Verge.io, the company with a simpler way to virtualize data centers, has added new features to its Verge-OS software to give users the performance of GPUs as virtualized, shared resources. This creates a cost-effective, simple and flexible way to perform GPU-based machine learning, remote desktop, and other compute-intensive workloads within an agile, scalable, secure Verge-OS virtual data center.
GPU passthrough to VMs
Verge-OS abstracts compute, network, and storage from commodity servers and creates pools of raw resources that are simple to run and manage, creating feature-rich infrastructures for environments and workloads like clustered HPC in universities, ultra-converged and hyper-converged enterprises, DevOps, and Test/Dev, compliant medical and healthcare, remote and edge compute including VDI, and xSPs offering hosted services including private clouds.
Current methods for deploying GPUs systemwide are complex and expensive, especially for remote users. Rather than supplying GPUs throughout the organization, Verge.io allows users and applications with access to a virtual data center to share the computing resources of a single-GPU-equipped server. Users and administrators can pass through an installed GPU to a virtual data center by simply creating a virtual machine with access to that GPU and its resources.
Alternatively, Verge.io can manage the virtualization of the GPU and serve up vGPUs to virtual data centers. This allows organizations to easily manage vGPUs on the same platform as all other shared resources. Yan Ness, CEO of Verge.io said;
« The ability to deploy GPU in a virtualized, converged environment, and access that performance as needed, even remotely, radically reduces the investment in hardware while simplifying management. Our users are increasingly needing GPU performance, from scientific research to machine learning, so vGPU and GPU passthrough are simple ways to share and pool GPU resources as they do with the rest of their processing capabilities. »
Verge-OS is an ultra-thin software that is easy to install and scale on low-cost commodity hardware and self-manages based on AI/ML. A single license replaces separate hypervisor, networking, storage, data protection, and management tools to simplify operations and downsize complex technology stacks.