Behind every AI inferencing strategy: The storage decision multi-model databases demand
By Vincent Hsu (CTO , Fellow & VP IBM Storage) , Sandeep Patil (Distinguished Engineer, IBM Storage)
Enterprise AI discussions have spent the last few years focused heavily on models, accelerators and compute capacity. But as organisations move AI applications from experimentation into production, another challenge is becoming just as important: how quickly and reliably AI systems can access the enterprise data they need to generate a useful answer.
This shift is expanding the infrastructure conversation beyond models and GPUs. The question is no longer simply how well a model performs, but how efficiently it can access current, trusted and relevant information at the point of inference.
That is bringing databases and storage closer to the centre of enterprise AI architecture. The systems that have powered transactional workloads for decades across banking, insurance, healthcare, retail and manufacturing are evolving to support multiple data types, including the vector embeddings used by...
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