Nvidia's Blackwell Ultra Upends 15 Years of GPU Segmentation Strategy

For fifteen years, Nvidia systematically widened the performance gap between single-precision (FP32) and double-precision (FP64) computing on consumer GPUs. Where the 2010 Fermi architecture maintained a 1:8 FP64-to-FP32 ratio, today's RTX 5090 stretches this to 1:64 - enabling 104.8 TFLOPS for gaming-focused workloads while throttling scientific computation to just 1.64 TFLOPS. This wasn't accidental. It represented sophisticated market segmentation: enterprise GPUs commanded 20x price premiums partly through superior FP64 performance essential for computational fluid dynamics, climate modeling, and financial simulations.

FP32 versus FP64 throughput scaling on Nvidia consumer GPUs over time

Between 2010's GTX 480 and 2025's RTX 5090, FP32 performance surged 77x while FP64 crawled forward just 9.65x. The segmentation logic held until generative AI rewrote the rules. Modern AI training thrives on lower precisions (FP16-BF16-FP8), making consumer GPUs unexpectedly capable for serious workloads. Nvidia responded with controversial licensing restrictions, but technological innovation proved harder to contain.

Enter FP64 emulation techniques. Methods like Dekker's double-float arithmetic (1971) split 64-bit values into two FP32 components: a high-precision anchor and low-precision correction term. Andrew Thall later adapted these for GPUs, trading 5 bits of precision for substantially higher throughput. The breakthrough came via tensor cores. The Ozaki scheme decomposes FP64 matrices into FP8 slices, leveraging AI-optimized hardware for distributed computation reassembled into full-precision results.

Emulated double representation using high and low FP32 parts

Nvidia integrated Ozaki scheme support into cuBLAS in 2025, coinciding with Blackwell Ultra's radical departure. The enterprise-focused B300 slashes native FP64 performance to 1.2 TFLOPS (1:64 ratio), mirroring consumer constraints while quadrupling FP4 tensor cores. This inversion reflects AI's dominance in enterprise revenue: segmentation now occurs along precision axes that matter to neural networks.

Enterprise vs consumer GPU price ratio over time

Market segmentation hasn't disappeared - it migrated. Where FP64 once divided consumer and enterprise silicon, the new frontier emerges in ratios like the RTX 5090's 1:1 FP16:FP32 versus B200's 16:1. Nvidia insists they're not abandoning hardware FP64, but the Blackwell pivot proves that emulation and AI-driven architectures have permanently altered compute economics. Fifteen years of carefully constructed segmentation eroded not by consumer demand, but by the unintended consequences of the AI tsunami.

References

• NVIDIA Ampere Architecture Whitepaper
• Extended-Precision Floating-Point Numbers for GPU Computation
• Unlocking Tensor Core Performance with Floating-Point Emulation
• NVIDIA on 64-bit Computing Future