[numbers_guy]

A question that has been bugging me for a while is what will NVIDIA do with its HPC business? By HPC I mean clusters intended for non-AI related workloads. Are they going to cater to them separetely, or are they going to tell them to just emulate FP64?

[arcanus]

Hopper had 60 TF FP64, Blackwell has 45 TF, and Rubin has 33 TF.

It is pretty clear that Nvidia is sunsetting FP64 support, and they are selling a story that no serious computational scientist I know believes, namely that you can use low precision operations to emulate higher precision.

See for example, https://www.theregister.com/2026/01/18/nvidia_fp64_emulation...

It seems the emulation approach is slower, has more errors, and doesn't apply to FP64 vector, only matrix operations.

[dgacmu]

This is kind of amazing - I still have a bunch of Titan V's (2017-2018) that do 7 TF FP64. 8 years old and managing 1/4 of what Rubin does, and the numbers are probably closer if you divide by the power draw.

(Needless to say, the FP32 / int8 / etc. numbers are rather different.)

[adrian_b]

For a long time AMD has been offering much better FP64 performance than NVIDIA, in their CDNA GPUs (which continue the older AMD GCN ISA, instead of resembling the RDNA used in gaming GPUs).

Nevertheless, the AMD GPUs continue to have their old problems, weak software support, so-and-so documentation, software incompatibility with the cheap GPUs that could be used directly by a programmer for developing applications.

There is a promise that AMD will eventually unify the ISA of their "datacenter" and gaming GPUs, like NVIDIA has always done, but it in unclear when this will happen.

Thus they are a solution only for big companies or government agencies.

[wmf]

AMD MI430X is taking that market.