GPU VRAM Price (€) Bandwidth (TB/s) TFLOP16 €/GB €/TB/s €/TFLOP16
NVIDIA H200 NVL 141GB 36284 4.89 1671 257 7423 21
NVIDIA RTX PRO 6000 Blackwell 96GB 8450 1.79 126.0 88 4720 67
NVIDIA RTX 5090 32GB 2299 1.79 104.8 71 1284 22
AMD RADEON 9070XT 16GB 665 0.6446 97.32 41 1031 7
AMD RADEON 9070 16GB 619 0.6446 72.25 38 960 8.5
AMD RADEON 9060XT 16GB 382 0.3223 51.28 23 1186 7.45

This post is part “hear me out” and part asking for advice.

Looking at the table above AI gpus are a pure scam, and it would make much more sense to (atleast looking at this) to use gaming gpus instead, either trough a frankenstein of pcie switches or high bandwith network.

so my question is if somebody has build a similar setup and what their experience has been. And what the expected overhead performance hit is and if it can be made up for by having just way more raw peformance for the same price.

  • BrightCandle@lemmy.worldEnglish
    21·
    21 hours ago

    Initially a lot of the AI was getting trained on lower class GPUs and none of these AI special cards/blades existed. The problem is that the problems are quite large and hence require a lot of VRAM to work on or you split it and pay enormous latency penalties going across the network. Putting it all into one giant package costs a lot more but it also performs a lot better, because AI is not an embarrassingly parallel problem that can be easily split across many GPUs without penalty. So the goal is often to reduce the number of GPUs you need to get a result quickly enough and it brings its own set of problems of power density in server racks.