Bitfusion to Join with VMware to Present the Need for Elastic Artificial Intelligence Infrastructure at VMworld 2018 US
August 13, 2018
Artificial intelligence, machine learning and analytics adoption
drive increased demand for flexible virtual infrastructure
SILICON VALLEY, CA. – August 13, 2018 – Bitfusion, the Elastic Artificial Intelligence (AI) software company, together with VMware, will present a session at VMworld 2018 US focusing on ‘Elastic AI Infrastructure on vSphere.’ The presentation will highlight how Bitfusion’s elastic AI infrastructure combines with VMWare vSphere to enable network-attached virtual GPU and FPGA deployment from a shared pool, enabling real-time response to machine learning workload demand.
“As enterprises look to support more applications and users due to the adoption of artificial intelligence, analytics and machine learning strategies, they require flexible technologies that deliver high performance while maintaining costs savings,” said Michael Zimmerman, CEO of Bitfusion. “The combination of Bitfusion Elastic AI with VMWare vSphere delivers a unique solution that increases productivity, profitability, CapEx, OpEx and agility.”
Bitfusion creates a virtual elastic GPU cluster from all of the scattered deployed GPU servers in an enterprise. With Bitfusion software, each physical GPU can be partitioned into multiple flexible and elastic virtual GPUs. Any user, framework and computer server can attach instantaneously to a remote fractional GPU, single GPU or group of GPUs in the virtual cluster, run the AI code and then detach. Bitfusion virtualizes the capacity and location of GPUs and makes them accessible to any compute machine in the network.
VMworld 2018 US will take place August 26 – 30, 2018, at Mandalay Bay in Las Vegas, Nevada. The Bitfusion breakout session will take place on August 27th from 2:30 p.m. – 3:30 p.m.
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 a GPU cluster of any size by allowing any workload to remotely attach over Ethernet to a partial GPU, one or more GPUs anywhere in the cluster, for only the duration of the runtime code. www.bitfusion.io.
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