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Discussion on: How to build a virtualized GPU that executes remotely and keeping your data local?

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Carolyn Weitz

I’ve fielded this question a few times from customers who need heavy GPU compute but can’t move sensitive data off‑premises. The key is to use GPU virtualization—technologies like NVIDIA vGPU or SR‑IOV to carve a physical GPU into multiple virtual devices that live in the cloud or a remote data center. You then establish a secure, high‑speed tunnel (VPN or dedicated link) so your application streams only the model weights or tensor data to the remote GPU, while all of your raw data remains on local storage. AceCloud’s GPUaaS supports this pattern: we provide the virtual GPU endpoint and secure networking, you keep your data vault‑locked on your side, and all the heavy lifting happens off‑site without compromising compliance or performance.