Hello people. Welcome to my presentation today I wanna talk about virtualized GPU for AI in enterprise storage. To optimize to provide a cost optimized solution. With the evolution of artificial intelligence there has been increasing demand for powerful GPUs to support the AI workloads. The organizations currently face a significant challenges related to the infrastructure costs, operational complexity and deployment efficiency. In this presentation I'm gonna talk about how Dell Technologies. VMware and Nvidia have collaborated to develop a virtualized GPU solution, specifically designed for enterprise storage environments and creating a more flexible and cost effective solution approach towards AI implementation. Now let's talk about the GPU market and its performance potential. So the present GPU market holds up to 11.8 billion market share when this projected to grow up to 29% by 2030. A study show how, virtualized GPUs exhibit an average overhead off about 18% across various workloads. But benefits of this implementation like resource utilization, market flex management, flexibility and cost optimization, which will offset these performance considerations. The collaborative approach of industry's leading tech companies like Dell, VMware. And Nvidia is gonna solve upcoming compute challenges for AI implementations. This strategic alliance has produced an exceptional outcomes demonstrating near linear performance scaling from one to eight GPUs within a single server for deep learning training workloads. Independent research conforms that the state of art virtual GPU implementations consistently deliver. Up to 94% of native bare metal performance across diverse artificial intelligence workloads. Here is the comparison of the performance metrics with like virtual GPUs versus bare metal GPUs. The metrics clearly states the virtual GPU. Performance meets almost 92% of the efficiency as traditional bare metal configuration. Achieving the most, a achieving a significant portion of what traditional traditional virtual, bare metal GPUs has been producing. Here are the key components of the solutions, the enterprise ready validated architecture integrates NVIDIA's AI Enterprise Software Suite, empowering the organizations, leveraging VMware. We spear to efficiently virtualize and containerize mission critical artificial intelligence workloads. Comprehensive performance testing that has been done has shown an exceptional results with deep learning training workloads achieving up to 96% of what traditional bare metal GPU performs. So interference workloads almost demonstrate. Even more impressive resulting results reaching to up to 98% of what a traditional GPU has been giving to us. So in the, if you look at the various components of the, this solution that has been designed, there is a virtualized deployment layer where it offers a flexible implementation across. OnPrem on-premises infrastructure or if it's a cloud service provider, we can always containerize the applications. Accelerate and simplified AI workload deployments with optimized resource utilization. And the Nvidia AI enterprise software suite offers optimized software for virtualized AI workflows. Let's compare how a virtualized GPU has been performing when compared to bare metal GPU in different use cases. So when it comes to deep learning training for ai, 96% of the bare metal performance in the virtualized environments has been achieved with minimal which is like a minimal performance. Reduction when you compare that off with traditional bare metal performance in the interference workloads the study shows we have achieved. The GPUs have virtualized, GPUs has achieved 90 per 98% of the performance. Compared to that of our traditional traditional GPUs in the tensor flow image classification it was quite a significant mark. It has achieved 95% of performance retention processing up to 4,500 images per second. In the general AI workloads you see a remarkable of 96% of performance efficiency across standard enterprise AI applications. When it comes to the memory intensive workloads, 92 per approximately 92% of the performance retention has been achieved, which shows like, only four to 5% of the decrease the come. Decreased performance compared to non-virtualized environments. Let's see. What are the operational benefits of, implementing virtualized GPUs. So you see a significant reduced deployment time. So infrastructures provisioning tasks, infrastructure provisioning tasks usually used to take weeks. That can be overcome within even, I mean in days or even hours. Reducing up to 60% of deployment time for. All the AI workloads, you see a remarkable, improved operational efficiency with organizations leveraging the VMware expertise for AI deployments achieved that, that has, they have achieved like 83%. Or greater operational efficiency and required 30% less specialized training I mean to, for their employees. Those implementing a dedicated AI infrastructure, you see an enhanced a resource utilization when compared to physical GPUs traditional bare metal GPUs. So organizations implementing these virtual GPU solutions achieved approximately three times better resource utilization than dedicated AI infrastructure and reduced maintenance overhead by approximately 54%. And also about all these the total cost of ownership is very much low. The unified approach developed by all these three tech giants reduced an overall IT operational expenses by 33% and lowers the total cost of ownership by approximately 40% over a five year period. Let's see how organization improve their operations from going to by implementing virtualized GPU solutions around the workload deployment time. The AI workload deployment time reduces up to 60% of their timelines. Transforming like week long projects into days or even hours. The operational you see a significant operational efficiency