Everpure Simplifies Enterprise AI with Evergreen//One for AI and Data Stream Beta

Core Insights - Everpure has launched Evergreen//One for FlashBlade//EXA and the upcoming Everpure Data Stream Beta to facilitate the transition from pilot to production in enterprise AI projects by reducing cost and complexity barriers [1][2] Group 1: Product Offerings - Evergreen//One for AI extends across FlashBlade//EXA, providing the necessary performance, scalability, and throughput for large-scale AI training and inference [2] - The Everpure Data Stream Beta, set to launch later in 2026, automates data movement from ingestion to inference, thereby accelerating time-to-result [2][8] - Everpure's technology allows for global deployment of storage on a pay-as-you-go basis, addressing capacity planning challenges and enabling scalability to meet evolving AI workloads [4] Group 2: Performance Validation - FlashBlade//EXA has achieved the highest score for the SPEC Storage AI_Image benchmark, successfully powering 6,300 simultaneous AI jobs, demonstrating its capability to sustain more concurrent training tasks than any other solution [7] - Recent benchmarks from SPECstorage Solution 2020 and MLPerf validate FlashBlade//EXA's consistent performance in transforming raw data into trained models at scale [5] - The integration of NVIDIA-Certified Storage (NVCS) validation with FlashBlade//EXA provides a foundation for full-stack confidence and aligns with NVIDIA Cloud Partner reference architectures [6] Group 3: AI Economics and Infrastructure - FlashBlade//EXA moves data twice as fast as its closest competitor while occupying less than half a rack of storage, ensuring over 90% GPU utilization across large NVIDIA Hopper clusters [7] - Everpure's platform emphasizes continuous data optimization as a strategy for success in the AI-driven landscape, treating AI readiness as an ongoing journey rather than a one-time upgrade [11] - The collaboration with Supermicro and the NVIDIA AI Data Platform reference design allows enterprises to unlock the true value of their data rapidly [9]