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href="https://github.com/pytorch/pytorch" title="Go to PyTorch GitHub"> <div id="topnav-gh-icon"></div> </a> </li> <li class="navSearchWrapper reactNavSearchWrapper" key="search"> <div class="search-border"> <div id="search-icon"></div> <input id="search-input" type="text" title="Search" /> <div id="close-search">X</div> </div> </li> </ul> </div> <script src="/assets/main-menu-dropdown.js"></script> <a class="main-menu-open-button" href="#" data-behavior="open-mobile-menu"></a> </div> </div> </div> <div class="main-background blog-background"></div> <div class="jumbotron jumbotron-fluid"> <div class="container"> <p class="featured-post">Featured Post</p> <h1 class="blog-index-title"> <a href="/blog/pytorch2-6/">PyTorch 2.6 Release Blog</a> </h1> <p class="featured-blog-preview">We are excited to announce the release of PyTorch庐 2.6 (release notes)! This release features mul...</p> <a href="/blog/pytorch2-6/" class="btn btn-lg with-right-arrow"> Read More </a> </div> </div> <div class="main-content-wrapper"> <div class="main-content"> <div class="container"> <div class="row blog-vertical"> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 28, 2025</p> <h4> <a href="/blog/2025-priorities-for-tac/">2025 Priorities for the PyTorch Technical Advisory Council (TAC)</a> </h4> <p> </p> <img src="" class="img-fluid"> </div> <a href="/blog/2025-priorities-for-tac/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 24, 2025</p> <h4> <a href="/blog/how-intel-uses-pytorch-to-empower-generative-ai-through-intel-arc-gpus/">How Intel Uses PyTorch to Empower Generative AI through Intel Arc GPUs</a> </h4> <p>Intel has long been at the forefront of technological innovation, and its recent venture into Generative AI (GenAI) solutions is no exception. With the rise of AI-powered gaming experiences, Intel sought to deliver an accessible and intuitive GenAI inferencing solution tailored for AI PCs powered by Intel鈥檚 latest GPUs. By leveraging PyTorch as the backbone for development efforts, Intel successfully launched AI Playground, an open source application that showcases advanced GenAI workloads. </p> <img src="" class="img-fluid"> </div> <a href="/blog/how-intel-uses-pytorch-to-empower-generative-ai-through-intel-arc-gpus/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 21, 2025</p> <h4> <a href="/blog/accelerating-llm-inference/">Accelerating LLM Inference with GemLite, TorchAO and SGLang</a> </h4> <p>Large Language Models (LLMs) are typically very resource-intensive, requiring significant amounts of memory, compute and power to operate effectively. Quantization provides a solution by reducing weights and activations from 16 bit floats to lower bitrates (e.g., 8 bit, 4 bit, 2 bit), achieving significant speedup and memory savings and also enables support for larger batch sizes. </p> <img src="" class="img-fluid"> </div> <a href="/blog/accelerating-llm-inference/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 14, 2025</p> <h4> <a href="/blog/genai-acceleration-intel-xeon/">GenAI Acceleration for PyTorch 2.5 on Intel庐 Xeon庐Processors</a> </h4> <p>This blog is the fifth in a series focused on accelerating generative AI models with pure, native PyTorch. We demonstrate the GenAI acceleration of GPTFast, Segment Anything Fast, and Diffusion Fast on Intel庐 Xeon庐Processors. </p> <img src="" class="img-fluid"> </div> <a href="/blog/genai-acceleration-intel-xeon/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 09, 2025</p> <h4> <a href="/blog/ascend-backend-w-torchtune/">Integrating Ascend Backend with Torchtune through PyTorch Multi-Device Support</a> </h4> <p>In this blog, we will briefly introduce torchtune, the Ascend backend, and demonstrate how torchtune can be used to fine-tune models with Ascend. </p> <img src="" class="img-fluid"> </div> <a href="/blog/ascend-backend-w-torchtune/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">January 06, 2025</p> <h4> <a href="/blog/hi-po-low-bit-operators/">High-Performance Low-Bit Operators for PyTorch</a> </h4> <p>We are excited to announce the addition of embedding operators with low-bit weights (1-8 bit) and linear operators with 8-bit dynamically quantized activations and low-bit weights (1-8 bit) for Arm CPUs in TorchAO, PyTorch鈥檚 native low-precision library. These operators work seamlessly across all PyTorch surfaces, including eager, torch.compile, AOTI, and ExecuTorch, and are available to use in torchchat. </p> <img src="" class="img-fluid"> </div> <a href="/blog/hi-po-low-bit-operators/" class="btn btn-lg with-right-arrow"> Read More </a> </div> <div