Source: SiliconANGLE
The industry's shift from measuring raw compute power to tokens-per-watt efficiency directly elevates storage from peripheral infrastructure to core constraint—because modern LLMs are increasingly I/O bound rather than compute bound. This reframes vendor competition and capex allocation: companies like CoreWeave and Lambda Labs are winning not on faster GPUs but on reducing the energy cost of moving data between memory hierarchy layers. Storage bandwidth and latency are now the real differentiator in training and inference economics. For enterprises building inference infrastructure, storage performance now determines ROI on billion-dollar AI investments, not processor flops.