Buy an RTX 5090, the fastest consumer GPU money can buy, and you get 104.8 TFLOPS of FP32 compute. Ask it to do double-precision math and you get 1.64 TFLOPS. That 64:1 gap is not a technology limitation. For fifteen years, the FP64:FP32 ratio has been slowly getting wider on consumer GPUs, widening the divide between consumer and enterprise silicon. Now the AI boom is quietly dismantling that logic.
The Evolution of FP64 on Nvidia GPUs
The FP64:FP32 ratio on Nvidia consumer GPUs has degraded consistently since the Fermi architecture debuted in 2010. On Fermi, the GF100 die shipped to both GeForce and Tesla lines; the hardware supported 1:2 FP64:FP32, but GeForce cards were driver-capped to 1:8.1
Over time, Nvidia moved away from “artificially” lowering FP64 performance on consumer GPUs. Instead, the architectural split became structural; the hardware itself is fundamentally different across product tiers. While datacenter GPUs have consistently kept a 1:2 or 1:3 FP64:FP32 performance (until the recent AI boom, more on that later), the performance ratio on consumer GPUs has consistently gotten worse. From 1:8 on the Fermi architecture in 2010 to 1:24 on Kepler in 2012 to 1:32 in 2014 to our final 1:64 ratio on Ampere in 2020.
This effectively also means that over 15 years, from the GTX 480 in 2010 to the RTX 5090 in 2025 the FP64 performance on consumer GPUs only increased 9.65x from 0.17 TFLOPS to 1.64 TFLOPS, while in the same time range the FP32 performance improved a whopping 77.63x from 1.35 TFLOP to 104.8 TFLOP.
FP32 vs FP64 throughput scaling across Nvidia GPU generations.2
Nvidia's Move to Segment the Market
So why has FP64 performance on consumer GPUs progressively gotten weaker (in relation to FP32) while it stayed consistently strong on enterprise hardware?
If this were purely a technical or cost constraint, you would expect the gap to be smaller. But since historically, Nvidia has taken deliberate steps to limit double-precision (FP64) throughput on GeForce cards, it makes it hard to argue this is accidental. The much simpler explanation is market segmentation.
Most consumer workloads, such as gaming, 3d rendering, or video editing do not need FP64. High-performance computing on the other hand has long relied on double precision (FP64). Fields such as computational fluid dynamics, climate modeling, quantitative finance, and computational chemistry depend on numerical stability and precision that single precision (FP32) cannot always provide. So FP64 becomes a very convenient lever: weaken it on consumer GPUs, preserve it on enterprise versions, and you get a clean dividing line between markets. Nvidia has been fairly open about this. In the consumer Ampere GA102 whitepaper, they note "The small number of FP64 hardware units are included to ensure any programs with FP64 code operate correctly.".3
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