Merge branch 'site' into fix-previous-versions

Author: Tony-Y

_community_stories/56.md

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+---
+title: 'How Intel Uses PyTorch to Empower Generative AI through Intel Arc GPUs'
+ext_url: /blog/how-intel-uses-pytorch-to-empower-generative-ai-through-intel-arc-gpus/
+date: Jan 24, 2025
+tags: ["Gaming"]
+---
+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’s 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.
+

_posts/2025-01-24-how-intel-uses-pytorch-to-empower-generative-ai-through-intel-arc-gpus.md

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+---
+layout: blog_detail
+title: "How Intel Uses PyTorch to Empower Generative AI through Intel Arc GPUs"
+author: "Team PyTorch" 
+---
+
+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’s 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.
+
+**The Business Challenge**
+
+Our goal was to deliver an accessible and intuitive GenAI inferencing solution tailored for AI PCs powered by Intel. We recognized the need to showcase the capabilities of the latest GenAI workloads on our newest line of client GPUs. To address this, we developed a starter application, [AI Playground](https://github.com/intel/ai-playground), which is open source and includes a comprehensive developer reference sample available on GitHub using PyTorch. This application seamlessly integrates image generation, image enhancement, and chatbot functionalities, using retrieval-augmented generation (RAG) features, all within a single, user-friendly installation package. This initiative not only demonstrates the functionality of these AI workloads but also serves as an educational resource for the ecosystem, guiding developers on effectively leveraging the [Intel® Arc™ GPU](https://www.intel.com/content/www/us/en/products/details/discrete-gpus/arc.html) product line for advanced AI applications. This solution leverages Intel® Arc™ Xe Cores and [Xe Matrix Extensions (XMX)](https://www.intel.com/content/www/us/en/support/articles/000091112/graphics.html) for accelerating inferencing.
+
+![AI Playground](/assets/images/intel-case-study/fg1.png){:style="width:100%"}
+
+**How Intel Used PyTorch**
+
+PyTorch is the core AI framework for AI Playground. We extensively leverage PyTorch's eager mode, which aligns perfectly with the dynamic and iterative nature of our generative models. This approach not only enhances our development workflow but also enables us to rapidly prototype and iterate on advanced AI features. By harnessing PyTorch’s powerful capabilities, we have created a robust reference sample that showcases the potential of GenAI on Intel GPUs in one cohesive application. 
+
+**Solving AI Challenges with PyTorch**
+
+PyTorch has been instrumental in addressing our AI challenges by providing a robust training and inference framework optimized for discrete and integrated Intel Arc GPU product lines. Choosing PyTorch over alternative frameworks or APIs was crucial. Other options would have necessitated additional custom development or one-off solutions, which could have significantly slowed our time to market and limited our feature set. With PyTorch, we leveraged its flexibility and ease of use, allowing our team to focus on innovation through experimentation, rather than infrastructure. The integration of [Intel® Extension for PyTorch](https://www.intel.com/content/www/us/en/developer/tools/oneapi/optimization-for-pytorch.html#gs.j6azz7) further enhanced performance by optimizing computational efficiency and enabling seamless scaling on Intel hardware, ensuring that our application ran faster and more efficiently.
+
+**A Word from Intel**
+
+*With PyTorch as the backbone of our AI Playground project, we achieved rapid development cycles that significantly accelerated our time to market. This flexibility enabled us to iteratively enhance features and effectively align with the commitments of our hardware launches in 2024\.*
+
+*\-Bob Duffy, AI Playground Product Manager*
+
+![PyTorch Case Stidu](/assets/images/intel-case-study/fg2.png){:style="width:100%"}
+
+**The Benefits of Using PyTorch**
+
+The biggest benefit of using PyTorch for us is the large PyTorch ecosystem, which connects us with an active and cooperative community of developers. This collaboration has facilitated the seamless deployment of key features from existing open source projects, allowing us to integrate the latest GenAI capabilities into AI Playground. Remarkably, we accomplished this with minimal re-coding, ensuring that these advanced features are readily accessible on Intel Arc GPUs.
+
+**Learn More**
+
+For more information about Intel’s AI Playground and collaboration with PyTorch, visit the following links:
+
+* [PyTorch Optimizations from Intel](https://www.intel.com/content/www/us/en/developer/tools/oneapi/optimization-for-pytorch.html#gs.j8h6mc)  
+* [AI Playground GitHub](https://github.com/intel/ai-playground)   
+* [AI Playground](https://intel.com/ai-playground)   
+* [AI Playground Deep Dive Video](https://youtu.be/cYPZye1MC6U)  
+* [Intel GPU Support Now Available in PyTorch 2.5](https://pytorch.org/blog/intel-gpu-support-pytorch-2-5/)

assets/images/intel-case-study/fg1.png

@@ -102,6 +102,13 @@ <h4>ExecuTorch</h4>
             </p>
           </a>
         </div>
+        <div class="col-sm-5 domain-card">
+          <a href="https://pytorch.org/ao/stable/index.html">
+            <h4>torchao</h4>
+            <p>torchao is a library for custom data types and optimizations. Quantize and sparsify weights, gradients, optimizers and activations for inference and training using native PyTorch.
+            </p>
+          </a>
+        </div>
       </div>
     </div>
   </div>