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, 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 product line for advanced AI applications. This solution leverages Intel® Arc™ Xe Cores and Xe Matrix Extensions (XMX) for accelerating inferencing.
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 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
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: