| 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.
Products targeted towards businesses have always been unreasonably more expensive than those targeted towards consumers. It sucks for us AI hobbyists that Nvidia are stingy with VRAM on consumer cards, but I don’t find it surprising.
Personally I only have a single RTX 3090, but I know a lot of people online who are stacking multiple consumer cards to run AI. Buying used 3090s and putting them in a mining rig is probably still the best value for money if you need a large amount of VRAM.
How much VRAM do you actually need btw?