SPONSOR CONTENT FROM SOLIDIGM, October 30, 2025
This is the era of the artificial intelligence (AI) gold rush. Around the world and in every sector, businesses are racing to determine how they might apply AI to their business models and instantly vault themselves over their competitors.
To make the best decisions about how to incorporate AI, businesses need to examine the resources and infrastructure they need to fulfill AI’s enormous potential. Yet in their rush to embrace AI and its promises of transformation, many organizations overlook two critical components that give this technology its power: their data and their storage.
Organizations have long adopted cloud and on-premises infrastructure to build the primary data centers—notorious for their massive energy consumption and large physical footprints—that fuel AI’s large language models (LLMs).
Today these data centers are making edge data processing an increasingly attractive resource for fueling LLMs, moving compute and AI inference closer to the raw data their customers, partners, and devices generate.
Large Workloads, Small Footprints
With their full-stack application processing capabilities in a smaller footprint, the edge data center market is forecast to grow from $15 billion in 2024 to nearly $40 billion by 2030, according to Research and Markets.
Organizations looking for their competitive edge are seeing many benefits of analyzing this edge data: greater agility, scalability, and security, and reduced latency and bandwidth needs.
Building and applying robust LLMs to power innovation and growth require businesses to address and access massive volumes of raw edge data. But many enterprises focusing on that data’s potential might not be thinking about storage.
Powerful storage drives are critical to getting the most from edge data. In a range of evolving enterprise sectors, including health care, gas and energy, media and entertainment, automotive, finance, and real estate, businesses are adopting a new generation of performance-focused, rightsized, high-capacity solid-state drive (SSD) servers to release trapped edge data with far greater energy efficiency than traditional hard-disk drives (HDDs) with spinning technology can achieve.
Two key AI inference workloads at the edge, text-based LLM applications and continuous data processing, such as sensor or video content and analytics, have distinct storage needs. To unlock efficient, scalable, secure AI deployments outside the core data center, these workloads each need affordable, energy-efficient SSDs that offer low-latency read access, high mixed-data access, and greater storage capacity and speed in a small footprint by pairing the right SSD with the right application.
Succeeding at the Edge
Using robust SSDs to get the most from edge data touches a wide range of sectors. In the automotive industry, testing cars with autonomous driving and advanced driver-assistance systems generates edge data that car manufacturers use to strengthen object detection and other complex safety applications. These vehicles are rolling edge data centers, generating terabytes of sensor and camera data.
To convert this data in near real time and predict the widest and most complex range of scenarios, computer company InoNet uses a new generation of high-capacity, low-latency SSDs to create powerful in-vehicle testing systems that can manage and analyze test-drive and simulation data and preserve the data’s accuracy while vehicles move among locations.
In finance, Intercontinental Exchange (IE), one of the world’s largest data aggregators, manages 700 billion transactions a day from the New York Stock Exchange and other marketplaces to help customers build statistical analyses and make trading decisions in real time. That capability depends on flexible, highly scalable SSDs to store and quickly distribute terabytes of highly granular compressed data.
IE also provides mortgage and real estate data services, using AI to analyze the minute details of documents, photographs, videos, and other data types for its multiple listing services. To serve these customers across the U.S., IE uses storage to extract, write, and retrieve data for easy, immediate access.
As more companies in various sectors adopt AI and compute-intensive workloads, scaling data operations while controlling energy consumption is a mounting challenge.
Data analytics company Ocient’s Hyperscale Data Warehouse incorporates energy-efficient SSDs for real-time analysis at scale, letting customers extract insights from compute-intensive petabyte-scale data sets using hardware with a minimal footprint. One Ocient customer using its SSD-powered platform has saved $1.6 million in energy costs over three years while cutting hardware, capital expenses, and operating expenses.
The SSD Difference
For businesses building transformative AI strategies, it’s critical to consider how to incorporate their edge data into building the most powerful LLMs—and whether their own infrastructure is up to storing, processing, and analyzing those massive data sets.
For customers, partners, and facilities generating data far from their primary data centers, building distributed edge solutions into modern data infrastructure with SSDs can offer greater performance and higher capacity than conventional HDDs, managing data with lower transmission, processing, and storage costs.
And as AI-driven data workloads become increasingly compute- and energy-intensive, incorporating smaller and more agile SSDs can make a critical difference, helping organizations increase compute power while reducing costs to make the most of their edge data to get an edge over their competitors.
Story from Harvard Business Review
