A growing challenge in AI data storage.
Artificial intelligence is driving breakthroughs across industries, revolutionizing everything from healthcare diagnostics and financial modeling to autonomous vehicles and large-scale automation. However, as AI systems become more sophisticated, the demands on data storage have grown exponentially, creating challenges in scalability, efficiency, and cost.
Machine learning datasets now require petabytes of storage, with some enterprises managing exabyte-scale datasets to keep up with evolving AI models. These massive datasets must be stored, retrieved, and processed efficiently to support model training and inference. The storage infrastructure behind AI is no longer just an IT concern—it has become a core enabler of AI innovation itself.
Despite advances in AI computing, traditional storage architectures have become complex and expensive at the scale required to feed data hungry GPUs, introducing limitations that slow down AI adoption. There are three reasons for this:
First, while SSD-based architectures deliver high-speed performance, their high acquisition costs make them impractical for the massive-scale storage needs of AI training workloads. Retaining large datasets solely on SSDs is financially unsustainable for most enterprises.
Second, while SAS/SATA hard drive systems continue to provide reliable and cost-effective storage for many enterprise applications, AI workloads place unique demands on storage infrastructure. SAS/SATA interfaces rely on proprietary silicon, host bus adapters (HBAs), and controller architectures that were not originally designed for the high-throughput, low-latency needs of AI workloads. As AI adoption scales, these factors can introduce complexity and additional latency, making it harder for AI models to quickly access massive datasets.
Finally, AI workloads that depend on cloud-based storage often experience high WAN data transfer costs, latency spikes, and unpredictable retrieval times. These inefficiencies limit the responsiveness of AI models and increase operating expenses while processing hardware waits for remote data.
Consequently, as AI continues to scale, a new approach is needed—one that complements existing storage architectures while balancing capacity, cost, and speed to support AI training and inference without compromise.
A new approach: NVMe hard drives for AI workloads.
Seagate is pioneering a transformational solution by bringing NVMe technology to high-capacity hard drives. By developing NVMe as a future standard protocol for hard drive connectivity, Seagate provides an alternative designed to optimize AI data pipelines, reducing storage bottlenecks while maintaining the affordability and density advantages of hard drives.
Unlike SAS/SATA-based hard drives, NVMe hard drives remove the need for HBAs, protocol bridges, and additional SAS infrastructure, making AI storage more streamlined. These drives allow AI workloads to scale seamlessly by integrating high-density hard drive storage with high-speed SSD caching in a unified NVMe architecture.
This shift would provide significant advantages. First, by eliminating hardware adapters to interface with the processor, NVMe hard drives simplify AI storage deployment, allowing organizations to build large-scale AI storage environments without specialized controllers. Second, with a single NVMe driver and OS stack, these drives ensure that hard drives and SSDs work together efficiently, removing the need for separate software layers.
One of the most critical benefits is direct GPU-to-storage data access through DPUs, bypassing CPU bottlenecks. Traditional storage architectures route data through CPU-driven pipelines, creating latency issues. NVMe hard drives can eliminate this inefficiency, enabling AI models to ingest and process massive datasets with significantly reduced delays.
Additionally, NVMe over Fabrics (NVMe-oF) enables NVMe hard drives to integrate into distributed AI storage architectures, ensuring seamless scaling within high-performance data center networks. This feature is particularly beneficial for enterprises needing flexible, composable storage solutions for AI workflows.
By using NVMe hard drives alongside SSDs, organizations will be able to optimize cost while maintaining performance, reserving SSDs for active datasets and using hard drives for long-term AI training data retention.
Proving the future: Seagate’s NVMe hard drive proof of concept.
To demonstrate the potential real-world impact of NVMe hard drives, Seagate conducted a proof of concept (POC) integrating NVMe hard drives, NVMe SSDs, NVIDIA BlueField DPUs, and AIStore software, showcasing a high-efficiency AI storage ecosystem.
