AI technologies are advancing rapidly. Generative AI and large language models are being increasingly applied across industries. As a result, the global data center market is experiencing a new wave of growth. Data shows that the global data center market reached $219.23 billion in 2023. It is projected to grow from $242.72 billion in 2024 to $584.86 billion by 2032, with a compound annual growth rate (CAGR) of 11.6%. This trend is driving higher demand for storage technology and computing efficiency among enterprises worldwide. At the same time, it presents new challenges for the storage architecture of future data centers.
The Future Challenges and Opportunities of Data Centers
Exponential Data Growth:
With the advancement of generative AI, data centers must handle exponentially increasing volumes of data. Every step, from model training to real-time inference, demands efficient storage and rapid access. Traditional storage architectures can no longer meet these needs.
Storage Performance Bottlenecks:
Data centers now face higher demands for data throughput and latency. Particularly during AI training and inference, managing massive data read/write and computation tasks has become urgent. These tasks must be completed within limited timeframes.
Energy Efficiency and Sustainability:
Data centers consume a lot of energy. They face the challenge of reducing energy consumption. Improving computational efficiency is also essential. Future storage devices must deliver better performance. They must focus on energy improvement. This includes achieving higher performance per watt (IOPS/Watt) and lowering total cost of ownership (TCO).
Adaptability to Diverse Markets:
In mature markets, high-performance and large-capacity storage architectures are needed to support complex AI scenarios. In emerging markets, cost-effective and energy-optimized solutions are essential. Meeting diverse demands is fundamental to the global expansion of intelligent computing centers. Enterprises must enhance performance while optimizing energy efficiency to meet the global push for sustainability.
At the same time, market diversity across regions places higher adaptation requirements on storage technologies. In mature markets like North America and Europe, data centers need high-performance storage solutions. Emerging markets like Asia-Pacific and the Middle East have specific needs. They demand highly reliable and energy-efficient storage. These needs tackle the broad challenges and opportunities brought by AI technologies.
Western Digital Introduces AI Data Lifecycle Framework, Breaking Down Storage Requirements Layer by Layer
Western Digital’s AI Data Cycle framework divides AI data processing into six stages. These include data collection, preparation and transformation, model training, inference, interaction and prompting, and new content generation. These stages span the entire AI development and application process. Each stage has independent yet interconnected storage performance requirements. These requirements cover multidimensional needs like high throughput, low latency, and large capacity.
The Deputy President of Western Digital stated, “The surge of artificial intelligence is driving industry transformation at an unprecedented pace. The resulting exponential growth in data volume presents new challenges. Additionally, increasingly complex computational workloads pose new challenges for users building the next-generation data centers. Western Digital deeply understands the critical role data storage plays in accelerating AI innovation. Through the AI Data Cycle framework, it helps users build advanced data storage infrastructures. This accelerates the deployment of AI features and applications.”
In the data collection and archiving stage, enterprise-class HDDs (eHDDs) offer high capacity. They are cost-effective and highly reliable. eHDDs lay the foundation for large-scale expansion in data centers. During the data preparation and transformation stage, storage demand gradually shifts from HDDs to enterprise-class SSDs (eSSDs). This shift supports complex data processing. It also supports vectorized workloads. The next model training stage places even greater demands on storage performance. SSDs with high performance and low latency become critical to meet the needs of GPU computing environments. These SSDs also support hybrid data lakes for real-time inference, interaction, and prompting. Finally, in the new content generation stage, the cycle begins again with the data generated. This provides higher-quality inputs for future AI model training.
Each stage involves different infrastructure, computing, and storage requirements. They also have distinct workload characteristics. Yet, all are integral components of the AI data cycle. Western Digital recommends the high-capacity Ultrastar DC HC690 UltraSMR HDD for the first stage. This recommendation addresses the differentiated storage needs across these stages. This drive is made for massive data storage demands in hyperscale cloud and enterprise data centers. It is ideal for AI workflows that need large-scale storage and strict TCO improvement.
For the second stage, Western Digital suggests the Ultrastar DC SN655+ enterprise SSD with PCIe Gen 4 interface. It integrates multiple software features and functions tailored to AI use cases.
For stages three, four, and five, Western Digital has launched its first enterprise-class PCIe Gen 5.0 solution—the SanDisk DC SN861 NVMe SSD. With exceptional performance, ultra-high capacity, and energy-efficient design, it is the ideal choice for AI-centric scenarios.
Performance Enhancement: Utilizing the PCIe Gen 5.0 interface, the SN861 delivers 3x higher random read performance compared to the earlier generation. This upgrade significantly reduces latency during model training. It also enhances inference processes. It is a perfect fit for generative AI and real-time inference tasks.
High Storage Capacity: With support for up to 16TB of storage, it ensures the processing of large-scale datasets. This feature meets the high-density storage demands of data centers.
Energy Efficiency Improvement: Achieves higher IOPS per watt with lower power consumption. This not only reduces total cost of ownership (TCO), but also contributes to green computing goals.
Certification and Compatibility: Western Digital’s enterprise-grade SSD SN861 E1.S has been certified for the NVIDIA GB200 NVL72 system. This certification ensures stable operation on NVIDIA’s high-performance computing and AI design platform GB200 NVL72. This demonstrates its broad adaptability across AI computing platforms, making it a reliable choice for global data centers.
Western Digital offers a comprehensive portfolio of enterprise storage solutions. This includes high-performance PCIe Gen 5 SSDs like the SanDisk DC SN861 NVMe SSD. These solutions offer the capacity, performance, energy efficiency, and cost advantages required for next-generation AI workloads. They empower users to confidently meet the evolving storage demands of AI advancements. Users can unlock the full potential of their data.
The AI-driven data storage demand is continuously expanding, and the market size of intelligent computing centers will also keep growing. Intelligent computing centers are an upgraded version of data centers. They are designed to meet the computational needs brought by the rapid development of AI technologies. The centers target more complex and advanced application scenarios. Western Digital’s AI Data Cycle Framework is vital in the current market. SanDisk DC SN861 NVMe SSD is equally important. They will play a crucial role in the future markets. They will also become the core driving force in leading intelligent computing centers towards a smarter and more sustainable direction.