NVIDIA (NVDA.Q) and VMware (VMW.NYSE) are joining forces to deliver enhanced GPU services for the latter’s VMware Cloud on AWS integrated cloud service.

The GPU services will power their modern applications, including artificial intelligence, machine learning and data analytics workflows. The intent here is to give customers the ability to migrate all of their business related applications and containers to the cloud without data loss, where they can take full advantage of high performance computing, machine learning, data analytics and video processing applications.

“From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line. Together with VMware, we’re designing the most advanced GPU infrastructure to foster innovation across the enterprise, from virtualization, to hybrid cloud, to VMware’s new Bitfusion data center disaggregation,” said Jensen Huang, founder and CEO, NVIDIA.

Did you ever see the early 1990’s film The Lawnmower Man? It’s Flowers for Algernon meets The Matrix, wrapped up in a bow of ’90’s optimism with a whiff of golden age science fiction.

Spoiler alert: Jeff Fahey uploads himself into the pre-broadband internet in the end to the chagrin of 56k baud modem users everywhere, who probably groaned as the time remaining on their porn download went up by at least a week. By the end of the movie, Jeff Fahey’s uploaded consciousness gave him conscious control over the processes of the internet.

We’ll probably never reach that level of strange and unusual technology, but cloud computing is probably the closest we have right now.

This partnership brings Vmware Cloud on AWS customers access to a new, highly scalable and secure cloud service using Amazon EC2 bare metal instances accelerated by NVIDIA T4 GPUs, and new NVIDIA Virtual Compute Server (vComputeServer) software.

It’s not exactly a super-enhanced consciousness uploaded directly into the internet controlling your data, but it’s close enough for government work. Cloud computing presents with respectable storage capacity, giving your workers the ability to do their job from anywhere, and NVIDIA’s chips make it faster and more scalable.

Companies, especially in the tech industries, are increasingly moving to artificial intelligence (AI) technologies to enhance productivity. These require computers powerful enough to analyze petabytes of corporate data and develop predictive models, and across industries enterprises are implementing machine learning applications like image and voice recognition, advanced financial modelling and natural language processing using neural networks using NVIDIA GPUs.

Customers will be able to move their work to the cloud with a click of a button, with no downtime.

Benefits of VMware Cloud on AWS with NVIDIA GPU for AI, ML and Data Analytics

• Elastic AWS infrastructure:With the ability to automatically scale VMware Cloud on AWS clusters, accelerated by NVIDIA T4, https://e4njohordzs.exactdn.com/wp-content/uploads/2021/10/tnw8sVO3j-2.pngistrators will be able to grow or shrink available training environments depending on the needs of their data scientists.
• Accelerated computing for modern applications: NVIDIA T4 GPUs feature Tensor Cores for acceleration of deep learning inference workflows. When these are combined with vComputeServer software for GPU virtualization, businesses have the flexibility to run GPU-accelerated workloads like AI, machine learning and data analytics in virtualization environments for improved security, utilization and manageability.
• Consistent Hybrid Cloud Infrastructure and Operations: With VMware Cloud on AWS, organizations can establish consistent infrastructure and consistent operations across the hybrid cloud, leveraging VMware industry-standard vSphere, vSAN and NSX as a foundation for modernizing business-critical applications. IT operators will be able to manage GPU-accelerated workloads within vCenter, alongside GPU-accelerated workloads running on vSphere on-premises.
• Seamless, end-to-end data science and analytics pipeline:The NVIDIA T4 data center GPU supercharges mainstream servers and accelerates data science techniques using NVIDIA RAPIDS™, a collection of NVIDIA GPU acceleration libraries for data science including deep learning, machine learning and data analytics.

When NVIDIA finally rolls these services out then business will be able to leverage the hybrid cloud platform infrastructure to completely modernize their operations, storing the proprietary information in a secure data centre that supports the most computer-intensive workloads, including AI, machine learning and data analytics.

—Joseph Morton