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VMware acquires Bitfusion to virtualize hardware accelerators

VMware

VMware has unveiled its plan to acquire the US-based startup Bitfusion to help enterprises more efficiently use artificial intelligence (AI) on-premises and in hybrid cloud environments.

Founded in 2015, Bitfusion provides virtualization software for AI infrastructure, with a strong focus on GPU technology.

Hardware accelerators like GPUs (graphic processor units), FPGAs (field-programmable gate arrays), and ASICs (application-specific integrated circuits) provide efficiency and flexibility into the AI space. But the hardware accelerators generally used are deployed with bare-metal practices, which aren’t much efficient. Virtualizing the hardware accelerators can speed up the sharing of resources and optimize costs.

With Bitfusion acquisition, VMware will be well-positioned to support AI workloads by virtualizing hardware accelerators. Its solution brings more sophisticated management by “sharing of graphics card resources between isolated GPU workloads.”

Bitfusion’s software platform decouples physical resources from the servers that are connected to the cloud.

“In many ways, Bitfusion offers for hardware acceleration what VMware offered to the compute landscape several years ago. Bitfusion also aligns well with VMware’s “Any Cloud, Any App, Any Device” vision with its ability to work across AI frameworks, clouds, networks, and formats such as virtual machines and containers,” wrote VMware in a blog post.

While Bitfusion focuses on GPUs, it can also be extended to support FPGAs and ASICs. VMware said that Bitfusion also aligns well with its “Any Cloud, Any App, Any Device” vision with its ability to work across AI frameworks, clouds, networks, and formats like VMs and containers.

Following the closure of acquisition, VMware will integrate Bitfusion into its vSphere platform. The software virtualization giant will work the Bitfusion team to close the gap between traditional CPU-only infrastructure and the hardware that features accelerators.

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