AI’s overlooked storage opportunity
The AI infrastructure discussion is typically framed around the cost of data centers, the power requirements, and the compute needed to train and run models, including GPUs and high-performance storage. That’s hardly surprising given the eye-watering investment numbers occupying the headlines.
The other key commodity, of course, is data to fuel those models. According to Stanford University’s 2025 AI Index Report, dataset sizes for training LLMs are doubling every eight months. In practical terms, as each model is built, some data will move quickly into curation and model-development environments, where fast access is essential.
Much of it, however, will wait longer while teams establish its relevance to a particular AI use case – not sitting idle, but held securely and ready to move quickly into curation, training and transformation pipelines when needed.
Skip Levens is Product Leader and AI Strategist at Quantum.
From a storageperspective, this raises a point...
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