NVIDIA and VMware Partnership at VMworld 2019 | Summary and Q&A

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November 6, 2019
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NVIDIA
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NVIDIA and VMware Partnership at VMworld 2019

TL;DR

AI and ML are becoming increasingly prevalent in all areas of business and science, offering the ability to unlock valuable insights from data.

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Questions & Answers

Q: How is AI and ML becoming prevalent in business and science?

AI and ML are emerging as ubiquitous workloads, allowing businesses and scientists to extract valuable insights from data that traditional analytics cannot provide. This trend is seen across various industries.

Q: How do GPUs contribute to AI and ML?

GPUs are valuable for high-performance computing and AI/ML applications. They provide the computational power required for processing large datasets and complex algorithms, improving the performance and efficiency of these workloads.

Q: What improvements have been made in virtualization for GPU usage?

Previously, virtualization only supported pass-through mode for GPUs on vSphere. However, recent advancements now enable fractional and full GPU usage, not just for graphics but also for compute tasks. This expands the capabilities of virtualized environments for AI and ML workloads.

Q: How does VMware's collaboration with Nvidia benefit customers?

The collaboration between VMware and Nvidia brings important advancements and technologies to customers who require GPU resources in their data centers. These include secure multi-tenancy, rapid resource provisioning, and self-provisioning. The joint engineering efforts have resulted in features like V motion, which allows the migration of GPU-enabled virtual machines between servers.

Summary & Key Takeaways

  • AI and ML are emerging as ubiquitous workloads, with the ability to unlock more insights from data than traditional analytics.

  • GPUs are useful for high-performance computing and AI/ML applications, and VMware's virtualization capabilities have expanded to support fractional and full GPUs.

  • The recent bit fusion acquisition enables extending GPU capabilities into distributed environments, enhancing VMware's offerings for important workloads.

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