It's amazing to step back and look at how much of NVIDIA's success has come from unforeseen directions. For their original purpose of making graphics chips, the consumer vs pro divide was all about CAD support and optional OpenGL features that games didn't use. Programmable shaders were added for the sake of graphics rendering needs, but ended up spawning the whole GPGPU concept, which NVIDIA reacted to very well with the creation and promotion of CUDA. GPUs have FP64 capabilities in the first place because back when GPGPU first started happening, it was all about traditional HPC workloads like numerical solutions to PDEs.

Fast forward several years, and the cryptocurrency craze drove up GPU prices for many years without even touching the floating-point capabilities. Now, FP64 is out because of ML, a field that's almost unrecognizable compared to where it was during the first few years of CUDA's existence.

NVIDIA has been very lucky over the course of their history, but have also done a great job of reacting to new workloads and use cases. But those shifts have definitely created some awkward moments where their existing strategies and roadmaps have been upturned.

FP64 performance is limited on consumer because the US government deems it important to nuclear weapons research.

Past a certain threshold of FP64 throughput, your chip goes in a separate category and is subject to more regulation about who you can sell to and know-your-customer. FP32 does not matter for this threshold.

https://en.wikipedia.org/wiki/Adjusted_Peak_Performance

It is not a market segmentation tactic and has been around since 2006. It's part of the mind-numbing annual export control training I get to take.

No mention of the Radeon VII from 2019 where for some unfathomable reason AMD forgot about the segmentation scam and put real FP64 into a gaming GPU. From this 2023 list, it's still faster at FP64 than any other consumer GPU by a wide margin (enterprise GPU's aren't in the list). Scroll all the way to the end.

https://www.eatyourbytes.com/list-of-gpus-by-processing-powe...

I'm not sure why the article dismisses cost.

Let's say X=10% of the GPU area (~75mm^2) is dedicated to FP32 SIMD units. Assume FP64 units are ~2-4x bigger. That would be 150-300mm^2, a huge amount of area that would increase the price per GPU. You may not agree with these assumptions. Feel free to change them. It is an overhead that is replicated per core. Why would gamers want to pay for any features they don't use?

Not to say there isn't market segmentation going on, but FP64 cost is higher for massively parallel processors than it was in the days of high frequency single core CPUs.