Welcome to this Infinia demo. Today we'll be showing how Infinia can handle all the IO needs in an AI data pipeline. The Infinia architecture can be broken down into sections.Storage services providing enhanced resilience and elastic scale capabilities of the storage itself. The data plane comprised of a key value store as well as the presentation of data to clients via IO protocols. SQL queries of the KV store data and metadata and an SDK to integrate directly with applications and frameworks.And finally, ...

Welcome to this demonstration. Today we'll be showing how Infinia can be used with Apache Spark. Most AI workflows follow a common set of data functions, data collection, pre-processing, tagging, and indexing that form the data preparation stages of a data pipeline.Once the data is ready, it then is used for training and validation stages before finally being deployed once the model has achieved a certain level of predictive accuracy. Infinia is a key component of this workflow. The Infinia architecture shi ...

Performance Comparison - DDN Infinia writes chunks at 0041 seconds (4 milliseconds) per chunk, significantly faster than AWS [6] - AWS object store writes each chunk at 01169 seconds (112 milliseconds) per chunk [7] - DDN Infinia uploads a 628-chunk document in approximately 25 seconds, while AWS takes around 74 seconds [7] - DDN Infinia is approximately 285 times faster than AWS in document upload [7] - DDN Infinia retrieves chunks in 01600 seconds (160 milliseconds) total, averaging 32 milliseconds per chunk [13] - AWS retrieves chunks in 165 seconds, with each chunk taking 331 milliseconds [14] - DDN Infinia is 103 times faster than AWS in total query retrieval time [14] AI Pipeline Impact - With DDN Infinia, an analyst can upload and query an annual report in just 2 seconds [8] - A 30x performance advantage transforms the entire AI pipeline, making documents readily available for AI consumption [9] - Reduced latency with DDN Infinia can save significant time, potentially turning a 5-minute research task into 3 seconds [15] - Latency compounds across multiple users and sessions, impacting GPU economics and overall productivity [15]

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]

Infinia Platform Overview - Infinia is a data intelligence platform, not just a traditional storage product, offering S3 object interface, CSI, Cinder, and file system interfaces [1] - The platform is designed for large-scale deployments, already tested at almost an exabyte in size across approximately 1,000 nodes [1][2] - Infinia is a pure software product that can be integrated into the cloud, with a system already running at GCP for testing [2] Key Features and Capabilities - Infinia supports extensive metadata tagging, allowing tens of thousands of metadata attributes per object for enhanced data discovery and enrichment [1][3] - The system is highly multi-tenant, enabling service providers to manage large-scale systems efficiently while providing SLAs for individual end-users [1][2] - Infinia offers quality of service (QoS) at the application level, allowing prioritization of performance for critical tasks [2] - The platform supports online upgrades, capacity expansion, and reduction without downtime [2] Data Intelligence and AI Workloads - Infinia can serve as a Lakehouse on-premise, providing object, block, and parallel file system access to the same data [2][3] - Remote bucket support allows Infinia to pull metadata from existing data sets, enabling querying and caching of data from external sources [2] - Native library support and SDK can provide up to 10x performance improvement for data ingestion with frameworks like Spark [3] Performance and Scalability - The system is designed for very large scale, with deployments reaching almost an exabyte of capacity [1][2] - Infinia can deliver millions of object operations per second with single-digit millisecond latency [4] - Client-side data reduction and erasure coding eliminate east-west traffic, improving overall performance [3] Resilience and Availability - The system demonstrated high resilience by maintaining operation with only a short IO delay (10-15 seconds) after an entire rack of 480 drives went offline [4]

Core Message - AI is essentially HPC (High Performance Computing), emphasizing the importance of data in both fields for extracting intelligence and value [1] - DDN (DataDirect Networks) provides the "rocket fuel" or data intelligence infrastructure that enables better, faster, and more accurate insights from massive datasets in real-time [1] - Data intelligence is critical for AI transformation, enablement, and acceleration, requiring the unification, curation, and analysis of distributed data from various sources [1] Challenges and Solutions - Current challenges hindering AI acceleration include GPU scarcity, limited data center space, and insufficient power; a data intelligence framework is needed to alleviate these issues [2] - DDN's solutions focus on delivering more capabilities from existing GPUs, shrinking data center footprint, and lowering power consumption [2] - DDN accelerates data ingestion, freeing up GPU cycles, and optimizing networks to reduce time to insight and enhance value [2] DDN's Technology and Positioning - DDN is the only data intelligence platform deployed internally at NVIDIA, and also supports massive deployments like XAI with over 100,000 GPUs [1] - DDN's new technology, Infinia, is a high-performance, multi-tenancy data intelligence platform that supports multiple protocols and minimizes data movement [2] - DDN's solutions maximize the value from infrastructures deployed at scale in data centers and the cloud, benefiting both HPC and AI applications [3] Market Impact and Growth - DDN powers more than half a million GPUs and has deployments at the exobyte level, demonstrating significant growth and scale [3] - DDN's ability to solve challenges at massive scale translates to bulletproof stability and cost-effectiveness across a broad range of installations [3] - DDN aims to accelerate scientific and business outcomes by handling data at the edge, in data centers, and in the cloud [3]

AI Model Efficiency & Adoption - DeepSync's approach highlights opportunities for significantly more efficient AI models than previously thought [1] - This efficiency is accelerating the adoption of AI across various sectors [1] Product Development & Integration - The company has launched R1, indicating a new product or platform [1] - R1 is designed to interact with Infinia data intelligence layer to solve problems [1]

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]

Solution Overview - DDN provides a one-click high-performance RAG (Retrieval-Augmented Generation) pipeline for enterprise use, deployable across various environments [1] - The RAG pipeline solution incorporates NVIDIA NIM within the NVIDIA Nemo framework, hosting embedding, reranking, and LLM models, along with a MILV vector database [2] - DDN Infinia cluster, with approximately 0.75 petabytes capacity, serves as the backend for the MILV vector database [3] Technical Details - Infinia's AI-optimized architecture, combined with KVS, accelerates NVIDIA GPU indexing [3] - The solution utilizes an NVIDIA AI data platform reference design to facilitate the creation of custom knowledge bases for extending LLM capabilities [4] - The one-click RAG pipeline supports multiple foundation models for query optimization [7] Performance and Benefits - Integration between DDN Infinia and NVIDIA Nemo retriever, along with NVIDIA KVS, results in faster response times, quicker data updates, and rapid deployment of custom chatbots [9] - The RAG pipeline enables detailed and accurate responses to specific queries, as demonstrated by the Infinia CLI hardware management features example [8][9]

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]