For instance, um if you're a pharmaceuticals company and you want to bring a new drug to market, it's a very complex process. It requires significant amounts of data processing. That data processing is read intensive but mostly write intensive because you're doing simulations.So you need an AI infrastructure, a data intelligence platform that gives you the ability to run these extremely complex simulations at massive scale. The underlying data intelligence platform has to be extremely fast, extremely effici ...

let's bring up Sven our CTO so James just mentioned and talked a lot about exos scaler now it's time time to talk about the Next Generation infinia good afternoon uh my name is Sven I'm ddn CTO and I would like to talk a little bit about infinia um infinia is a really interesting project uh we started this about seven years ago um and we took a very different approach on how we get it to Market so instead of just gaing it we basically picked a couple of very large scale customers we have actually a system r ...

AI Infrastructure & Market Trends - Modern AI applications are expanding across various sectors like finance, energy, healthcare, and research [3] - The industry is evolving from initial LLM training to Retrieval Augmented Generation (RAG) pipelines and agentic AI [3] - Vulture is positioned as an alternative hyperscaler, offering cloud infrastructure with 50-90% cost savings compared to traditional providers [4] - A new 10-year cycle requires rethinking infrastructure to support global AI model deployment, necessitating AI-native architectures [4] Vulture & DDN Partnership - Vulture and DDN share a vision for radically rethinking the infrastructure landscape to support global AI deployment [4] - The partnership aims to build a data pipeline to bring data to GPU clusters for training, tuning, and deploying models [4] - Vulture provides the compute infrastructure pipeline, while DDN offers the data intelligence platform to move data [4] Scalability & Flexibility - Enterprises need composable infrastructure for cost-efficient AI model delivery at scale, including automated provisioning of GPUs, models, networking, and storage [2] - Elasticity is crucial to scale GPU and storage resources up and down based on demand, avoiding over-provisioning [3] - Vulture's worldwide serverless inference infrastructure scales GPU resources to meet peak demand in different regions, optimizing costs [3] Performance & Customer Experience - Improving customer experience requires lightning-fast and relevant responses, making time to first token and tokens per second critical metrics [4] - Consistency in response times is essential, even with thousands of concurrent users [4] - The fastest response for a customer is the ultimate measure of customer satisfaction [4] Data Intelligence Platform - DDN's Exascaler offers high throughput for training, with up to 16x faster data loading and checkpointing compared to other parallel file systems [5] - DDN's Infinia provides low latency for tokenization, vector search, and RAG lookups, with up to 30% lower latency [5] - The DDN data intelligence platform helps speed up data response times, enabling saturated GPUs to respond quickly [6]

Product Overview - Infinia is a software-defined, next-generation data intelligence platform designed to support both structured and unstructured data, integrating diverse data sources into a unified platform [1] - Infinia aims to reduce complexity in AI deployments and can be deployed on various hardware configurations, including cloud, data centers, reference architectures, and OEM solutions [1] - Infinia operates on the premise of SLAs for capacity, performance, and resilience, abstracting away traditional storage management complexities like volumes and IOPS capping [1] Key Features and Capabilities - Infinia focuses on simplifying data ingestion with low-latency access, emphasizing data tagging for categorization and retrieval in complex environments with billions of objects [1] - Infinia addresses limitations in existing storage solutions by offering unlimited metadata scalability, allowing for thousands of tags per object to refine data definition [1] - Infinia includes capabilities for filtering data at the edge, enabling efficient transfer of relevant data to the core data center for analytics [2] - Infinia offers a software development kit (SDK) to enable customers and service providers to accelerate their applications by using a high-performance interface into Infinia [2] Performance and Efficiency - Infinia achieved a 22x speedup in an indexing and ingest workload on AWS by integrating Nvidia GPUs and utilizing Infinia's S3 interface, resulting in a 70% reduction in GPU resource consumption [1][2] - The company demonstrated a factor of 600 speedup in certain workloads related to identifying data for training AI models [1] Architecture and Integration - Infinia implements data services on top of a key-value store, providing different views onto the data through protocols like S3, file interfaces, and SQL interfaces [2] - Infinia includes a massively distributed SQL server, allowing native access to data and metadata stored in the key-value store through SQL queries [2]

[Music] I'm Laurel Roseman. I'm vice president of channel and alliances for North America for Nebius. I think the AI market today as we all know is extremely competitive. So to find partners and providers in the GPU space that can really be an advocate for the customer, build the product in the stack the way the customer needs to support their business and be nimble in how you work with them and provide enterprisegrade infrastructure. number one core um core importance there. That is how you're going to win ...