Written by Jeff Janukowicz, IDC Research Vice President

By leveraging flash data storage, companies can efficiency unlock valuable insights, uncover hidden patterns, and extract actionable intelligence from their data, enabling them to stay ahead of the curve and differentiate themselves in the market.

The top 4 AI storage imperatives 

AI infrastructure is complex. While the compute infrastructure of CPUs and GPUs is a vital component of AI solutions and has been the focus infrastructure investments up until now, data storage is also a foundational pillar of the hardware infrastructure for the AI era. The widespread adoption of artificial intelligence and machine learning (AI/ML) workloads means that architectures will evolve. This underscores newfound opportunities in data storage as use cases and massive data sets only continue to grow. These requirements can be understood by examining the demands for data and the role of storage within AI infrastructure solutions.

1. Volume and location of data 

The AI workflow or pipeline often requires massive amounts of data for ingestion, preparation, training, validation, and inference. With the advent of big data technologies, organizations are collecting and storing vast data sets from various sources, such as sensors, social media, and IoT devices. Managing, storing, and archiving these large volumes of data efficiently is a key requirement in the AI era. Additionally, storing and acting on more and more data closer to or at the source of data generation is vital to maintain a high level of efficiency.

2. Speed of data

In addition to the sheer volume of data, the speed at which data is generated and processed has increased tremendously. Real-time analytics and decision-making are becoming more critical in domains such as finance, healthcare, the cloud, and edge. Data storage systems need to support high-speed data ingestion, processing, and retrieval to enable real-time AI applications.

3. High performance

AI workloads often require high-performance data storage to meet the computational demands of training complex models and processing large data sets. Data storage systems should provide low-latency access to data and high throughput to support intensive AI workloads efficiently and maximize compute infrastructure investments.

4. Data efficiency

Efficiency plays an essential role in ensuring that AI solutions attain optimal performance, control costs, enhance energy conservation for accelerated training and inference, facilitate scalability, and bolster reliability. AI servers consume large amounts of energy, and AI solutions often struggle with delivering enough power to the data center. Thus, leveraging more power- and space-efficient storage can deliver huge benefits to overall infrastructure efficiency, including freeing up space and power needed to accommodate more compute. Through the integration of data-efficient techniques, solutions can deliver more effective and impactful AI data storage systems, thereby generating value across a broad spectrum of AI applications.

While it may be convenient to think of all AI solutions as being the same, the reality is that data in the AI era comes in various formats, including structured, semi-structured, and unstructured data. This includes text, images, videos, sensor data, and time-series data. Data storage systems must be capable of handling diverse data types efficiently and support data models of various sizes to be compatible with AI algorithms and solutions.

For AI solutions, one of the most important considerations is the increased value of data, given that large and high-quality data sets result in the most accurate and complete AI models. Extracting actionable insights and value from data is the primary goal of AI applications. Data storage systems should provide mechanisms for data analysis, visualization, and integration with AI algorithms to deliver meaningful insights and drive business decisions.

Overview of the flash data storage landscape 

AI workloads often require high-performance storage systems and servers to meet the computational demands of training complex models and processing large data sets. Enterprise storage systems and servers should provide low-latency access to data and high throughput to support intensive AI workloads efficiently. Flash storage can be highly beneficial for AI workloads due to its unique characteristics that align well with the demands of AI applications.

What is flash storage?

Flash storage is a type of storage device that uses solid-state storage technology (typically 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. Solid state drives (SSDs) are the most common type of flash storage and have no moving parts, making them faster, more durable, and more energy efficient.

SSDs consist of NAND flash memory chips, a controller, and a cache. The controller manages data storage and retrieval operations, optimizing performance and ensuring data integrity. The NAND flash memory chips store data in a non-volatile manner, meaning the data persists even when the power is turned off. There are several types of NAND flash memory used in SSDs including single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), and quad-level cell (QLC), with each offering different levels of performance, endurance, and cost. In enterprise environments, SSDs provide high performance, reliability, and endurance to meet the demanding requirements of enterprise and data center applications and workloads.

Advantages of SSD storage for AI

SSDs offer several advantages for AI applications, especially when compared to legacy HDDs, due to their characteristics and performance capabilities:

1. Speed and performance

SSDs provide significantly faster data-access times and high throughput. This high-speed access is crucial for AI applications, especially for tasks such as data preprocessing, model training, and inference, where large data sets need to be read and processed quickly.

SSDs offer low latency, meaning they can quickly respond to read and write requests. SSDs also help mitigate the IO blender effect when simultaneously random read and write requests to the storage system result in mixed and unpredictable I/O patterns, leading to decreased overall storage performance. These characteristics are essential for AI workloads that require real-time processing, such as natural language processing, recommendation engines, and real-time analytics.

