By Scott Shadley, Director, Leadership Narrative and Evangelist at Solidigm. Solidigm were finalists in the ‘Best Cloud Infrastructure‘ awards at The 2024/25 Cloud Awards.

With the emergence of the artificial intelligence (AI) era, explosive growth in AI applications has catalyzed disruptive innovation across every layer of the tech stack.

This has driven demand for more infrastructure, more power (necessitating more cooling), faster networks, and higher capacity storage.

AI algorithms continue to increase in complexity as AI applications advance in sophistication, permeating all sectors from energy to healthcare; from tech to finance. These factors contribute to a continually escalating mass of generated and raw, unprocessed data, demanding storage solutions that can maintain consistent performance, higher reliability and better energy efficiency while accommodating massive data sets.

Cloud workloads are concurrently growing and becoming more data intensive, presenting a challenge for scaling data pipelines. Many enterprises have efforts underway to migrate to more locally owned solutions or use co-location services, where space and infrastructure are more challenged. In this environment, and as AI, machine learning (ML), and other high-demand workflows continue to proliferate, the requirements for cloud storage are poised to grow exponentially, driving the need for scalable and future-ready solutions.

In this environment of rapid acceleration and exponential growth, scalable, reliable, efficient storage emerges as an imperative. From underpinning AI applications that require petabytes of real-time data access to enabling global data center efficiency, the role of innovative storage solutions has never been more critical. The following imperatives define the storage needs of today’s data consumers:

1) Volume and Location of Data

AI workflows demand immense amounts of data for ingestion, preparation, training, validation, and inference. With the rise of big data technologies, enterprises are amassing and storing huge datasets from diverse sources, including sensors, social media platforms, and IoT devices. Effectively managing, storing, and archiving these massive volumes of data has become a critical requirement in the AI era. Furthermore, storing and acting on incrementally larger amounts of data closer to the source of generation is essential to achieving optimal efficiency. This is spurring the growth of Edge-based AI as well.

2) Speed of Data

Along with mushrooming volumes of data, the speed of data generation and processing continues to increase. Real-time decision-making and analytics for areas like finance, healthcare, and manufacturing depends on high-speed data transfer and processing, requiring storage solutions to handle enormous data sets with minimal latency.

3) High Performance

The computational intensity of AI and ML workloads demands storage that complements advanced compute infrastructures like GPUs. Storage must deliver low-latency access and necessary throughput to support AI workloads and maximize hardware infrastructure investments.

4) Data Efficiency

Efficiency is essential in ensuring that AI solutions achieve optimum performance, control costs, improve energy conservation, facilitate scalability, and strengthen reliability. AI servers devour huge amounts of energy, and AI solutions often struggle with delivering sufficient power to the data center. Therefore, utilizing power- and high-capacity storage solutions can produce sizable benefits to infrastructure efficiency, freeing up the space and power needed to accommodate more compute. Through integration of data-efficient methods, solutions can deliver more effective and impactful AI data storage systems, creating value across a wide range of AI applications.

5) Scalability

As workloads and data grow exponentially, storage solutions must scale seamlessly and within the confines of the available infrastructure. This ensures providers can adapt without significant rearchitecting or cost increases.

The case for high-capacity SSDs

Modern storage devices like NAND-based solid-state drives (SSDs) are the unsung heroes of the tech stack, acting as the foundation that supports everything from basic data storage to high-performance, scalable workloads in AI, cloud computing, and beyond.

SSDs are a type of storage device that use nonvolatile, storage technology called NAND flash memory to store data. Unlike traditional mechanical hard disk drives (HDDs), which use spinning disks and moving read/write heads to access data, SSDs have no moving parts, making them faster, more durable, and more energy efficient. The SSD controller – the vital brain within the drive – manages data storage and retrieval operations, optimizing performance and ensuring data integrity. The NAND flash memory is the persistent data store.

By utilizing modern, high-capacity SSDs, enterprises can activate valuable insights, reveal patterns, and derive actionable intelligence from their massive amounts of data, equipping them to get ahead of the curve and differentiate themselves from the pack.

CoreWeave is one such innovative enterprise. One of the world’s largest GPU cloud providers today, CoreWeave specializes in delivering massive scale Nvidia GPUs on top of the industry’s fastest and most flexible, scalable infrastructure. The company has tapped Solidigm SSDs as a strategic component of its data pipeline, continually scaling capacity based on customer demand for AI training.

CoreWeave’s wholesale shift to these large SSDs enables higher performance and lower latency for critical workloads where the more traditional HDD storage simply cannot satisfy the need.

“One of the secret weapons of CoreWeave is that our software stack is purpose-built to handle…massive GPU training clusters from the initial provisioning to hardware validation through passive and active health checks and all the way through orchestration and scheduling,” said Jacob Yundt, CoreWeave director of compute architecture. “Our cloud is uniquely designed to bring massive amounts of GPUs online as fast as possible.”

CoreWeave relies on Solidigm’s SSD storage solutions to meet the demands of these massive AI training clusters. By aggressively deploying high-capacity SSDs, the company has scaled its storage alongside its compute, ensuring seamless operations for even the most data-intensive workloads without a massive footprint increase.

“Scaling GPU density is only half the battle,” Yundt said. “Without matching storage capacity and performance, our customers can’t reach their potential. Solidigm’s high-capacity drives strike the perfect balance between density, performance, and efficiency, enabling us to deliver groundbreaking AI services.”

The role of storage innovation

Cloud providers and enterprises need innovative SSD technology and critical storage solutions to thrive in this rapidly expanding landscape. Introduction of the world’s largest 122TB SSD enables scalable, sustainable, high-performance cloud infrastructures designed to meet today’s demands and tomorrow’s challenges through pioneering advancements in technology.

These latest innovations provide a data foundation for all cloud and enterprise solutions to deliver flexibility and capacity to meet the largest AI workload challenges. Advances in NAND, controllers, and overall SSD design technology brings resiliency and reliability to businesses not seen before.

“The AI era is placing incredible demand on data pipelines from the data center to the edge, and at Solidigm we’ve watched a sea change in storage capacity, performance, and design requirements as IT organizations keep pace with feeding the compute demand,” said Scott Shadley, Solidigm director of leadership narrative and evangelist.

Why SSD storage is beneficial for AI applications

AI-era data varies according to formats and solutions – it may be video, images, text, sensor data or time-series data; it may be structured, semi-structured, or unstructured.

Compatibility with AI algorithms and solutions requires the underlying data storage systems to support data models of different sizes and handle these varying data types.

SSD storage provides multiple advantages for AI use cases, including performance, low latency, scalability, and operational efficiency.

While SSDs typically require an initial larger capex investment compared to HDDs, they can demonstrate better OpEx cost effectiveness when enterprises consider reliability, performance, and the total cost of ownership (TCO), particularly in high-performance workloads.