The rapid evolution of artificial intelligence (AI) technologies is precipitating a profound transformation in data storage requirements, highlighting a potential bottleneck in AI advancement due to insufficient memory and storage capacities. This presentation examines the interplay between AI development and data storage technologies, focusing on the growing disparity between their respective growth rates.
Current AI clusters are experiencing a doubling in computing speed approximately every two months, a pace that starkly contrasts with the 3-5 year doubling time of contemporary data storage technologies. This discrepancy is generating significant challenges, as the demand for data storage surges in tandem with the proliferation of AI applications. Notably, recent trends indicate that the price per terabyte (TB) for solid-state drives (SSD), hard disk drives (HDD), and tape storage has increased by 15-35%, driven by the burgeoning appetite for data storage solutions spurred by language-based generative AI models such as ChatGPT.
The demand for data storage is further exacerbated by AI's capacity to generate vast quantities of images and videos, necessitating even greater storage capabilities. AI systems, particularly those involved in training complex models, require fast-access SSDs for efficient data processing and HDDs for mid-term data retention (up to five years) to continuously refine and improve these models. Additionally, long-term cold storage is becoming increasingly critical for AI applications that rely on extensive historical data, such as autonomous driving. Here, training data must be preserved for decades, encompassing development, production, and operational phases.
Beyond autonomous driving, other AI applications, including drug design, healthcare, and aviation, also necessitate long-term data storage solutions. These fields require the retention of vast datasets over extended periods, underscoring the critical need for advancements in storage technology to keep pace with AI's accelerating computational demands.
This presentation aims to shed light on the urgent need for innovative storage solutions to sustain AI's growth trajectory and explores potential avenues for bridging the gap between AI's computational power and data storage capabilities. By addressing these challenges, we can ensure that AI continues to evolve and unlock its full potential without being hindered by storage limitations.
