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title: PyTorch Edge
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      <h1 class="small">PyTorch Edge</h1>
      <p class="lead pt-3">Build innovative and privacy-aware AI experiences for edge devices</p>
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        <h2 class="mt-5 mb-2">PyTorch Edge</h2>
        <p>The AI landscape is quickly evolving, with AI models being deployed beyond server to edge devices such as mobile phones, wearables, AR/VR/MR and embedded devices. PyTorch Edge extends PyTorch's research-to-production stack to these edge devices and paves the way for building innovative, privacy-aware experiences with superior productivity, portability, and performance, optimized for these diverse hardware platforms.</p>
        <h2 class="mt-5 mb-2">Introducing ExecuTorch</h2>
        <p>To advance our PyTorch Edge offering, we developed <a href="https://pytorch.org/executorch-overview">ExecuTorch</a>, our new runtime for edge devices. ExecuTorch facilitates PyTorch inference on edge devices while supporting portability across hardware platforms with lower runtime and framework tax. ExecuTorch was developed collaboratively between industry leaders including Meta, Arm, Apple, and Qualcomm. </p>
        <p>With ExecuTorch, we’ve renewed our commitment to on-device AI. This extends our ecosystem in a much more “in the spirit of PyTorch” way, with productivity, hackability, and extensibility as critical components. We look forward to supporting edge and embedded applications with low latency, strong privacy, and innovation on the edge.  </p>
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        <h2>Learn more about PyTorch Edge</h2>
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        <p class="lead">What’s New in ExecuTorch</p>
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          ExecuTorch
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        <p class="lead">Try ExecuTorch</p>
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          ExecuTorch Documentation
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