VMware’s latest acquisition announcement of virtualization software provider for AI infrastructure, Bitfusion, shows the intention of enabling running AI applications on virtualization platforms.
Traditionally, virtualization of CPU-only infrastructure enables a more efficient way of operational flexibility, storage capacity and processing power to leverage both on-premise infrastructure and hybrid cloud environments.
The next step in virtualization is supporting AI- and ML-based workloads by virtualizing hardware accelerators such as graphics processor units (GPUs), field-programmable gate arrays and application-specific integrated circuits, regardless of location in the environment, on-premises and in the cloud.
Virtualization can increase IT agility, flexibility, and scalability while creating significant cost savings. Workloads get deployed faster, performance and availability increases and operations become automated, resulting in IT that’s simpler to manage and less costly to own and operate.
According to VMware, multi-vendor hardware accelerators are key components in delivering modern applications. Specifically, the virtualization of accelerated computing power provides more efficiency and flexibility for ML and AI-based applications, with faster automation, increased sharing of resources at a lower cost of deployment.
This virtualization solution enables a better sharing of GPU resources in a virtualized infrastructure rather than through isolated GPU-powered workloads and resources per sever. It entails virtualizing the chips that companies use to run AI applications and ML projects, allowing them to get the most out of the AI processing power and usage.
With this acquisition, VMware aims to enhance AI capabilities and include hardware acceleration for applications as a leverage for its popular vSphere virtualization platform.
A strong focus of VMware remains the ability to work across multiple clouds, networks, VMs and containers, and now also AI frameworks. Thanks to the virtualization solution for hardware accelerators, AI technology can be deployed and scaled efficiently on any cloud, app and device, further compartmentalising and streamlining hardware resources.
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