← Edge AI & Private Inference

Desktop and Mobile Deployment

Deploying local AI inference on macOS, Windows, Linux, iOS, and Android -- llama.cpp, MLX, CoreML, MediaPipe, and packaging local models into desktop and mobile applications.

The native advantage

Browser deployment is powerful for reach, but native applications have advantages that matter for enterprise:

  • Full GPU access. Native applications use the GPU directly via Metal, CUDA, Vulkan, or DirectML. No browser sandbox. No shared memory constraints. A native app on an M3 MacBook Pro with 36GB unified memory can run models that no browser tab could handle.
  • Background processing. Browsers throttle background tabs. Native applications can run inference in the background while the user does other work.
  • System integration. Native apps can access the filesystem, clipboard, system notifications, and OS-level accessibility features. A desktop AI assistant that monitors your clipboard for text to summarise is possible natively but not in a browser.
  • Offline by default. Native applications with bundled models work offline without any special architecture. The model is just a file on disk.

The tradeoff is distribution. Browsers need no installation; native applications need packaging, signing, and deployment through MDM or app stores. For enterprise internal tools where you control the deployment pipeline, this is usually acceptable.

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Your organisation wants an AI-powered writing assistant that helps employees draft emails and reports. It should work offline and integrate with the OS clipboard. What deployment target fits best?