Selfhosted

50062 readers

252 users here now

A place to share alternatives to popular online services that can be self-hosted without giving up privacy or locking you into a service you don't control.

Rules:

Be civil: we're here to support and learn from one another. Insults won't be tolerated. Flame wars are frowned upon.
No spam posting.
Posts have to be centered around self-hosting. There are other communities for discussing hardware or home computing. If it's not obvious why your post topic revolves around selfhosting, please include details to make it clear.
Don't duplicate the full text of your blog or github here. Just post the link for folks to click.
Submission headline should match the article title (don’t cherry-pick information from the title to fit your agenda).
No trolling.

Resources:

selfh.st Newsletter and index of selfhosted software and apps
awesome-selfhosted software
awesome-sysadmin resources
Self-Hosted Podcast from Jupiter Broadcasting

Any issues on the community? Report it using the report flag.

Questions? DM the mods!

founded 2 years ago

MODERATORS

HybridSarcasm@lemmy.world

HybridSarcasm@lemmy.hybridsarcasm.xyz

Very large amounts of gaming gpus vs AI gpus (ani.social)

submitted 2 weeks ago by TheMightyCat@ani.social to c/selfhosted@lemmy.world

43 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.

you are viewing a single comment's thread
view the rest of the comments

[–] enumerator4829@sh.itjust.works 27 points 2 weeks ago (7 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.

[–] TheMightyCat@ani.social 2 points 2 weeks ago* (last edited 2 weeks ago) (6 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.

[–] enumerator4829@sh.itjust.works 2 points 2 weeks 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.

load more comments (5 replies)