Combining read performance exceeding that of some cost-optimized TLC SSDs and capacities up to 61.44TB [1] built on industry-leading NAND density [2], the Solidigm D5-P5336 has been architected to efficiently accelerate and scale increasingly massive data sets found in widely deployed read-intensive workloads—all while increasing storage density, reducing TCO, and enabling a more sustainable storage infrastructure than both TLC SSD- and HDD-based solutions.
Storage Density Matters
Widely adopted modern workloads are becoming even more data hungry. Many AI models are growing approximately 10X in size every two years [3]. Streaming services have shifted from limited paid for capacity to unlimited free capacity [4]. With connected IoT devices expected to reach 14.5 billion [5] by the end of 2023, supported by the tailwind of an accelerating 5G rollout, there is no end in sight to unabated growth in data-rich services and applications.
Accompanying this trend is the decentralization of compute and storage to the edge to improve service levels, reduce cost, and increase agility. As recently as 2018, only 10% of enterprise-generated data was created and processed outside of centralized data centers or cloud services. By 2025, Gartner expects 75% of that data to be created, processed, and stored at the edge [6]. Storage challenges such as space, power and cooling, sustainability, and serviceability become even more acute when considering locality constraints at edge deployments.
Optimized Capabilities for Read-intensive Workloads
In this environment, modern, data hungry workloads such as data pipelines and data lakes for AI, ML and Big Data Analytics, CDN, scale-out NAS, object storage, and edge usages are increasingly concerned with storing and accessing vast amounts of data efficiently at speed. The D5-P5336 is optimized for both requirements with read performance equivalent to TLC SSDs and capacities that are 2 – 3X higher than other storage alternatives.
The D5-P5336 delivers TLC-equivalent performance for read and data-intensive workloads.
