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.

  • TheMightyCat@ani.socialOPEnglish
    1·
    5 months ago

    My target model is Qwen/Qwen3-235B-A22B-FP8. Ideally its maxium context lenght of 131K but i’m willing to compromise. I find it hard to give an concrete t/s awnser, let’s put it around 50. At max load probably around 8 concurrent users, but these situations will be rare enough that oprimizing for single user is probably more worth it.

    My current setup is already: Xeon w7-3465X 128gb DDR5 2x 4090

    It gets nice enough peformance loading 32B models completely in vram, but i am skeptical that a simillar system can run a 671B at higher speeds then a snails space, i currently run vLLM because it has higher peformance with tensor parrelism then lama.cpp but i shall check out ik_lama.cpp.