Software-Defined GPU

Making GPUaaS work for service providers and the mainstream AI hosting market

You’re not virtualizing GPU the wrong way – you’re just not doing it the optimal way for maximum utilization, flexibility and ROI from your hardware investments.

This high-level whitepaper explains the difference between GPU virtualization and true GPU as a Service (GPUaaS).

It explores the benefits and drawbacks of GPU passthrough, instancing and slicing, and explains why service provider GPUaaS requires a different approach – especially for AI inference workloads. To get your free copy, just fill out the form.


Contents

Why GPU virtualization ≠ GPUaaS

What does service provider GPUaaS need?

Software-Defined GPU requirements

hosted·ai GPUaaS architecture

hosted·ai hyperconverged platform

Standalone GPUaaS with hosted·ai

More information