class="vertical-blog-container"> <div class="col-md-4"> <p class="blog-date">December 23, 2024</p> <h4> <a href="/blog/2024-year-in-review/">PyTorch Grows as the Dominant Open Source Framework for AI and ML: 2024 Year in Review</a> </h4> <p>This past year was a monumental year for PyTorch from major releases to the flagship PyTorch Conference. We鈥檝e seen incredible growth in contributions from more than 3,500 individuals and 3,000 organizations. It鈥檚 safe to say PyTorch has now become the dominant deep learning framework for AI/ML. PyTorch leads the model training space with a 63% adoption rate according to the recent Shaping the Future of Generative AI Report from the Linux Foundation. </p> <img src="" class="img-fluid"> </div> <a href="/blog/2024-year-in-review/" class="btn btn-lg with-right-arrow"> Read More </a> </div> </div> <div class="d-flex justify-content-center"> <ul class="pagination"> <li class="page-item"> <a class="page-link" href="/blog/">Previous</a> </li> <li class="page-item"><a class="page-link" href="/blog/3/">Next</a> </li> </ul> </div> </div> </div> </div> <div class="container-fluid quick-start-module quick-starts"> <div class="container"> <div class="row"> <div class="col-md-8 start-locally-col"> <h3>Install PyTorch</h3> <p>Select your preferences and run the install command. Stable represents the most currently tested and supported version of PyTorch. This should be suitable for many users. Preview is available if you want the latest, not fully tested and supported, builds that are generated nightly. Please ensure that you have <b>met the prerequisites below (e.g., numpy)</b>, depending on your package manager. You can also <a href="/get-started/previous-versions">install previous versions of PyTorch</a>. Note that LibTorch is only available for C++. </p> <p><b>NOTE:</b> Latest PyTorch requires Python 3.9 or later.</p> <div class="row"> <div class="col-md-3 headings"> <div class="col-md-12 title-block"> <div class="option-text">PyTorch Build</div> </div> <div class="col-md-12 title-block"> <div class="option-text os-text">Your OS</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Package</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Language</div> </div> <div class="col-md-12 title-block"> <div class="option-text">Compute Platform</div> </div> <div class="col-md-12 title-block command-block"> <div class="option-text command-text">Run this Command:</div> </div> </div> <div class="col-md-9"> <div class="row ptbuild"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">PyTorch Build</div> </div> <div class="col-md-6 option block version selected" id="stable"> <div class="option-text">Stable (1.13.0)</div> </div> <div class="col-md-6 option block version" id="preview"> <div class="option-text">Preview (Nightly)</div> </div> </div> <div class="row os"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Your OS</div> </div> <div class="col-md-4 option block" id="linux"> <div class="option-text">Linux</div> </div> <div class="col-md-4 option block" id="macos"> <div class="option-text">Mac</div> </div> <div class="col-md-4 option block" id="windows"> <div class="option-text">Windows</div> </div> </div> <div class="row package"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Package</div> </div> <div class="col-md-3 option block" id="conda"> <div class="option-text">Conda</div> </div> <div class="col-md-3 option block selected" id="pip"> <div class="option-text">Pip</div> </div> <div class="col-md-3 option block" id="libtorch"> <div class="option-text">LibTorch</div> </div> <div class="col-md-3 option block" id="source"> <div class="option-text">Source</div> </div> </div> <div class="row language"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Language</div> </div> <div class="col-md-6 option block version selected" id="python"> <div class="option-text">Python</div> </div> <div class="col-md-6 option block" id="cplusplus"> <div class="option-text">C++ / Java</div> </div> </div> <div class="row cuda"> <div class="col-md-12 title-block mobile-heading"> <div class="option-text">Compute Platform</div> </div> <div class="col-md-2 option block version" id="cuda.x"> <div class="option-text">CUDA 11.8</div> </div> <div class="col-md-2 option block version" id="cuda.y"> <div class="option-text">CUDA 12.1</div> </div> <div class="col-md-2 option block version" id="cuda.z"> <div class="option-text">CUDA 12.4</div> </div> <div class="col-md-3 option block version" id="rocm5.x"> <div class="option-text">ROCm 5.2</div> </div> <div class="col-md-3 option block version" id="accnone"> <div 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