Artificial Intelligence has outpaced conventional enterprise IT, demanding petabytes of data delivered at terabytes per second to keep GPUs productive. The FlashBlade//EXA architecture is specifically engineered to solve this “starvation” problem by disaggregating metadata from data nodes. By using a scale-out metadata cluster, the system allows metadata and data capacity to scale independently, ensuring that billion-file training runs do not bottleneck at the directory level.

Readers will find this technical brief indispensable for understanding how to build a high-performance computing (HPC) environment that avoids vendor lock-in. It explains the transition to standards such as pNFS and FlexFiles, which enable standard enterprise operating systems to access parallel storage without custom vendor software. For AI leaders, the primary takeaway is performance: FlashBlade//EXA has demonstrated read throughput of more than 2.7 GB/s per GPU.

• Massive throughput: Capable of delivering over 5TB/s of read throughput in a 60-node configuration, meeting the needs of the largest GenAI models.
• Low latency metadata: Utilizes distributed key-value databases to deliver up to 20 times the metadata performance of traditional scale-out architectures.
• Storage efficiency: Uses DirectFlash software to manage flash media directly, eliminating the write amplification and performance lag of off-the-shelf SSDs.
• Flexible data nodes: Allows users to repurpose existing commodity x86 servers as data nodes, reducing the total cost of ownership for AI infrastructure.