When enterprises move from digital transformation to intelligent reconstruction, storage systems are no longer simple data containers, but the core base for determining whether AI services can run smoothly, quickly and stably. When traditional storage architectures are faced with big model training, real-time reasoning, and multi-modal data processing, there are common pain points that cannot keep up with performance, inflexible expansion, and O & M. The new generation of AI native storage is bringing new solutions to enterprise data infrastructure through architecture innovation and intelligent capabilities.

1. Architecture reconstruction: from passive adaptation to native support, directly hitting the pain points of AI business

the core feature of AI business is large amount of data, miscellaneous access modes, and high performance requirements. Traditional storage is based on the block/file/object separation design, which is difficult to meet the mixed load requirements of multiple scenarios such as training, reasoning, and big data analysis. The breaking point of the new generation of storage is to implement "AI native adaptation" from the underlying architecture ":

• unified Data Plane: a set of storage supports multi-protocol access to blocks, files, objects, and HDFS at the same time. It does not require cross-system data copying and can be directly connected to the big model training framework, database, and big data platform, data is written at one time and shared in multiple scenarios ".
• Parallel IO engine: The distributed all-flash architecture and RDMA network are adopted. A single cluster can provide TB-level aggregate bandwidth and million-level IOPS to solve the problem of slow data loading in large model training, training cards and other data "industry challenges.
• Multimodal data engine: Built-in AI-optimized metadata management can efficiently process unstructured data such as images, videos, text, and model files, and improve metadata query performance by more than 10 times.

Second, performance jump: AI training is no longer "waiting for data", and inference response is smoother

for AI services, storage performance directly determines the model training efficiency and user experience. The next generation of storage has transformed from an I/O bottleneck to a performance engine through collaborative software and hardware optimization:

• training scenario: eliminate data walls: through prefetch cache, intelligent scheduling, and parallel reading technologies, the data loading speed is increased by 5-10 times, and the GPU utilization rate of large model training clusters is increased from 40% to more than 90%, significantly shorten the training cycle.
• Inference scenario: low latency guarantee: Based on all-flash media and intelligent cache policies, sub-millisecond access latency is realized, which can support millions of concurrent inference requests and meet the real-time response requirements of intelligent customer service and recommendation systems.
• Hybrid load isolation: supports service-level QoS control, which isolates the I/O resources of different services, such as training, inference, and databases, to prevent high-load services from affecting the stability of core systems.

3. Elastic expansion: from vertical scale-out to on-demand growth, it is suitable for long-term growth of enterprise data.

The data volume of enterprises is increasing exponentially, while the explosive demand of AI business makes the traditional storage expansion mode more difficult. The distributed architecture of next-generation storage enables synchronous expansion of capacity and performance:

• linear Scalability: supports non-interrupt horizontal expansion. A single cluster can be expanded to thousands of nodes and EB-level capacity. New nodes are automatically added to the resource pool. The performance increases linearly with the number of nodes.
• Hierarchical Storage optimization: based on the frequency of data access, hot data is automatically stored in the high-performance all-flash layer, and cold data is migrated to the low-cost large-capacity layer. This ensures performance while ensuring performance, reduce storage costs by 40%-60%.
• Multi-Site collaboration: supports cross-region Multi-Active and data disaster recovery, enables remote backup and rapid recovery of data, and ensures the continuity of AI training platforms and core business systems in fault scenarios.

4. Intelligent operation and maintenance: enable storage management from "manual fire fighting" to "automatic driving"

as storage clusters grow larger and larger, the costs and risks of manual O & M also increase. The new generation of storage built-in AI O & M engine implements automated management throughout the lifecycle:

• intelligent fault prediction: by collecting device running data in real time and analyzing AI algorithms, potential faults such as disks, controllers, and networks can be identified in advance, and data migration can be automatically triggered to avoid service interruption.
• Performance bottleneck location: automatically analyzes the service I/O characteristics, identifies issues such as hot disks, slow I/O, and queue congestion, and generates optimization suggestions with one click. Administrators can quickly resolve issues without node-by-node troubleshooting.
• Intelligent Resource Scheduling: dynamically adjusts the allocation of storage resources according to the business load, automatically reduces the load and saves energy during off-peak hours, and quickly allocates resources during peak hours to ensure business performance and reduce energy consumption costs.

Conclusion

the rapid iteration of AI technology is forcing the innovation of enterprise storage architecture. The new generation of AI native storage is no longer a passive infrastructure, but a core engine that drives AI business innovation. It builds a future-oriented data base for enterprises through four capabilities: architecture reconstruction, performance jump, elastic expansion, and intelligent operation and maintenance, helping enterprises achieve cost reduction, efficiency improvement, and business breakthrough in the intelligent transformation.