Source: Uncoveralpha
As AI deployment shifts from training to inference—where the money actually flows—companies are discovering that raw compute capacity no longer determines speed or efficiency. Memory bandwidth and storage per token are becoming the hard limits, forcing a pivot from GPU-centric architectures toward systems optimized for data movement rather than arithmetic throughput. This reshuffles the hardware and software stack, favoring new chip designs and inference frameworks while devaluing older GPU advantages. Challengers who solve token memory constraints more elegantly than incumbent silicon vendors gain room to compete.