| 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.
LLMs are experimental, alpha-level technologies. Nvidia showed investors how fast their cards could compute this information. Now investors can just tell the LLM what they want, and it will spit out something that probably looks similar to what they want. But Nvidia is going to sell as many cards as possible before the bubble bursts.
so… GPU were for crypto before, now it’s for LLM, weird world we live in
Any time you need a CPU that can do a shit load of basic math, a GPU will win every time.
You can design algorithms specifically to mess up parallelism by branching a lot. For example, if you want your password hashes to be GPU-resistant.