Well, thank you very much for having me to glad to be able to speak to all of you again. So, I'm Preston Smith from Purdue University and I'm going to talk about how our AI computing investment is is part of the foundation for our institutional strategy at Purdue. So, Purdue right now has four major pillars strategic projects.You can see here a new campus in Indianapolis. If you if you're familiar with Indiana geography, Purdue is between Indianapolis and Chicago about halfway and Indianapolis will be at th ...

Inference Market & KV Cache Importance - Inference spending is projected to surpass training spending, highlighting its growing significance in the AI landscape [2] - KV cache is crucial for understanding context in prefill stages and augmenting tokens in decode stages during inference [3][4] - Utilizing DDN as a KV cache can potentially save hundreds of millions of dollars by retrieving previously computed contexts instead of recomputing them [5] Disaggregated Inference & Performance - Disaggregated inference, running prefill and decode on different GPUs, improves efficiency, requiring a global KV cache for information dissemination [6] - DDN's fast storage delivers KV caches at extremely high speeds, leading to massive efficiency gains [9] - DDN's throughput is reportedly 15 times faster than competitors, resulting in a 20 times faster token output [10] Productivity & Cost Efficiency - Implementing a fast shared KV cache like DDN can lead to a 60% increase in output from GPU infrastructure [12] - DDN aims to deliver a 60% increase in tokens output per watt, per data center, per GPU, and per capital dollar expenditure [13] - Using DDN offers the strongest improvement in GPU productivity over the next five years by accelerating inference models [12]

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]

Hi, I'm Sean, group product manager here at Google Cloud, focusing on our storage solutions, and I just wrapped up a session at DDN's booth here at SC25. We're talking about our new managed luster offering that's available for a variety of HBC and AI workloads. We talked about the ecosystem that Google Cloud brings for the HBC environments and includes optimized server hardware including new machine like H4D specifically designed for high performance computing workloads.We also have cluster toolkit to autom ...

Welcome everybody. It's great to see a great crowd on this Monday before the show in St. Louis of all places.So this is SC25. We've actually I think been coming to SC for the past 20 years. So we are certainly veterans like Alex was saying in HPC and we're close to getting veterans in AI because I mean we've been involved with Nvidia and AI and other players for the past now 9 10 years already.It's kind of crazy. We started literally 9 years ago in Guilty with Jensen and Nvidia trying to map out what would ...

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 ...