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microsoft · Oct 13, 2023 · 5bee082 · 5bee082
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                     <section class="py-md-5 pb-4 pt-4 mt-5">
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                         <h1 class="r-heading">Run PyTorch models on the edge</h1>
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+                        <p class="article-date">12th October, 2023</p>
 
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                                         <li>High cost of cloud resources (especially when device capabilities are underutilized)</li>
                                         <li>Application requirements to operate without internet connectivity</li>
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+                                    <img src="../images/pytorch-on-the-edge-with-ort.png" style="width:70%;" alt="Diagram showing the PyTorch logo representing a PyTorch model, fanning out to icons for web, mobile and browser devices running ONNX Runtime" class="img-fluid">
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                                     <p>In this article, we'll demystify running PyTorch models on the edge. We define 'edge' as anywhere that is outside of the cloud, ranging from large, well-resourced personal computers to small footprint devices such as mobile phones. This has been a challenging task to accomplish in the past, but new advances in model optimization and software like <a href="https://onnxruntime.ai/pytorch">ONNX Runtime</a> make it more feasible – even for new generative AI and large language models like Stable Diffusion, Whisper, and Llama2.</p>
-                                    <img src="../images/pytorch-on-the-edge-with-ort.png" alt="Diagram showing the PyTorch logo representing a PyTorch model, fanning out to icons for web, mobile and browser devices running ONNX Runtime" class="img-fluid">
 
                                     <h2 class="blue-text">Considerations for PyTorch models on the edge</h2>