2. Scalability

SSDs are highly scalable in terms of both capacity and performance. They can be easily scaled up by adding more SSDs or deploying higher capacity configurations (such as QLC) to meet the growing storage demands of AI applications. This scalability ensures that the data storage infrastructure can keep pace with the increasing volume of data generated by AI workloads.

SSDs in AI solutions enable costly GPUs to scale and improve utilization by providing fast and efficient storage for data preprocessing, loading, and transfer, as well as reliable storage for model checkpointing and recovery. By leveraging the capabilities of SSDs, AI systems can maximize the efficiency of GPU resources, accelerate model training, and achieve better performance outcomes.

3. Operational efficiency

The operational efficiency of SSDs in data centers is driven by their speed, reliability, scalability, energy efficiency, and density improvements. At scale, SSDs have lower power consumption compared to overprovisioned HDDs, resulting in reduced cooling requirements within the data center and helping to address data center power bottlenecks due to their more energy-efficient profile. Newer SSD form factors also enable higher density as solutions move away from traditional HDD-based ones.

AI solutions can be expensive due to the high cost of CPUs, GPUs, and memory. SSDs provide efficient data storage to support the vast amount of AI data to deliver a cost-effective AI solution. While SSDs typically have a higher initial cost (in terms of dollars per gigabyte) compared to HDDs, they can be more cost effective when factoring in performance, reliability, and the total cost of ownership, particularly in high-performance workloads such as AI.

Considering Solidigm

Data storage requirements are evolving rapidly with the explosion of the AI era, and it’s important to find the right partner that can provide the flexibility and breadth for each specific AI application. Solidigm is a global leader in innovative NAND flash storage solutions with a comprehensive portfolio of SSD products based on SLC, TLC, and QLC technologies. Headquartered in Rancho Cordova, California, Solidigm operates as a standalone U.S. subsidiary of SK hynix with offices across 13 locations worldwide.

The company has a long legacy of solid-state innovation and leadership, and is positioning itself with solutions for the AI era. Solidigm currently offers a storage portfolio that optimizes and accelerates AI infrastructure, scales efficiently, and improves operation efficiency from core data centers to the edge. The products that power AI solutions include:

1. Solidigm D7-P5810

For high read/write and endurance requirements, the Solidigm D7-P5810 is a fast PCIe 4.0 SSD based on 144-layer SLC 3D NAND, delivering up to 1.6TB capacity with up to 65DWPD for sequential workloads and up to 50DWPD for random workloads with extreme write-intensity and high-endurance needs.

2. Solidigm D5-P5430

For many mainstream and read-intensive workloads, the Solidigm D5-P5430, provides a blend of hyper-dense, affordable storage and high throughput. The D5-P5430 is designed to deliver TLC-like read performance with massive lifetime writes, on QLC economics, leading to reduced total cost of ownership and improved sustainability of data center and edge infrastructures. Available in a broad range of form factors and capacities up to 30.72TB, the Solidigm D5-P5430 can be deployed in a wide array of 1U and 2U configurations. 

3. Solidigm D5-P5336

For the lowest cost with read optimization, the Solidigm D5-P5336 is part of the company's fourth-generation QLC SSDs (using 192-layer QLC NAND flash) for the data center, delivering a blend of high capacities up to 61.44TB with read-optimized performance and high value to read and data-intensive workloads. The Solidigm D5-P5336 has been designed to efficiently accelerate and scale with increasingly massive data sets found in data-hungry workloads, such as data pipelines and data lakes for AI, ML, and big data analytics.

Challenges 

Storage requirements in the AI era are rapidly evolving, driven by the exponential growth of data in next-generation AI systems. The volume of data stored, its storage location, and retention duration are critical factors that will continue to shape storage infrastructure as AI becomes increasingly ubiquitous worldwide. Storage solutions offered by vendors such as Solidigm and others must not only meet current demand but also remain adaptable and flexible as enterprise data center infrastructure expands to support AI at scale. This necessitates a focus on high-speed access, scalability, and efficiency to accommodate the dynamic nature of AI workloads. Looking ahead, advancements in storage technologies tailored for AI are expected to play a pivotal role in shaping the future of data storage in AI-driven environments.

Conclusion

The onset of the AI era has triggered a significant revolution in data storage, leading to groundbreaking innovations and new opportunities. As the landscape of AI evolves, the significance of data storage remains paramount in shaping the trajectory of technology and enterprise infrastructure. With the growing value of data in the AI ecosystem, there arises a heightened need to prioritize data storage. By leveraging flash data storage, companies can efficiently unlock valuable insights, uncover hidden patterns, and derive actionable intelligence from their data, enabling them to stay ahead of the curve and differentiate themselves in the market.

Learn more about storage for AI solutions here.