Hey guys, uh my name is Omar. I'm just going to give a brief overview of u what I see is important, where do we think we're going. And my job basically is to just work as closely as possible with you guys who are our customers to make sure that we build the best products possible.And then I'm going to invite the really smart people, Dr. . Jameskumer and Swen who can come up and take a deep dive into some of the exciting technologies that we're building. So with that um I won't spend too much time on this ch ...

How are GPUs being constrained in average environments today. And really the big challenge is data and actually we sometimes call artificial intelligence data intelligence at DDN because it's really about how we turn data as a source code into applications you can run which are intelligent and provide intelligence to the to the customers. And when we're building those models and when we're running those models and even when we're preparing the data to build those models, these are all GPU operations that ar ...

my journey started uh largely instrumenting code to run on a single GPU. This was state-of-the-art uh when I started and that that was sort of great. We had some challenges but we had this this disc media called SATA and it sort of kept up with what we wanted to do but it didn't take long when we started instrumenting as science instrumenting code for multiGPU particularly as science began to increase the size of data we could do more.So they were doing more. So we were growing the platform capability by ad ...

AI Adoption - Ease of integration is crucial for enterprises adopting AI [1] - AI adoption depends on ease of use and management, not just value [1] DDN's Position - DDN's current focus is on ease of integration [1]

Google Cloud HPC & AI Solutions - Google Cloud offers managed luster for HPC and AI workloads [1] - Google Cloud provides an ecosystem for HPC environments, including optimized server hardware like H4D [1] - Google Cloud's cluster toolkit automates the provisioning of compute, network, and storage resources [2] Storage Performance & Optimization - Managed luster is a high-performance, low-latency solution for both HPC and AI [2] - Google Cloud offers various storage options to match price, performance, and capacity needs [2] - The goal is to maximize the utilization of accelerators for HPC or AI workloads [2]

Company Strategy & Market Positioning - DDN is doubling down on AI and expanding its global presence, recognizing the importance of markets beyond the US, Europe, and Japan, including the Middle East, South Asia, and the Nordics [2][3][4] - DDN supports multiple GPU vendors like AMD, Intel, and Cerebras, but highlights Nvidia as the current market leader [4][5] - DDN emphasizes the importance of data strategy alongside GPU acquisition, highlighting data as the key differentiator for AI success [9][10][11] - DDN is widening its go-to-market strategy, engaging with resellers, VARs, global system integrators (Accenture, Deloitte, Cognizant, HCL), and AI server/hardware vendors (Super Micro, Lenovo, Eviden, Dell, HP) [19][20] Product & Technology - DDN's Exoscaler platform is designed for large-scale HPC and AI training, supporting multi-model training with thousands of GPUs and offering easy deployment and upgrades [16] - DDN's Infinia technology offers fast object storage and can run on various platforms, including Nvidia's Bluefield DPUs, with a roadmap including scale-out NFS [12][17][18] - DDN now has a T1 offering inside Google Cloud Platform (GCP), allowing users to test Exoscaler directly [21] Financial & Investment - DDN received a $300 million investment from Blackstone, valuing the company at a few billion dollars, demonstrating its long-term commitment [23] Recognition & Partnership - DDN was recognized as the second-ranked company worldwide (first outside of China) for AI and enterprise in a recent Gartner report [24][25] - Nvidia relies on DDN for its supercomputers, highlighting the strong partnership and its expansion into the enterprise world [27]

AI Infrastructure Challenges & Solutions - Data bottlenecks constrain GPU performance in AI training and inference, leading to wasted resources and reduced productivity [2][4][5][11] - DDN addresses these bottlenecks by optimizing data movement through fast storage systems and integration with AI frameworks and hardware like Nvidia [5][6] - Inference is becoming increasingly important, with spending expected to surpass training systems, posing challenges in model loading, RAG (Retrieval Augmented Generation), and KV cache management [7][8][9] - DDN Core combines Exascaler for training and Infinia for data management to provide a seamless AI experience [13][14] DDN's Value Proposition - DDN's solutions improve data center efficiency by increasing "answers per watt," delivering more compute with less energy consumption [12][13] - DDN handles KV cache, increasing the effective memory of GPU systems and improving productivity by up to 60% in large-scale GPU data centers [9][10] - DDN offers fast-track solutions for enterprises to adopt AI, whether on the cloud or on-premise, through partnerships like the one with Google Cloud [15][16][17] - DDN's platform supports various use cases, including HPC, AI training and inference, research data management, and secure data sharing [19][20] Strategic Considerations - DDN emphasizes the importance of considering data first when building AI at scale, advocating for data desiloing and secure access [28][29] - DDN supports sovereign AI, enabling nations to develop AI models relevant to their specific data, language, and culture while ensuring security and data sharing [20][21][22] - Partnerships are crucial for delivering efficient AI solutions tailored to customer preferences, whether cloud, on-premise, or hybrid [23][24] - AI factories, which integrate data preparation, training, simulation, and production, present complex data challenges where DDN excels [25][26][27]

um the AI infrastructure evolution across the globe is at a breakneck speed. So from the seat where you're at and from your expertise, what are the inflection points. What are the imperatives that someone needs to see.>> Sure. I mean the inflection points is that we've already done through the test phase, right. So I mean if you're looking at the large foundational models, interesting models, you know, there there's basically growth and scale that's going to keep on going, right.So you have this basically m ...

There are three waves simulation AI and then you got quantum more and more data is coming in. What is an AI factory if someone asked you. >> So to me okay to me an AI factory is a tool a capability it could be physical as in a data center it could be a combination of physical in a data center and cloud but it's a way to generate and create business value business outcomes and financial outcomes for organizations who are looking at making investments in AI.So an AI factory for an enterprise, let's say financ ...

So in the past customers picked object store interfaces because of their simplicity and picked file systems interfaces primarily for their performance. What would you do if you have a technology that can give you the interactive performance of a file system but the simplicity of an object store. That is essentially what Infinia does. ...