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]

Product Overview - Infinia is a ground-up developed product intended as more than just an object store, offering multiple data interfaces [1] - The system provides database access through an SQL interface and can be used as a notification bus, critical for AI workflows [2] - It is a pure software product designed to solve complicated problems, making it attractive for service providers [3] Key Features & Capabilities - Built-in architectural capabilities around multi-tenancy provide technology as a service [4] - Deep integration capabilities through an SDK enable customers to accelerate applications and AI workflows [4] Ecosystem & Partnerships - DDN has ongoing partner development with ecosystem partners to accelerate technology adoption through the Infinia SDK [4]

AI Infrastructure Challenges - AI workloads (inference, training, RAG) competing for resources can cause performance bottlenecks and delays [1] - Mixed-tenant AI loads can lead to noisy-neighbor issues, impacting performance [1] Solutions & Benefits - Next-gen AI infrastructure provides full control over the environment, regardless of workload complexity [1] - Dynamic resource isolation prevents noisy-neighbor issues [1] - Efficient scaling of AI infrastructure while maintaining performance is achievable [1] Key Learning Objectives - Guarantee performance under heavy, mixed-tenant AI loads [1] - Prevent noisy-neighbor issues with dynamic resource isolation [1] - Scale AI infrastructure efficiently while maintaining performance [1]