where 83% greater operational efficiency for the organizations leveraging existing VMware expertise, specialized in training. You don't have to. You don't have to plan on like training your employees for a specialized training which reduces like up to 30% of your time, resources cost in, in training your employees compared to implementing a dedicated AI infrastructure. Resource utilization has been improvised up to three, three times better when compared by implementing the virtualized solutions. Maximizing, maximizing the company's rate of investment on returns, maintenance overhead. You reduce about 54% of 54% of reduction in ongoing maintenance requirements compared to traditional siloed AI infrastructure. Here are some of the platforms where AI has integrated where AI has been integrated and, let's discuss about how the AI has virtual GPUs has helped with the AI integration on these platforms coming to the human resource sector AI powered like. AA helped this area in talent acquisition where systems are able to hire where the companies are able to hire their employees with reduced time of 37% while improving the quality of the candidates by. Quality of the human resources by 28%. The application, these applications these applications process these applications being are able to process an average of 6,000 resumes per day while requiring only 52 with only 50 to 20% of the computational resources needed in. Non-virtualized environments. So organizations implementing these solutions through virtualized GPU infrastructure, reported achieving a full deployment time of 42% more on efficient than using than using a dedicated system in the information technology area. Area AI has enhanced ai, got enhanced, cybersecurity solutions, demonstrated impressive results with organizations reporting a 47% reduction in security incidents. And a cost saving averaging about 3.33 million annually from prevented breaches. Organizations adopted virtualized that has adopted virtualized GPU solutions for their AI initiatives. Reported an average of return of investment of one 34% over a three year period where the deployments cost 20 up to 35% lower than a dedicated infrastructure approaches. In the customer service industry the organizations coming to the customer service industry, organizations deploying AI powered conservation systems could handle 65% of more of these customer interactions while reducing the staff requirements by 23% with virtualized GPU solutions. Enabling these systems to be deployed like 3.5 approximately three, three times, or four times faster than the traditional approaches. Leveraging a common infrastructure platform across multiple departments was a critical success factor with 38% lower training cost and 41% faster implementation timelines. Let's now see a department wise the platform wise benefits of AI implementation with virtualized GPU. So in the human ser resource area or industry the industry has seen, the recruitment has accelerated with 37% reduction in time to hire, and which enhance the talent acquisition with 28% improvement in quality of higher metrics. It has strengthened the workforce stability through about 24% improvement in employee retention. So boost, it also boosted the operational effectiveness with about 30% increase in the workforce productivity in the information technology area. You have seen like security posture with with 47% reduction in the critical in incidents. By delivering like about 3 million in annual cost savings from PREVENTED breaches. So it also generated a substantial one 34% of return on investment over a three year implementation period. It all, it has also achieved up to 35% lower infrastructure deployment cost in the customer service industry. You have seen you, you'll see the research shows there's a dramatical dramatically expanded capacity with about 65% increase in the customer. 65% of increase in the customer interactions. It has also optimized resource allocation with 23% reduction in staffing requirements. And also expediting the implementation process with three times faster. Faster than traditional approach. It has also reduced the operational expenses about with that, about reducing the training cost and the hassle of employees getting that trained. About 38% to summarize accelerating the virtualized GPUs has accelerated. Enterprise AI adoption. Because the, because of the unified infrastructure approach where the collaboration between tech giants like Dell, VMware, and Nvidia represents a significant advancements. In enterprise AI infrastructure addressing both technological and operational challenges that have traditionally impeded widespread AI integration a significant comparable performance. So the performance analysis confirm analysis. The performance analysis confirmed that virtualized GPUs on virtualized GPU environments deliver results comparable to bare metal implementations. For most AI workloads while offering substantial operational and financial benefits, including faster deployment and improved resource utilizations the enterprise wide implementation, the solutions. Support for these G solutions. The virtualized GPU solution support for diverse AI requirements across enterprise departments enables organizations to implement comprehensive AI strategies without creating infrastructure, silos, or operational complexities. It has finally, the end resulted end result is acceleration in AI transformation. While as AI continues to transform the business operations across the industries. The virtualized GPU approach provides a pragmatic path for enterprises seeking to balance technological innovation. With operational efficiency and cost optimizations. This concludes my presentation. Hope you enjoyed my presentation and gained insights about virtualized GPUs, which is going to be a remark, a significant development in the industry. Thank you.