Infinia Platform Overview - Infinia is a software-defined, metadata-driven, containerized, cloud-native data intelligence platform designed for scalability, performance, and efficiency across core, cloud, and edge environments [1] - The platform supports critical data protocols like object and block, integrating with AI data acceleration libraries like TensorFlow and PyTorch [1] - Infinia enhances AI execution engines by serving data in its native form, reducing the need for data conversion and speeding up applications [1] Metadata and Multi-Tenancy Capabilities - Infinia allows for tagging massive amounts of metadata to objects, enabling faster data discovery and processing, with no limitations on metadata capability [1] - The platform has built-in multi-tenancy capabilities, providing SLAs for individual tenants and sub-tenants on capacity and performance, ensuring quality of service [1] Scalability and Cloud Native Design - Infinia is fully containerized, allowing for scale-out at web scale, starting from a few terabytes and scaling to exabytes [1] - The product is designed to be cloud-native and will soon be available in leading cloud provider marketplaces [2] AI Data Challenges and Solutions - Infinia addresses the complexity of managing large amounts of distributed multimodal data across core, cloud, and edge environments by creating a unified platform [1] - It tackles the demand for extremely low latency required to run AI applications, as well as the high costs associated with running AI [1] - The platform ensures data protection at any time and at any scale [1] Performance Metrics - Infinia can deliver time to first byte in less than a microsecond [2] - It can deliver 30 to 40 million objects per second in list object operations [2] - Infinia can deliver terabytes per second throughput at large scale [2] Efficiency and Sustainability - Infinia can achieve 10x data reduction, fitting over 100 petabytes of storage into a single rack [2] - It can reduce the overall data center footprint by a quarter compared to competitors, saving 10x power and cooling costs [2] Security Features - Infinia focuses on security authentication and access control, preventing unauthorized data access [2] - Data is always encrypted, and all actions within the system are audited [2] - The platform provides 99.9999% uptime enabled by reliability-focused features [2] Key Business Outcomes - Infinia aims to reduce complexity and achieve more accurate results on a unified platform for AI inference, data analytics, and model training [2] - It accelerates innovation by running AI apps faster, enabling businesses to beat the competition [2] - The platform enables rapid deployment across the cloud core and the edge to increase productivity, boost efficiency, and maximize ROI [2]

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 ...

Essentially what you're trying to do is you're trying to convert data into information. AI struggle sometimes is it has to read a large swath of multimodel data at all times and convert that to information which could be a small little nugget that you actually need. You don't need to move large swats of data every time you need to do something.We're just going to focus on the metadata layer and that gives you context and converts data into information in real time. Right. So as soon as the data comes in, ho ...

Multimodal AI & Metadata - Multimodal AI is essential for enterprises to truly benefit from AI [1] - Metadata tagging and movement are crucial for the economics of AI to work, given limitations in data center space and power [1] - Metadata attributes are very key to the success of AI initiatives [1] Data Intelligence & Business Value - The core focus should be on a metadatarrier infrastructure that can handle low-latency object transformation to gain insight [2] - The ultimate goal of bringing data into the environment to train models is to gain data intelligence [2] - AI initiatives must result in business value for enterprises and leisure value for consumers [3]

Core Technology - The company utilizes accelerated computing and artificial intelligence to learn from data [1] - The company transforms raw data into data intelligence [1] - The company embeds intelligence into models and extracts semantics, intelligence, and information from data [2] - Instead of serving raw data, the company serves metadata, knowledge, and insights [2] - The semantic layer of data is extremely compressed [2]

AI Infrastructure and Architecture - Infinia was conceived due to the need for a different architecture for AI, one that scales efficiently for training, has low latency, is distributed on-premise and multi-cloud, and minimizes data movement [1] - The industry is shifting towards Data Intelligence, reframing storage of raw data into informational form, which is a new opportunity for DDN to provide data intelligence for enterprises running AI [1] - Metadata and tagging are essential for multimodal AI, enabling the movement of metadata and making the economics viable due to the compression ratio [1] AI Application and Adoption - Enterprises need to adopt AI at an accelerated pace, requiring the application layer to be supercharged and the infrastructure to be efficient [1] - The industry is moving from high-performance computing to Enterprise, and then to digital twins of Enterprise, enabled by technologies like Omniverse [2] - AI is enabling companies to create digital twins, allowing them to run thousands of experiments simultaneously and optimize outcomes, applicable to enterprises, governments, and individuals [2] AI Model and Ecosystem - Post-training, which involves problem-solving and reasoning, is a crucial and compute-intensive part of intelligence, following pre-training [3] - The release of open-source reasoning models like DeepSeek's R1 is accelerating AI adoption by highlighting opportunities for more efficient models [3] - The CUDA ecosystem is enabling the application of AI in specific industries like Life Sciences, Financial Services, and autonomous driving [3] Strategic Partnership and Future Vision - The partnership between Nvidia and DDN is expanding from supercomputing to Enterprise and Omniverse, with Infinia playing a key role [4] - Companies should both use public cloud AI and build their own specialized AI, curating AI agents from various sources to solve large problems [3] - Differentiation for organizations comes from specialized application of AI, enabled by technologies like Nvidia's Nims and DDN's Infinia [4]

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]