[dantillberg]

Most crypto mining on GPUs would use 100% of memory bandwidth, but only a fraction of the compute available. This is a consequence of ASIC resistance of their mining algorithms -- custom silicon can only offer a modest benefit over GPUs if the hard part is memory bandwidth.

[noosphr]

So does llm inference. You're lucky if you hit 40% of the advertised flops.

[fwipsy]

Right, but datacenter GPUs optimized for LLM training/inference would have a bandwidth:compute ratio scaled to that workload.

[noosphr]

No they don't.

[dapperdrake]

Then where are the HDMI ports on Nvidia's current data center GPU product lines?

[eru]

Why not? Seems like they would be poorly optimised?

[noosphr]

Because llm inference is not the only workload a GPU can do and custom silicon cost $10b a chip.

[eru]

AI optimised cards is the biggest cashcow for nvidia. They are definitely doing custom silicon. And Google et al have cards that don't even pretend to be able to do graphics.

[NuclearPM]

Why is that true? Can’t you just make more stuff parallel and shrink the ASIC chips accordingly?

[jmalicki]

No - inability to do so is part of the design of a good cryptographic hash, quite explicitly.

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