VMworld—NVIDIA and VMware today announced their intent to deliver accelerated GPU services for VMware Cloud on AWS to power modern enterprise applications, including AI, machine learning and data analytics workflows. These services will enable customers to seamlessly migrate VMware vSphere-based applications and containers to the cloud, unchanged, where they can be modernized to take advantage of high-performance computing, machine learning, data analytics and video processing applications.
Increasingly businesses are applying artificial intelligence (AI) technologies to differentiate and advance their processes and offerings. Enterprises are rapidly adopting AI(1) and implementing new AI 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. Additionally, VMware recently acquired Bitfusion, which enables VMware to make GPU capabilities efficiently available for AI and machine learning workloads in the enterprise.
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 (vCS) software.
“From operational intelligence to artificial intelligence, businesses rely on GPU-accelerated computing to make fast, accurate predictions that directly impact their bottom line,” said Jensen Huang, founder and CEO, NVIDIA. “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.”
“Our customers are embracing the unique value of VMware Cloud on AWS to accelerate the migration and modernization 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 AI, ML and Data Analytics
Once available businesses will be able to leverage an enterprise-grade hybrid cloud platform to accelerate application modernization. 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 AI, machine learning and data analytics. Benefits will include:
- Seamless portability: Customers will be able to move workloads powered by NVIDIA Virtual Compute Server 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 Virtual Compute Server 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, 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.
- Gartner, “AI and ML Development Strategies,” July 15, 2019.