this post was submitted on 14 Jul 2025
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45
Very large amounts of gaming gpus vs AI gpus (ani.social)
submitted 3 days ago by [email protected] to c/[email protected]
40 comments fedilink hide all child comments
 
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.

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[–] [email protected] 27 points 2 days ago (4 children)

Well, a few issues:

  • For hosting or training large models you want high bandwidth between GPUs. PCIe is too slow, NVLink has literally a magnitude more bandwidth. See what Nvidia is doing with NVLink and AMD is doing with InfinityFabric. Only available if you pay the premium, and if you need the bandwidth, you are most likely happy to pay.
  • Same thing as above, but with memory bandwidth. The HBM-chips in a H200 will run in circles around the GDDR-garbage they hand out to the poor people with filthy consumer cards. By the way, your inference and training is most likely bottlenecked by memory bandwidth, not available compute.
  • Commercially supported cooling of gaming GPUs in rack servers? Lol. Good luck getting any reputable hardware vendor to sell you that, and definitely not at the power densities you want in a data center.
  • TFLOP16 isn’t enough. Look at 4 and 8 bit tensor numbers, that’s where the expensive silicon is used.
  • Nvidias licensing agreements basically prohibit gaming cards in servers. No one will sell it to you at any scale.

For fun, home use, research or small time hacking? Sure, buy all the gaming cards you can. If you actually need support and have a commercial use case? Pony up. Either way, benchmark your workload, don’t look at marketing numbers.

Is it a scam? Of course, but you can’t avoid it.

[–] [email protected] 2 points 2 days ago* (last edited 2 days ago) (2 children)
  • I know the more bandwidth the better, but i wonder how does it scale. I can only test my own setup which is less then optimal for this purpose with pcie 4.0 x16 and no p2p, but it goes as follows: a single 4090 gets 40.9 t/s while 2 get 58.5 t/s using tensor parrelism tested on Qwen/Qwen3-8B-FP8 with vLLM. I am really curious how this scales over more then 2 pcie 5.0 cards with p2p, which all cards here listed except the 5090 support.
  • The theory goes that yes while the H200 has a very impressive bandwith of 4.89 TB/s, but for the same price you can get 37 TB/s spread across 58 RX 9070s, but if this actually works in practice i don't know.
  • I don't need to build a datacenter, i'm fine with building a rack myself in my garage. And i don't think that requires higher volumes than just purchasing at different retailers
  • I intend to run at fp8 so i wanted to show that instead of fp16 but its surprisingly difficult to find the numbers for that, only the H200 datasheet, cleary displays FP8 Tensor Core, the RTX pro 6000 datasheet keeps it vague with only mentioning AI TOPS, which they define as Effective FP4 TOPS with sparsity, and they didn't even bother writing a datasheet for he 5090 only saying 3352 AI TOPS, which i suppose is fp4 then. the AMD datasheets only list fp16 and int8 matrix, whether int8 matrix is equal to fp8 i don't know. So FP16 was the common denominator for all the cards i could find without comparing apples with oranges.
[–] [email protected] 5 points 2 days ago (1 children)

I don't need to build a datacenter, i'm fine with building a rack myself in my garage.

During the last GPU mining craze, I helped build a 3-rack mining operation. Gpus are unregulated pieces of power-sucking shit from a power management perspective. You do not have the power requirements to do this on residential power, even at 300amp service.

Think of a microwave's behaviour ; yes, a 1000w microwave pulls between 700 and 900w while cooking, but the startup load is massive, almost 1800w sometimes, depending on how cheap the thing is.

GPUs also behave like this, but not at startup. They spin up load predictively, which means the hardware demands more power to get the job done, it doesn't scale down the job to save power. Multiply by 58 rx9070. Now add cooling.

You cannot do this.

[–] [email protected] 1 points 2 days ago

Thanks, While I still would like to know thr peformance scaling of a cheap cluster this does awnser the question, pay way more for high end cards like the H200 for greater efficiency, or pay less and have to deal with these issues.

[–] [email protected] 2 points 2 days ago

the H200 has a very impressive bandwith of 4.89 TB/s, but for the same price you can get 37 TB/s spread across 58 RX 9070s, but if this actually works in practice i don't know.

Your math checks out, but only for some workloads. Other workloads scale out like shit, and then you want all your bandwidth concentrated. At some point you’ll also want to consider power draw:

  • One H200 is like 1500W when including support infrastructure like networking, motherboard, CPUs, storage, etc.
  • 58 consumer cards will be like 8 servers loaded with GPUs, at like 5kW each, so say 40kW in total.

Now include power and cooling over a few years and do the same calculations.

As for apples and oranges, this is why you can’t look at the marketing numbers, you need to benchmark your workload yourself.

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