This POC highlighted key advantages of NVMe hard drives in AI workflows, providing evidence that they can have a significant impact in large-scale AI storage environments:
- The engineers demonstrated that direct GPU-to-storage communication via NVMe hard drives and DPUs helped reduce storage-related latency in AI data workflows.
- Legacy SAS/SATA overhead was eliminated, simplifying system architecture and improving storage efficiency.
- AIStore dynamically optimized caching and tiering, enhancing model training performance while simplifying storage aggregation and scalability to exabyte levels.
- NVMe-oF integration enabled seamless scaling, proving the composability of multi-rack AI storage clusters.
Through this POC, Seagate is demonstrating how NVMe hard drives will be able to support the world’s most demanding AI workloads without requiring all-flash architectures.
Real-world impact: AI storage in action.
Seagate is leveraging its decade of experience deploying AI models in its smart factories to validate NVMe hard drives in real-world AI workloads.
At Seagate’s quantum antenna production facilities, AI-driven defect detection relies on high-speed image ingestion and rapid retrieval for model training and continuous improvement. By applying insights from its own AI-enabled production environments, Seagate is exploring how NVMe hard drives would enable this process by providing scalable, cost-effective storage that supports both real-time processing and long-term retention:
- Mass capacity for storing high-definition images without lossy data compression.
- Efficient long-term storage of AI training datasets.
- Seamless access for AI model retraining and continuous improvements.
By exploring the integration of NVMe hard drives into a storage architecture, Seagate shows how the new technology would reduce AI storage costs while ensuring real-time responsiveness for AI defect detection. The efficiency gains include faster AI-driven analysis, improved accuracy, and lower infrastructure costs.
Beyond manufacturing, NVMe hard drives have applications in autonomous vehicles, healthcare imaging, financial analytics, and hyperscale cloud AI platforms.
Sustainability and cost savings: NVMe hard drive advantages.
AI infrastructure consumes massive amounts of power, making sustainability a growing concern. Seagate’s work with NVMe hard drives explores a cost-effective and energy-efficient alternative to SSD-heavy architectures.
Compared to SSDs, NVMe hard drives would offer:
- 10× more efficient embodied carbon per terabyte, significantly reducing environmental impact.
- 4× more efficient operating power consumption per terabyte, lowering AI data center energy costs.
- Significantly lower cost per terabyte, reducing AI storage TCO at scale.
As AI infrastructure expands, sustainable storage will become a critical factor in reducing both cost and environmental impact. Seagate’s development roadmap includes continued advancements in NVMe hard drive efficiency, with the goal of helping organizations scale AI storage while meeting long-term sustainability goals.
A roadmap for the future of AI storage.
Seagate is developing innovations that will enable the next generation of AI-ready storage infrastructure, aligning with industry trends and the needs of hyperscale and cloud environments.
The roadmap includes:
- Scaling the Mozaic platform (now shipping 36TB drives) to develop even higher-capacity NVMe hard drives.
- Advancing NVMe-oF support, enabling AI workloads to scale seamlessly across hybrid environments.
- Creating reference architectures, ensuring AI developers can deploy optimized storage solutions with ease.
Seagate is working with customers and partners to explore how NVMe hard drives can fit into next-generation AI storage solutions, ensuring enterprises can meet AI storage demands affordably and efficiently.
Seagate’s commitment to the future of AI storage.
AI is transforming industries, yet many organizations struggle with data management complexity and rising storage costs. Scalable, efficient storage is essential to keeping AI innovation moving forward.
Seagate’s work on NVMe hard drives is demonstrating how NVMe connectivity could reduce storage deployment complexity while maintaining the cost and density advantages of hard drives.
By enabling AIStore integration, NVMe-oF scalability, and GPU-optimized storage pathways in its POC, Seagate is leading the next wave of AI infrastructure innovation.
As AI reshapes industries, Seagate is redefining how AI storage infrastructure scales to meet the increasing data storage demand.
