DeepSeek large model API hard disk cache technology challenges and optimization of storage systems – Luisuantech

With the transformation of data center resource utilization patterns, traditional CPU-centric architectures consume about 30% of server resources when dealing with network and storage protocols, forming a “data center tax” that limits storage system performance. At the same time, with the development of intelligent computing business, the energy efficiency ratio of general CPU in processing Infrastructure services decreases, further aggravating the problem of resource utilization efficiency.

In the model inference scenario, DeepSeek introduces large model API caching technology, which saves computing resources by caching the token data calculated for the first time to the hard disk and optimizing subsequent output by cache hits. However, traditional storage architectures still face the following challenges:

Local storage limitation: The shortage of PCIe channels in the local computing server limits the mount size of the local SSD hard disk, resulting in limited cache capacity and insufficient read and write speed, affecting the reasoning efficiency of the model.

File System Performance Bottleneck: When a file system is used for mass storage mounting, file system performance problems (such as metadata management, data consistency maintenance, network latency, etc.) of remote storage clusters will reduce storage performance, limit the potential of hard disk caching technology, cause slower data reading speed, and affect the execution efficiency of inference tasks.

Reasoning delay problem: In complex task scenarios, the above problems will lead to too long reasoning delay, affecting the user experience, especially in financial transaction analysis, automatic driving and other scenarios requiring real-time response, which may lead to decision delay and miss key business opportunities.

In response to these problems, LUISUANTECH’s all-flash storage and Light Boat series products break through local storage limitations and file system bottlenecks, becoming the key to improving storage performance. This optimization not only significantly improves the performance of DeepSeek’s large model API caching technology, but also reduces computational costs and improves ROI. The optimized system can handle complex reasoning tasks more efficiently, reduce latency, improve user experience, and bring significant advantages to enterprises in the market competition.

In the context of digital transformation, building an efficient and reliable hard disk cache system is critical. Continuous optimization of storage architecture and file system performance will enable enterprises to meet future challenges and achieve sustainable development.