Core Functionality - Exoscaler data intelligence platform supports multi-tenancy by leveraging VLANs and secure data partitions [1][2] - Client access controls prevent unauthorized data access, enhancing security [2] - Capacity management controls via quotas allow flexible space allocation to tenants [2] Technical Implementation - Network configuration utilizes VLANs with paired IP addresses for intracluster networking and tenant connections [3] - Each tenant maps to two IPs for multiple connections to each VLAN, ensuring high availability [3] - Multi-tenancy is enabled via EMF settings and synced across the cluster [4] - Clients without registered IPs on the appropriate VLAN lose system access due to VLAN isolation [5] Quota Management - Hard quotas enforce strict limits, preventing tenants from exceeding allocated capacity, ensuring total capacity of all tenants never exceed the cluster's capacity [7][9] - Soft quotas allow tenants to use shared capacity by overallocating quotas, potentially leading to less waste but requiring trust [7][10] - Hybrid approach combines soft and hard quotas, providing leeway while preventing excessive consumption of free space [11][12] Data Handling - The system supports on-the-fly quota adjustments while serving data to clients [9] - Demonstrated the creation of a 10 TB (Terabyte) test file to illustrate quota enforcement [8]

The fastest AI isn’t just about GPUs. It’s about removing I/O bottlenecks that slow your business objectives down. Discover how DDN Infinia and Oracle Cloud Infrastructure (OCI) are redefining AI performance. Join this Ask the Experts session to learn how Infinia’s high-performance, S3-compatible storage along Oracle’s powerful and scalable Cloud Infrastructure deliver ultra-low latency, massive throughput, and linear scalability for the most demanding AI workloads. What you’ll learn: - The biggest challeng ...

I mean the other thing I think is extremely enabling is the CUDA ecosystem which you fostered and nurtured and and and helped really people embark on now with CUDA OBJ I think it is opening all kinds of possibilities because people can now tie into this and apply it combination of Kudo OBJ NIMS you know the inference part of it for specific industries life sciences financial services autonomous driving and so on and so forth you take all these things you tie together with the advances that will be made in t ...

Benchmarking & Performance Evaluation - MLPerf and IO500 are trusted, third-party benchmarks that provide clarity for making informed decisions about AI and HPC infrastructure [1] - These benchmarks simulate real-world workloads to measure speed, scalability, and efficiency [1] - The session aims to equip decision-makers with the knowledge to evaluate storage solutions for AI and HPC environments confidently [1] Key Learning Objectives - Identify the most relevant benchmark results for AI & HPC decision-makers [1] - Understand what MLPerf and IO500 tests entail and their significance [1] - Translate performance and scalability metrics into tangible business outcomes [1] DDN's Position - DDN demonstrates leadership in AI performance, offering benefits to users [1] Expertise - The session features technical experts from DDN, including Joel Kaufman, Jason Brown, and Louis Douriez [1]

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]

Uh this is really interesting. You know our journey started out in high performance computing and high performance computing training foundation models and frontier models. That is the extreme version of high performance computing.And so so Nvidia and DDN um found our friendship and our long-term partnership through that origin. Now what we'd like to do as we train these models, these models can now uh turn the world's enterprises, all of our data from raw data into data intelligence. Yeah.And so now our wo ...

Fraud Detection Challenges - Financial institutions lose billions of dollars to fraud annually [1] - Fraud evolves and legacy systems struggle to respond effectively [1] - A single breach can damage trust and lead to regulatory issues [1] DVN's Solution - DVN offers speed, scale, and intelligence for real-time threat detection [2] - DVN analyzes massive data flows to expose hidden patterns [2] - DVN empowers decision-makers with clarity and confidence [2] Value Proposition - DVN helps institutions prevent fraud [2] - DVN ensures institutions are defining the future [2]

For centuries, financial institutions have been the backbone of global economies. They built trust, powered progress, and shaped the world we live in. In highfrequency trading, speed isn't just a competitive edge.It's survival. Markets move in microsconds. Opportunities vanish before human eyes can react.Every delay, every missed signal, every nancond of latency can ripple into loss. DBN delivers ultra low latency, massive throughput, and real-time responsiveness, enabling financial institutions to trade at ...

Technology & Innovation - Omniverse is considered a phenomenal technology [1] - Digital twins in Omniverse can accelerate drug development [1] - Omniverse can maximize the likelihood of success and compress the time to take a drug to market [1] Pharmaceutical Industry Application - Developing a drug can cost billions of dollars and take years [1] - Omniverse can help explore multiple avenues in parallel for drug development [1] - Combining attributes from different experiments in Omniverse can maximize benefits [1]

Autonomous Vehicle Industry Challenges - Autonomous vehicles make hundreds of life-or-death decisions every second, but these decisions are limited by the quality of the underlying data [1] - The challenge lies in scattered information across systems, processing delays, and the disappearance of critical insights, not in sensors or algorithms [1] DDN's Solution and Benefits - DDN unifies vehicle sensor data, training simulations, and real-world testing into one intelligent stream [2] - DDN's infrastructure is built for split-second decisions, enabling faster model training on complete datasets [2] - Vehicles make smarter choices with full context, accelerating development and multiplying breakthroughs [2] - DDN is building the backbone that makes intelligence possible in autonomous vehicles [2] Impact and Future - Breakthroughs in autonomous vehicle technology mean fewer accidents and more lives protected [3] - The autonomous future is happening right now [3]