AI Perspectives

AI depends on mass-capacity data storage.

Storing and activating mass data is critical to the next wave of AI innovation.

Maximising AI's potential takes a balance.

App developers are racing to train and deploy AI models. The focus has been on finding the right data and ramping up computing power. As AI models and applications proliferate, figuring out how to store the massive amounts of exabytes they will generate becomes an urgent challenge. Supporting AI workloads requires a mix of important memory and storage technologies across the AI data workflow. But ultimately, AI at scale requires hard drives.

A spectrum of memory and storage enables AI.

Feeding AI the data it needs to learn, create, and improve requires a broad range of storage technologies. From high-throughput memory to high-capacity hard drives, determining the right storage mix for any AI workload is about balancing the need for performance, cost and scalability.

Most AI data ends up on hard drives.

AI compute clusters train, run, and optimise language models. GPUs, CPUs, NPUs, and TPUs are closely coupled with high-performance memory devices, offering terabyte- and even petabyte-per-second throughput for extreme computation. The input and output data they use and create flows into networked storage clusters where it’s preserved long-term, mostly on hard drives, to support future re-training, quality control and compliance.

The world’s largest data centres choose hard drives.

Today, the most advanced AI innovators also operate the world’s largest hyperscale and cloud data centres. These companies choose to store 90% of their online exabytes¹ on hard drives because they understand the unique price-to-performance value that hard drives offer for mass-capacity storage. Though SSDs are also a critical technology, hard drives will continue to store the majority of data as more AI-optimised architectures are deployed.

Why hard drives are ideal for AI at scale.

The data explosion of the AI era demands mass-capacity storage that scales. While solid-state drives (SSDs) are important for low-latency workloads, AI at scale runs on hard drives. Three key factors explain why.

Economies of scale.

On a cost-per-TB basis, hard drives are 6× more efficient to acquire than SSDs, making them the logical choice for AI data storage buildouts.²

Exabyte production at scale.

Measured in capital expenditure, hard drive manufacturing is 9× more efficient than the NAND industry.³

Sustainability at scale.

Hard drives use 4× less operating power per terabyte and have 10× less embodied carbon than SSDs.⁴

Hard drives are the backbone of AI data storage.

By supporting the entire AI data workflow, hard drives play a crucial role in validating AI models.

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Storage and compute

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Seagate's analysis of IDC's Multi-Client Study, Cloud Infrastructure Index 2023: Compute and Storage Consumption by 100 Service Providers, November 2023.
Seagate’s analysis based on Forward Insights Q323 SSD Insights, Aug. 2023; IDC Worldwide Hard Disk Drive Forecast 2022-2027, Apr. 2023, Doc. #US50568323; TRENDFOCUS SDAS Long-Term Forecast, Aug. 2023.
Using total embodied carbon with a 5-year lifecycle.
Sara McAllister et al., “A Call for Research on Storage Emissions,” Hotcarbon.org, 2024.

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AI depends on mass-capacity data storage.

Maximising AI's potential takes a balance.

A spectrum of memory and storage enables AI.

Most AI data ends up on hard drives.

The world’s largest data centres choose hard drives.

Why hard drives are ideal for AI at scale.

Hard drives are the backbone of AI data storage.

Discover more about AI and data storage.

Explore related resources.