| 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.
The table you’re referencing leaves out CUDA/ tensor cores (count+gen) which is a big part of the gpus, and also not factoring in type of memory. From the comments it looks like you want to use a large MoE model. You aren’t going to be able to just stack raw power and expect to be able to run this without major deterioration of performance if it runs at all.
Don’t forget your MoE model needs all-to-all communication for expert routing
Why do core counts and memory type matter when the table includes memory bandwith and tflop16?
The H200 has HBM and alot of tensor cores which is reflected in its high stats in the table and the amd gpus don’t have cuda cores.
I know a major deterioration is to be expected but how major? Even in extreme cases with only 10% efficiency of the total power then its still competitive against the H200 since you can get way more for the price, even if you can only use 10% of that.
Tflops is a generic measurement, not actual utilization, and not specific to a given type of workload. Not all workloads saturate gpu utilization equally and ai models will depend on cuda/tensor. the gen/count of your cores will be better optimized for AI workloads and better able to utilize those tflops for your task. and yes, amd uses rocm which i didn’t feel i needed to specify since its a given (and years behind cuda capabilities). The point is that these things are not equal and there are major differences here alone.
I mentioned memory type since the cards you listed use different versions ( hbm vs gddr) so you can’t just compare the capacity alone and expect equal performance.
And again for your specific use case of this large MoE model you’d need to solve the gpu-to-gpu communication issue (ensuring both connections + sufficient speed without getting bottlenecked)
I think you’re going to need to do actual analysis of the specific set up youre proposing. Good luck