AI continues to reshape infrastructure priorities, driving conversations about how organizations can meet the compute and performance needs of these increasingly powerful applications. However, behind every breakthrough model lies a hidden and growing challenge — storing the massive volumes of unstructured data that AI needs, consumes and creates.
Everyone talks about GPUs, but only a few are talking about where all that data comes from and ends up. From training sets and model checkpoints to inference logs and telemetry, AI pipelines generate hot, warm and cold data that flash alone simply cannot manage economically or sustainably. The reality is that hard disk drives (HDDs) play a critical role in AI infrastructure, serving as the backbone of long-term, high-volume storage that works in concert with flash.
The Reality of AI Workloads: It’s Not All Real-Time
Massive amounts of data, often reaching petabyte scale, power AI models, providing the data intelligence needed to enable quick, accurate decisions in the moment and at scale. AI consumes and generates huge volumes of data throughout every stage of the AI data life cycle. The more the data, the better the result. This includes data preparation and ingestion, model training, inference and prompting and inference engine and new-content generation — making AI dependent on storage solutions with varying features and functionalities.
Capacity, resiliency, scalability, $/TB, kW/TB and quality and performance at scale are all critical factors. Much of AI data is either write-once and read-later or write-heavy during specific phases, such as training or telemetry logging. This data is often retained for various reasons ranging from compliance to model retraining, snapshot capture and future auditing — all with their own requirements. Not all data needs high-performance flash. In fact, flash-forward AI platforms must pair fast tiers with more cost-effective ones.
Cloud and Hyperscale Environments Rely on HDD
Today, nearly 80% of cloud data runs on HDDs — and AI workflows should be no different. High-capacity, cost-efficient HDD solutions provide the foundation for today’s extensive unstructured big data and data lakes that store the massive volumes of datasets that are used to train models. This data comes from raw data archives, video content, object storage, system logs, metadata and backups, with the majority of data most likely stored in the cloud. As a result, every AI application needs smart, scalable and affordable capacity — and that’s where HDDs continue to shine. Some of the benefits of HDDs are as follows:
- Affordability
Cost per terabyte is critical when building a dynamic storage environment for AI applications. You don’t want to just throw all data on flash. That would be a waste of resources and budget. Most AI data is warm or cold, making high-capacity HDDs the ideal choice for storing it cost-effectively and at scale. According to research conducted by Western Digital, HDDs have a 6x acquisition cost advantage over flash storage — especially in high-capacity at-scale environments. Ongoing innovations in architecture allow HDDs to continue to deliver more value per terabyte, driving TCO down over time as HDD capacities increase.
- Performance
It’s easy to fall into the trap of comparing performance in a relative rather than an absolute sense. The truth is that HDDs are high-performance storage devices, suitable for a wide range of workloads in the AI data cycle. The TCO tradeoff is one of speed versus cost and not about overpaying for performance you don’t need. For example, if you want to move the contents of a four-bedroom house across the country, it doesn’t make logical or economic sense to ship your belongings by air; you ship them via a moving company using trucks and/or rail transport. But truck or rail is fast transport; this isn’t a comparison between airplanes and covered wagons on the Oregon Trail. HDDs offer a combination of performance and value that fits most workloads.
- Innovation
HDD solutions are constantly evolving to provide higher capacities, better performance and more value for organizations. This includes recording system innovations such as energy-assisted magnetic recording (EAMR) and shingled magnetic recording (SMR), and mechanical innovations such as helium-filled HDDs consisting of up to 11 disks in a 3.5” HDD form factor — all technologies that continue to push density, performance and efficiency. In the future, the widespread use of heat-assisted magnetic recording (HAMR) will drive HDD capacities even higher.
Storage Isn’t an Either/Or Proposition — It’s Layered
AI applications have extensive storage requirements, but it’s extremely unwise to simply throw everything on the latest and greatest high-performance flash solutions. SSDs have their place, but HDDs continue to form the backbone of long-term, at-scale storage, allowing organizations to meet their high-capacity needs in the most effective and cost-efficient manner possible. Today’s HDD solutions are not legacy technology — they are the constantly evolving workhorse of data infrastructure, built to manage the massive, growing storage demands of AI workloads now and into the future.
