VMware Private AI Business Update

This session will provide a business update on the state of VMware Private AI in the market. It focuses on advancements and announcements since VMware by Broadcom’s presentation at AI Field Day 4, including the key Enterprise AI challenges and the most common business use cases that have emerged. This session is followed by technical sessions and demos detailing the generally available version of VMware Private AI Foundation with NVIDIA, best practices for operationalizing VMware Private AI, and real world application of VMware Private AI do deliver AI applications to users.

The VMware Private AI Business Update presented by Jake Augustine at AI Field Day 5 provided a comprehensive overview of VMware’s advancements in the AI space, particularly focusing on the VMware Private AI Foundation with NVIDIA. Since its general availability in July, the solution has been designed to simplify the deployment of AI workloads across enterprises, leveraging VMware Cloud Foundation (VCF) and NVIDIA AI Enterprise. The collaboration between VMware and NVIDIA allows enterprises to operationalize GPUs within their data centers, providing a familiar control plane for IT teams while enabling data scientists to accelerate AI initiatives. The solution supports NVIDIA-certified hardware, including GPUs like the A100, H100, and L40, and offers flexibility in storage options, with VSAN being recommended but not mandatory for all workloads.

One of the key challenges VMware aims to address is the growing complexity and sprawl of AI workloads within organizations. As AI adoption increases, particularly with the rise of generative AI and large language models, enterprises are struggling to scale these workloads efficiently. VMware’s platform-based approach provides a unified infrastructure that allows IT teams to manage AI workloads at scale, reducing the need for data scientists to focus on infrastructure management. This approach also helps stabilize the organic growth of AI projects within organizations, offering better visibility into resource utilization and cost planning. By virtualizing AI workloads, VMware enables enterprises to optimize GPU usage, reducing costs and improving operational efficiency.

The presentation also highlighted the importance of time-to-value for enterprises adopting AI. VMware’s solution has demonstrated significant improvements in deployment speed, with one financial services customer reducing the time to deploy a RAG (retrieval-augmented generation) application from weeks to just two days. Additionally, the platform’s ability to handle both inference and training workloads, while integrating with third-party models and tools, makes it a versatile solution for enterprises at different stages of AI adoption. Overall, VMware’s Private AI Foundation with NVIDIA is positioned as a scalable, secure, and cost-effective solution for enterprises looking to operationalize AI across their organizations.