Data Storage for AI - Meeting the Demands of Modern AI Workloads with Open-E

AI is not only a Large Language Model, but most importantly, data. Learn how Open-E JovianDSS provides the hardware-agnostic, ZFS-based foundation needed to architect for petabytes of high-value, unstructured data.

Non-Negotiable Requirements for AI Workloads Explained

Pre-defined data storage solutions fail when scaling past the pilot phase. To ensure a return on investment for expensive AI workloads, your data storage system must deliver absolute fidelity and operational resilience. See how Open-E JovianDSS can help you ensure:

  • Absolute Data Integrity: End-to-end cryptographic verification and self-healing.
  • Predictive Uptime: Real-time I/O telemetry and proactive component replacement.
  • Massive Scalability: 128-bit file system foundation for near-limitless capacity.
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File size: 16.4 MB

250 ZB

 estimated global amount of data generated by artificial intelligence, to be reached by 2026.

Technical Autonomy for Data Architects

From Zvol blocksize mastery to metadata pinning on ZFS Special Devices, our ultimate guide provides the best practices for optimizing data storage environments for AI infrastructure. Learn how to surgically separate metadata traffic and utilize L2ARC to minimize RAM usage without sacrificing performance.

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