Bitfusion Joins the VMware Technology Alliance Partner Program

April 18, 2018

Sunnyvale, CA Apr. 16, 2018Bitfusion, The Elastic GPU Software Platform, today announced it has joined the VMware Technology Alliance Partner (TAP) program as an Access level partner. Members of the TAP program collaborate with VMware to deliver innovative solutions for virtualization and cloud computing. The diversity and depth of the TAP ecosystem provides customers with the flexibility to choose a partner with the right expertise to satisfy their unique needs.

With thousands of members worldwide, the VMware TAP program includes technology partners with the shared goal to bring the best expertise and business solutions for each unique customer environment.

“We welcome Bitfusion as a valued member of the VMware TAP program,” said Kristen Edwards, director, Technology Alliance Partner Program, VMware. “This membership means customers can take full advantage of a streamlined cloud infrastructure experience. By joining the program, Bitfusion is working with VMware to develop technologies that can transform customers’ environments.”

“Bitfusion is pleased to announce a VMware Technology Alliance Partnership,” said Michael Zimmerman, Bitfusion CEO. “As machine learning and AI workloads are growing and must be streamlined, Bitfusion offers virtualization and an AI network-attached set of solutions which complement VMware’s vSphere Cloud offering.”

Bitfusion’s product information, collateral and other assets are listed within the online VMware Solution Exchange at The VMware Solution Exchange is an online marketplace where VMware partners and developers can publish rich marketing content and downloadable software for customers.

About Bitfusion
AI and Machine Learning are changing every aspect of the data center. GPUs (and soon newer purposed-built AI hardware) are deployed at scale in enterprises to accommodate newer workloads. However, unlike storage, compute and networking, GPUs are deployed in a scattered, siloed and uncoordinated way across the enterprise. This drives negative metrics of productivity, profitability, CapEx, OpEx and agility. With Bitfusion, GPUs can be deployed as a shared and common pool, responding in real time to any demand of a machine learning workload. Bitfusion virtualizes the GPU cluster (of any size) by allowing any workload to attach remotely (over Ethernet) to one or more GPUs anywhere in the cluster, for only the duration of the runtime code. See:

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