"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."
"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, hi"
Organizations are racing to apply AI and must assess resources and infrastructure to realize AI's potential. Data centers have powered large language models but consume massive energy and occupy large physical footprints. Edge data processing moves compute and inference closer to raw data sources, improving latency, security, scalability, and bandwidth efficiency. The edge data center market is projected to grow substantially by 2030. Building robust LLMs requires access to massive volumes of raw edge data and appropriate storage. Many enterprises overlook storage when focusing on edge data. Performance-focused, rightsized storage drives are becoming critical across healthcare, energy, media, automotive, finance, and real estate.
Read at Harvard Business Review
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