delivers 10 TB/s aggregate bandwidth and 100 million IOPS. In addition, it reduces five-year TCO by 70% compared with traditional TLC-based solutions, accelerating model innovation for AI cloud providers and intelligent computing centers.

Limitations in Traditional AI Storage Architectures.

The explosive growth of AI is fundamentally transforming enterprise computing and storage requirements. Large-scale AI model training features highly read-intensive workloads that require tens of thousands of GPUs to concurrently access exabyte-scale datasets with sub-millisecond latency. Traditional storage architectures now face three major challenges:


Separated Data Silos:Traditional ETL processes require data to be moved from object storage to parallel file systems before training, resulting in time-consuming physical data migration. IDC research indicates that data teams spend 81% of their time on data preparation, slowing business iteration.

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