Understanding AI Storage Challenges and SSD Technology

This white paper discusses the challenges associated with data storage in artificial intelligence (AI) and machine learning (ML) environments, particularly focusing on the advantages of solid-state drive (SSD) technology. It outlines the AI processing cycle, which includes data ingestion, preparation, training, and inferencing, highlighting the unique storage requirements at each stage. The paper explains that the increasing size of training datasets necessitates high-capacity and high-performance storage systems to maintain efficiency. It details the importance of low latency and high throughput during model training and inferencing, emphasizing the role of SSDs in meeting these demands. Additionally, the document reviews different SSD technologies, including triple-level cell (TLC) and quadruple-level cell (QLC) flash, discussing their respective use cases and performance characteristics. The paper concludes by asserting that while QLC SSDs are suitable for high-capacity needs, TLC SSDs remain essential for applications requiring low latency and high performance.