTorch and vLLM

Ray sits between the training and inference frameworks (such as PyTorch, DeepSpeed, and vLLM) and the container orchestration layer (like Kubernetes or Slurm). This position allows Ray to coordinate distributed workloads efficiently while bridging the gap between model training and production-scale deployment.

Ray’s key capabilities include:

• Multimodal data processing: Handles massive, diverse datasets—text, images, audio, and video—in parallel, maximizing throughput and efficiency.
• Pre-training and post-tuning: Scales PyTorch and other frameworks across thousands of GPUs for both pre-training and fine-tuning tasks.
• Distributed inference: Deploys models in production with high throughput and low latency, dynamically managing workload bursts across heterogeneous clusters.

Together, these functions make Ray the “glue” that binds together model creation, optimization, and serving, effectively forming the distributed compute engine layer of modern AI infrastructure.

What This Means for Developers and Enterprises

In today’s AI-driven economy, organizations face immense challenges around scaling, vendor lock-in, and compute inefficiency. Proprietary systems often fragment workflows and slow innovation. With Ray joining the PyTorch Foundation, developers gain a fully open source, interoperable compute stack that eliminates many of these pain points.

As Matt White, GM of AI at the Linux Foundation, explained, this collaboration “unites the critical components needed to build next-generation AI systems.” The unification allows teams to develop advanced AI systems—ranging from large language models to multimodal applications—without relying on closed, proprietary infrastructure. Instead, developers can train and deploy AI models using an ecosystem that’s scalable, modular, and community-driven.

The Broader Implications for Open Source AI

The collaboration between PyTorch, vLLM, and Ray points toward a new era of open compute interoperability. With the Linux Foundation providing neutral governance, the AI industry gains a sustainable model for developing shared infrastructure—similar to how Kubernetes standardized cloud orchestration.

Industry leaders echoed this sentiment. Chris Aniszczyk of the Cloud Native Computing Foundation noted that “Ray and Kubernetes are naturally complementary,” combining orchestration and distributed computing strengths to power next-generation AI systems. Uber’s Director of Engineering, Zhitao Li, added that Ray is already a “core part” of their AI platform, powering large-scale training and data processing. And Meta’s Joe Spisak, a PyTorch Foundation board member, called Ray’s addition a “significant milestone for open source AI,” emphasizing how it creates a unified, community-driven compute stack.

Looking Ahead

Anyscale’s co-founder Robert Nishihara summarized the milestone succinctly:

“Our goal is to make distributed computing as straightforward as writing Python code. Joining the PyTorch Foundation ensures Ray continues to be an open, community-driven backbone for developers.”

Developers and contributors can engage with the project via the Ray GitHub repository or attend Ray Summit 2025 in San Francisco this November, where the community will further explore what this new open source foundation means for the future of AI scalability and accessibility.

In essence, the addition of Ray completes the missing layer in the open source AI ecosystem—bringing together modeling, inference, and distributed execution under one foundation. It’s a pivotal step toward a future where AI infrastructure is not only more powerful but also more open, efficient, and developer-friendly.