AI’s Biggest Bottleneck: Storage Constraints

Key points

• Storage capacity has emerged as a major constraint for AI data centers, with hard-drive lead times ballooning to over a year and enterprise flash storage expected to see shortages and price increases.
• AI inference is driving the demand for storage, with the need for real-time data access causing both hard disk drive (HDD) and solid-state storage (SSD) suppliers to increase their high-capacity options.
• SSD manufacturers are increasing QLC SSD production, but AI workloads will also expand, leading to tight supply conditions for enterprise SSDs by 2026, which may impact Microsoft’s Azure and other cloud services that rely on high-capacity storage.

As we continue to see an explosion in AI inferencing, storage capacity has become a major concern for data centers. Experts predict that hard-drive lead times will continue to grow, with some estimates suggesting that they will exceed a year. This is being driven by the increasing demand for real-time data access, which is causing both HDD and SSD suppliers to increase their high-capacity options. Microsoft, as a leader in the tech industry, is likely to be impacted by these changes, particularly in regards to their Windows Server and Azure products.

According to Constellation analyst Chirag Mehta, "AI inference isn’t just a GPU story, it’s a data story." This means that the focus is shifting from just processing power to the ability to handle large amounts of data. Dell’Oro Group projects that the storage drive market will grow at a CAGR of over 20% over the next five years, with both HDDs and SSDs playing distinct roles in AI infrastructure storage. Microsoft will need to adapt to these changes in order to continue providing high-quality services to their customers.

The TrendForce report highlights the importance of AI inference in driving storage needs. As AI models become more complex, they require access to vector databases and other data sources, which increases storage requirements during inference. This is leading to a shortage of high-capacity hard disk drives, with lead times surging from weeks to over a year. Azure, as a major cloud service provider, will need to find ways to address these shortages in order to continue providing reliable services to their customers.

To address these shortages, data center managers and storage buyers are being advised to prepare by treating SSD procurement like they do GPUs. This includes multi-sourcing, locking in lanes early, and engineering to standards so that vendor swaps don’t break the data path. Microsoft, as a major player in the tech industry, will need to work closely with their suppliers to ensure that they have a steady supply of high-capacity storage.

In the future, we can expect to see a mix of SSDs and HDDs being used in data centers. QLC SSDs, which offer high capacity and low power consumption, are becoming more popular for AI workloads. However, HDDs will still be used for cold storage, where low costs are more important than high performance. Microsoft’s Windows Server will need to be able to handle these different types of storage in order to provide the best possible performance for their customers.

As the demand for storage continues to grow, Microsoft and other companies will need to plan ahead to ensure that they have the necessary capacity to meet their needs. This includes building inventory now, qualifying multiple vendors, and starting early. By taking these steps, companies can reduce the risk of supply chain disruptions and ensure that they have the necessary storage to support their AI workloads. With the Azure cloud service and Windows Server products, Microsoft is well-positioned to meet the growing demand for storage, but they will need to continue to adapt to the changing landscape in order to remain competitive.

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