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PyTorch is an open-source library for tensor computation and deep learning on CPUs and GPUs. It supplies tools developers use to build and train models; it is not itself a model or a cloud-computing service. Separately, the PyTorch Foundation supports PyTorch and other open-source AI projects, while PyTorch maintainers continue to define the framework’s technical governance.
What PyTorch is used for
PyTorch gives developers tensor operations and deep-learning building blocks that can run on CPUs and GPUs. In practice, those tools are used to construct and train neural networks and to incorporate them into larger Python programs. The framework provides the machinery; developers choose or build the model and supply the data and compute environment.
The current PyTorch documentation describes it as an optimized tensor library for deep learning using GPUs and CPUs. Its documentation distinguishes stable APIs, which are intended to be maintained with backward-compatibility expectations, from unstable features that remain in development and may change. Check the live documentation when relying on a particular API or release.
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PyTorch’s design philosophy puts usability first and reasonable performance second. The official PyTorch Design Philosophy states, “PyTorch’s primary goal is usability,” and adds, “A secondary goal is to have reasonable performance.” These are project priorities, not a claim that PyTorch is the fastest choice for every workload.
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The project favors flexibility and integration with ordinary Python rather than imposing static-shape or graph-only constraints without a clear benefit. Its preference for explicit building blocks—such as visible movement of data or models between devices—can make program behavior easier to follow and errors easier to debug. That flexibility can be valuable during experimentation, while performance, deployment, hardware support, and interoperability still need to be evaluated for the specific application.
The 2019 PyTorch paper offers historical context for this approach: it describes an imperative, Pythonic programming style, user control within regular Python programs, debugging, and support for hardware accelerators including GPUs. It is useful for understanding the framework’s design lineage, not as documentation of current release features: PyTorch: An Imperative Style, High-Performance Deep Learning Library.
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How the PyTorch Foundation fits in
The PyTorch Foundation is hosted by the Linux Foundation. Its purpose is to support open-source, open-standards, and other open technical projects in the broader PyTorch community. The Foundation is an organizational steward; it is not the software framework itself.
The distinction between organizational and technical governance matters. The Foundation charter, amended January 1, 2026, says the Foundation’s Governing Board manages the organization and its Technical Advisory Council (TAC) coordinates collaboration and communication among technical projects. PyTorch’s maintainers define the framework’s technical governance. The Foundation’s overview and charter describe these roles.
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Hosted projects and the wider ecosystem
On May 7, 2025, the Foundation announced that it was expanding into an umbrella foundation and that vLLM and DeepSpeed had been accepted as its initial hosted projects. Hosted projects adopt the Foundation’s open governance model and receive operational support; ecosystem projects remain independently governed. The announcement describes vLLM as an inference engine and DeepSpeed as a distributed training library. The Foundation’s announcement explains the distinction.
The Foundation overview lists PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors among the projects it supports. That roster can change, so consult the current project overview for the latest membership. Support by the Foundation does not mean every listed project shares the same governance arrangement.
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What the Foundation reported for 2025
In its January 15, 2026 review, the PyTorch Foundation reported the following activity for 2025:
- Three new foundation-hosted projects and 14 ecosystem projects.
- Sixteen new members.
- Support for PyTorch releases 2.6, 2.7, 2.8, and 2.9.
- More than 3,400 attendees at what it described as its largest PyTorch Conference to date.
These are figures reported by the Foundation about its own organizational activity. They are not independent measures of PyTorch’s market share, performance, or project quality. In the same review, PyTorch TAC Chair Luca Antiga described his perspective: “I view PyTorch’s evolution as a fundamental shift from being the industry’s leading research framework to becoming the connective foundation of a fully open, production-grade AI ecosystem.” That is Antiga’s characterization, not an independently established ranking. See the Foundation’s 2025 review.
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How to get started with PyTorch
Start with the official installation selector rather than copying a single command: the appropriate setup depends on your operating system, package manager, compute platform, and accelerator. PyTorch’s documentation provides a local installation selector, the “Learn the Basics” guide, and a broader collection of tutorials. A book or hands-on deep-learning textbook can be a useful optional companion, but the official documentation and tutorials are available directly online.
How to decide whether PyTorch suits a project
There is no workload-independent winner implied by PyTorch’s design goals. Before choosing a framework, assess the project against the requirements that affect daily development and deployment:
- Programming and debugging: Consider whether a Python-integrated, explicit workflow fits how the team experiments and diagnoses issues.
- Hardware: Confirm support for the CPU, GPU, or other compute platform the project must use, including the intended installation and deployment environment.
- Execution model: Determine whether flexible execution fits the work or whether a compiler- or graph-oriented workflow is a firm requirement.
- Deployment and interoperability: Check how the chosen framework connects to the model-serving, export, and surrounding software systems the project needs.
- Ecosystem and stewardship: Evaluate relevant libraries, learning resources, contributor communities, and the governance model for the framework or projects on which the application depends.
PyTorch’s own documentation establishes its supported features and stated design priorities; it does not establish a workload-matched benchmark against other frameworks. Measure the actual workload and verify the relevant deployment path before making a performance-based choice.
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