Cloud platform accelerates ML, Al workloads

Healthcare, transportation, retail and financial services are all potential users who will be able to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged,” added Fanelli. There they can be modernised to take advantage of high performance computing, machine learning, data analytics and video processing applications.

Enterprises are rapidly adopting Al” and implementing new Al strategies that require powerful computers to create predictive models from petabytes of corporate data.

Across industries, enterprises are implementing machine learning applications such as image and voice recognition, advanced financial modeling and natural language processing using neural networks that rely on NVIDIA GPUs for faster training and real-time inference.

Through this partnership, VMware Cloud on AWS customers will gain access to a new, highly scalable and secure cloud service consisting of Amazon EC2 bare metal instances to be accelerated by NVIDIA T4 GPUs and new NVIDIA Virtual Compute Server (vComputeServer) software.

“Our customers are embracing the unique value of VMware Cloud on AWS to accelerate the migration and modernisation of business-critical applications,” said Pat Gelsinger, CEO, VMware. “Through new innovations driven by partnerships we have with industry leaders such as NVIDIA and AWS, we will bring best-in-class GPU acceleration services for the most intense data-driven workloads and modern applications across the hybrid cloud.”

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

Once available businesses will be able to leverage an enterprise-grade hybrid cloud platform to accelerate application modernisation.

They will be able to unify deployment, migration and operations across a consistent VMware infrastructure from data center to the AWS cloud in support of most compute-intensive workloads, including Al, machine learning and data analytics. Benefits will include:

* Seamless portability: Customers will be able to move workloads powered by NVIDIA vComputeServer software and GPUs with a single click of a button and no downtime using VMware HCx. This will give customers more choice and flexibility to execute training and inference in the cloud or on-premises.

* Elastic AWS infrastructure: With the ability to automatically scale VMware Cloud on AWS clusters accelerated by NVIDIA T4, administrators 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 virtualistion businesses have the flexibility to run GPU-accelerated workloads like Al, machine learning and data analytics in virtualisation environments for improved security, utilisation and manageability.

* Consistent Hybrid Cloud Infrastructure and Operations: With VMware Cloud on AWS, organisations can establish consistent infrastructure and consistent operations across the hybrid cloud, leveraging VMware industry-standard vSphere, vSAN and NSX as a foundation for modernising business-critical applications. IT operators will be able to manage GPU-accelerated workloads within vCenter, right 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.