Machine learning and AI give businesses a competitive edge. These technologies help extract insights from massive data collection, facilitate medical predictions, and improve predictive maintenance in manufacturing, just to name a few examples. However, businesses must figure out how to use these tools at scale without spiking infrastructure costs.

This increased cost comes from adding components that are shared among the organization and support ML workloads efficiently. For example, multi-CPU setups are inefficient at handling ML workloads and can create needlessly high overhead. Many organizations turn to more specialized processing hardware like GPUs, FPGAs, and ASICs for a better price-performance ratio. 

Businesses that use ML-specific processors need to ensure two things. First, that resources are readily available for applications that need them. Second, that they can control how resources are shared to optimize utilization. Let’s look at how GPU virtualization gives businesses this control and supports ML, AI, and other processor-hungry tasks.

How GPU Virtualization Facilitates ML and AI Workloads

Legacy GPU virtualization was CPU-based, which meant it couldn’t provide a native experience to users. More recently, this has changed to GPU-based virtualization, providing a native experience across multiple virtual machines. This change is important because the GPU’s ability to handle parallel tasks is what makes it especially adept at ML and AI workloads. Why use virtual GPUs (vGPUs) instead of regular GPUs?

Virtual GPUs allow hardware to be shared among many users instead of the one-to-one relationship of physical hardware. This distribution gives businesses three main advantages over physical hardware:

1. Reduced downtime during administrative tasks or upgrades.

To ensure that hardware is working at its peak and resources are being shared equitably, organizations must perform routine maintenance tasks. These include workload leveling, upgrading infrastructure, and performing software patches or upgrades. Virtual GPUs allow for live migrations that have minimal impacts on users — users may experience as little as a few seconds of downtime during migration.

2. Optimize GPU utilization.

Because live migrations don’t disrupt users’ workflow, it’s easy to scale or shift resources on the fly. Virtualization also allows administrators to create vGPU profiles based on workload and usage. These profiles help ensure the best experience for users and optimal usage of company resources. One example of this is VMware’s vSphere policies for scheduling vGPU utilization. VSphere uses policies that shift resources when GPUs don’t have tasks that reserve resources on an equal basis or based on the vGPU profile associated with each GPU.

3. Free up CPU resources.

When CPUs offload compute-intensive applications, their usage is dramatically reduced. This frees them up for CPU-focused workloads and helps avoid overworking them with tasks they are poorly designed to handle.

Virtual GPUs Need Quality Physical Hardware

Workload balancing and the optimization of computing resources are two of the best ways for companies to increase capabilities and improve price performance. Virtual GPUs make this possible by giving companies more control over how hardware is shared and more insights into utilization. However, to power vGPUs, companies need robust hardware.

Intequus can help you design and deploy computing infrastructure that supports your ML and AI needs. Our white box solutions provide extremely flexible build options, and our full-lifecycle service covers you from deployment to decommissioning. Talk about your hardware needs with a team member today. 

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