Covering Disruptive Technology Powering Business in The Digital Age

VMware, Inc. announced innovations to accelerate the adoption of artificial intelligence (AI) and machine learning (ML) technologies for businesses as they pivot towards Malaysia’s growing innovation economy. Organisations will have access to elastic infrastructure on-demand to support the increasing adoption of emerging technologies for growth in Malaysia’s digital future with Bitfusion Technology integrated into VMware vSphere 7. This new feature – VMware vSphere Bitfusion – is developed out of VMware’s 2019 acquisition of Bitfusion, a pioneer in the virtualisation of hardware accelerator resources including graphics processor unit (GPU) technology.

Home to one of the world’s most vibrant technology startup scenes and most dynamic e-commerce market, Southeast Asia’s innovation ecosystem is powering the growth of the region’s digital economy, which is projected to reach US$300 billion by 2025[1]. Businesses in the region are turning to technologies such as AI and ML to maximise operational efficiency and fast-track business innovation to capitalise on this growth opportunity. Organisations use hardware accelerators such as GPUs to dramatically improve the performance of AI/ML workloads that may run several hours or longer. IT teams have come to realise that these hardware accelerators are isolated islands—unable to be shared across many parts of the business. The inability to share those resources leads to inefficient and poor utilisation of both existing and newly purchased resources. The combination of Bitfusion and VMware vSphere will be able to deliver cost savings, enable resource sharing out of the box and deliver the right hardware accelerator resource, like a GPU, to the right workload at the right time.

“We aim to deliver the same value to GPUs that we delivered for CPUs,” said Krish Prasad, senior vice president and general manager, Cloud Platform Business Unit, VMware. “By breaking down existing silos of GPU resources, organizations will be able to achieve better utilization and efficient use of them through sharing – resulting in immediate cost savings. More importantly, organizations will be able to jumpstart new or stalled AI/ML initiatives to drive their business forward by sharing those GPU resources with their teams on-demand with VMware vSphere 7.”

“Organizations in Malaysia are excited about the growth prospects its flourishing digital economy brings. Many of them are now turning to AI and ML to automate, streamline and catalyze business innovations to unlock growth,” said Devan Parinpanayagam, Country Manager, VMware Malaysia. “With the enhancements to VMware vSphere 7, businesses will have a powerful tool that enables them to harness emerging technologies to drive their growth towards a future that is digital.”

VMware vSphere 7 with Bitfusion Enables Efficient GPU Pooling and Sharing

AI and ML-based applications – deep learning training in particular – rely on hardware accelerators to tackle large and complex computation. VMware vSphere 7 will enable enterprises to pool their powerful GPU resources on their servers and share them within their data centres with the newly integrated Bitfusion capabilities. That will enable organisations to efficiently and rapidly share GPUs across the network with teams of AI researchers, data scientists and ML developers relying on and/or building AI/ML applications.

Released in April 2020, VMware vSphere 7 was rearchitected into an open platform using Kubernetes to provide a cloud-like experience for developers and operators. The Bitfusion feature of VMware vSphere 7 will leverage GPUs for applications running in virtual machines or containers. Bitfusion can operate in a Kubernetes environment such as VMware Tanzu Kubernetes Grid, and is expected to run side-by-side as customers deploy AI/ML applications as part of an overall modern applications strategy. The Bitfusion feature of VMware vSphere will be available through a single download with no disruption to current infrastructure and will seamlessly integrate with existing workflows and lifecycles.

VMware completed the acquisition of Bitfusion last year with an intention to integrate the technology into VMware vSphere. Bitfusion offered a software platform that decoupled specific physical resources from the servers they are attached to in the environment. This included sharing GPUs in a virtualised infrastructure, as a pool of network-accessible resources, rather than isolated resources per server. In addition, the platform supported other accelerators like FPGAs and ASICs. Bitfusion will add these differentiated capabilities to the already existing support for GPUs in VMware vSphere with this launch.

Dell Technologies Taps VMware for Dell EMC Ready Solutions

Today, Dell Technologies also announced two new Ready Solutions: Dell EMC Ready Solutions for AI: GPU-as-a-Service and Dell EMC Ready Solutions for Virtualized High Performance Computing (HPC). Read more details here.

With the new Dell EMC Ready Solutions for AI: GPU-as-a-Service, customers will be able to quickly and conveniently take advantage of GPUs to supercharge AI projects including predictive analytics, machine learning and deep learning. These Ready Solutions will incorporate VMware Cloud Foundation including VMware vSphere Bitfusion along with Dell EMC servers, storage, networking and services. These solutions will help customers to provide developers and data scientists self-service access to a virtualized accelerator pool to increase the utilization and efficiency of these valuable resources.

The new Dell EMC Ready Solutions for Virtualized HPC (vHPC) will make it simpler for organizations to run demanding AI applications in VMware environments. The ability to virtualize HPC and AI operations with VMware Cloud Foundation including VMware vSphere Bitfusion or VMware vSphere Scale-Out Edition will offer rapid hardware provisioning on demand, faster initial setup, and configuration and ongoing maintenance with centralized management and security. Dell EMC Ready Solutions for vHPC support the intensive compute needs for bioinformatics, computational chemistry and computer-aided engineering.

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