Storage is often underestimated:
In infrastructure discussions, compute and networking get most of the attention, while storage is treated as secondary. For AI workloads, that can be a costly oversight.
Data throughput for specialized hardware:
AI infrastructure powered by GPUs can process massive volumes of data at unprecedented speeds. This puts immense pressure on the storage system to keep up.
Scale-out performance:
An on-prem, scale-out, software-defined storage setup allows you to meet high performance demands, grow capacity as needed, and stay in control of infrastructure costs.
Cost and efficiency:
Public cloud storage can lead to overspending, especially with long-running workloads and heavy experimentation. On-prem gives you predictability and cost control.
Ultimately, storage should be treated as a first-class citizen in your AI stack.
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