You may already be aware that we plan to retire Intel® Extension for PyTorch* soon. This was announced in the Intel® Extension for PyTorch* 2.8 release notes and also highlighted in community ticket #867.

We launched the Intel® Extension for PyTorch* in 2020 with the goal of extending the official PyTorch* to simplify achieving high performance on Intel® CPU and GPU platforms. Over the years, we have successfully upstreamed most of our features and optimizations for Intel® platforms into PyTorch* itself. As a result, we have discontinued active development of the Intel® Extension for PyTorch* and ceased official quarterly releases following the 2.8 release. We strongly recommend using PyTorch* directly going forward, as we remain committed to delivering robust support and performance with PyTorch* for Intel® CPU and GPU platforms.

We will continue to provide critical bug fixes and security patches for two additional quarters to ensure a smooth transition for our partners and the broader community. After that, we plan to mark the project End-of-Life unless there is a solid need to continue maintenance. Concretely, this means:

• We will continue to provide critical bug fixes and security patches in the main branches of Intel® Extension for PyTorch*: CPU (main) and GPU (xpu-main).
• We have stopped official quarterly releases. We will not create release branches or publish official binary wheels for Intel® Extension for PyTorch*.
• We will maintain Intel® Extension for PyTorch* as an open source project until the end of March 2026, to allow projects which depend on Intel® Extension for PyTorch* to completely remove the dependency.

Thank you all for your continued support! Let’s keep the momentum going together!

CPU 💻main branch   |   🌱Quick Start   |   📖Documentations   |   🏃Installation   |   💻LLM Example
GPU 💻main branch   |   🌱Quick Start   |   📖Documentations   |   🏃Installation   |   💻LLM Example

Intel® Extension for PyTorch* extends PyTorch* with up-to-date features optimizations for an extra performance boost on Intel hardware. Optimizations take advantage of AVX-512 Vector Neural Network Instructions (AVX512 VNNI) and Intel® Advanced Matrix Extensions (Intel® AMX) on Intel CPUs as well as Intel X^e Matrix Extensions (XMX) AI engines on Intel discrete GPUs. Moreover, through PyTorch* xpu device, Intel® Extension for PyTorch* provides easy GPU acceleration for Intel discrete GPUs with PyTorch*.

Intel® Extension for PyTorch* provides optimizations for both eager mode and graph mode, however, compared to eager mode, graph mode in PyTorch* normally yields better performance from optimization techniques, such as operation fusion. Intel® Extension for PyTorch* amplifies them with more comprehensive graph optimizations. Therefore we recommend you to take advantage of Intel® Extension for PyTorch* with TorchScript whenever your workload supports it. You could choose to run with torch.jit.trace() function or torch.jit.script() function, but based on our evaluation, torch.jit.trace() supports more workloads so we recommend you to use torch.jit.trace() as your first choice.

The extension can be loaded as a Python module for Python programs or linked as a C++ library for C++ programs. In Python scripts users can enable it dynamically by importing intel_extension_for_pytorch.

• Check CPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® CPUs. Source code is available at the main branch.
• Check GPU tutorial for detailed information of Intel® Extension for PyTorch* for Intel® GPUs. Source code is available at the xpu-main branch.

In the current technological landscape, Generative AI (GenAI) workloads and models have gained widespread attention and popularity. Large Language Models (LLMs) have emerged as the dominant models driving these GenAI applications. Starting from 2.1.0, specific optimizations for certain LLM models are introduced in the Intel® Extension for PyTorch*. Check LLM optimizations CPU and LLM optimizations GPU for details.

Note: Intel® Data Center Max 1550 GPU: support all the models in the model list above. Intel® Core™ Ultra Processors with Intel® Arc™ Graphics: support Llama 2 7B, Llama 3 8B and Phi-3-Mini 3.8B.

• ✅ signifies that it is supported.

• A blank signifies that it is not supported yet.

• 1: signifies that Llama-2-7b-hf is verified.

• 2: signifies that Meta-Llama-3-8B is verified.

• 3: signifies that Phi-3-mini-4k-instruct is verified.

Above models are intended to allow users to examine and evaluate models and the associated performance of Intel technology solutions. The accuracy of computer models is a function of the relation between the data used to train them and the data that the models encounter after deployment. Models have been tested using datasets that may or may not be sufficient for use in production applications. Accordingly, while the model may serve as a strong foundation, Intel recommends and requests that those models be tested against data the models are likely to encounter in specific deployments.

Intel is committed to respecting human rights and avoiding causing or contributing to adverse impacts on human rights. See Intel’s Global Human Rights Principles. Intel’s products and software are intended only to be used in applications that do not cause or contribute to adverse impacts on human rights.

The team tracks bugs and enhancement requests using GitHub issues. Before submitting a suggestion or bug report, search the existing GitHub issues to see if your issue has already been reported.

Apache License, Version 2.0. As found in LICENSE file.

See Intel's Security Center for information on how to report a potential security issue or vulnerability.

See also: Security Policy