For 15 years, NVIDIA's consumer GPUs have sacrificed FP64 performance to maintain a clear divide from enterprise hardware. The RTX 5090 exemplifies this with a 1:64 FP64:FP32 ratio, delivering 1.64 TFLOPS of double-precision compute versus 104.8 TFLOPS of single-precision. This deliberate segmentation, once justified by gaming and creative workloads, is crumbling as AI training thrives on lower precision formats.\n\nThe Blackwell Ultra architecture marks a turning point, dropping FP64 performance to 1.2 TFLOPS on the B300 datacenter GPU while prioritizing FP4 tensor cores.

This reversal abandons decades of hardware-based market segmentation in favor of low-precision compute optimization. Emulation techniques like the Ozaki scheme now bridge precision gaps, splitting FP64 operations into FP8 components for tensor core acceleration.\n\nNVIDIA's 2017 EULA restrictions on consumer GPU datacenter use became obsolete as researchers repurposed RTX 4090 cards for AI. The FP64:FP32 ratio on consumer hardware has deteriorated from 1:2 (Fermi) to 1:64 (Ampere), while enterprise GPUs now mirror consumer constraints.

This convergence suggests segmentation will shift toward contractual rather than technical boundaries, with FP64 emulation becoming a standard stopgap for precision-critical workloads.