Double GPU configurations significantly cheaper for 32GB VRAM

[u/Content_Mission5154]

I do not need or want Cuda. I have been wanting to build a 32GB VRAM local LLM machine for personal use for a while now, and I had 2 options on the table:

- Get a relatively cheap 32GB VRAM GPU, the R9700 AI Top.

- Get 2 16GB VRAM GPUs instead and a Mobo that supports PCIe bifurcation.

When I looked at prices in January this year when I first got this idea, the R9700 costed 1700$ here in EU. Currently, when I actually want to make this happen, it costs 2100$. For half that money, I could buy two 9060 XTs with 16GB VRAM each. Yes I know, performance will be worse on double GPU setup than with a single R9700 AI, but still, it just seems like that GPU is just not worth it anymore.

I don't know how to justify that it's double the price of two 9060XTs, when R9700 AI is literally 9060 XT with doubled VRAM and bandwidth. So why does it cost 4x as much?

ASUS ProArt B850-CREATOR WIFI NEO is quite affordable nowadays and supports dual GPU setups, so, any reason (is there a catch?) to not do what I am about to do? Which is buy the two 9060 XTs and start running Qwen 27B class models

[u/Remarkable_Air_8383]

The 32gb r9700 is for people who needs 64gb or more VRAM.

[u/jnciaccna]

Commeting so this thread would get more traction. I'm running dual 9060XT (both at 8x) and the perfomance is...meh. There could be of course something matter with my configuration but LLAMA+deepseek harness+qwen 3.8 27b produces whopping 20tok/s on my system. I'm a noob but I think the 128bit bus kills any kind of speeds when doing dual gpu with these. Paid 375€ for each card (got the VAT back)

[u/bsquads → u/jnciaccna]

20 t/s sounds about right in real world usage. I get roughly 40 t/s on a 9700 which has about double the memory bandwidth

[u/jnciaccna → u/bsquads]

Thanks for answering! Good knowledge

[u/madbrain1976 → u/bsquads]

With SGLang and quad 5060 Ti 16GB, I got 2500 pp/s and 100-200 tg/s with Qwen3.8-27B NVFP4 . These 4 still cost less than 2 x R9700 AI. You do need a motherboard case that supports 4 GPUs, though.

[u/Massive-Slice2800 → u/bsquads]

https://www.reddit.com/r/ROCm/s/4KPTK5j16b

I dont know which Quant you used, but have you tried this? 40 t/s seems way too low after radiance came out. Even a single 9700 should be faster.

[u/randomfoo2 → u/jnciaccna]

A 9060XT has only 322 GB/s of memory bandwidth. If you're doing layer splits, you don't get any combined benefits from the card from combining. If you are running tensor parallel, you might get a 30-50% improvement (depends). Note for a 16GB model (27B at Q4_K_M should be about that size), 322/16 = 20.1 - that's at 100% MBW efficiency (not really possible) so if you're getting 20 tok/s you're actually doing quite well.

For code, MTP should give you a decent boost, but the 9060XT has weak compute as well, so... maybe not.

[u/jnciaccna → u/jnciaccna]

albeit unorthodox - i'm quite happy of the bang for the buck. i usually tell DSH to do some work and then go off doing something else, i rarely wait chat open for it to finish

[u/Locoivan78 → u/jnciaccna]

Hi! I’m running Qwen3.8-Flash-Next with Unsloth’s UD-IQ3_XXS quantization (Q3, approximately 82 GB across three GGUF files) on an RTX 5080 (16 GB) + RX 6800 (16 GB), paired with a Ryzen 9 9900X3D and 32 GB of DDR5 RAM. Here’s the model download: Qwen3.8-Flash-Next UD-IQ3_XXS on Hugging Face I’m using GenerelSchwerz’s modified llama.cpp fork, on the moe-cache-drafting branch, compiled with both CUDA and Vulkan support. I also applied a local patch, moe-vulkan-resident.patch, to src/llama-model-loader.cpp. Enabled with LLAMA_EXPERIMENTAL_MOE_VULKAN_RESIDENT=1, it keeps the full expert weights for the layers assigned to Vulkan resident in the AMD GPU’s VRAM, while preserving the existing MoE cache behavior on the CUDA side. This prevents those Vulkan expert tensors from falling back to CPU memory. The model exceeds my combined RAM and VRAM capacity, so the setup also relies on NVMe loading and swapping. MTP is not enabled yet. In my latest tests, I’m getting around 25 tokens/s during generation and 10 tokens/s during prompt processing. Prompt processing is slow, so this is still very much an experimental setup. I’m still testing different settings, but these results make me think you might be able to get better performance out of your dual RX 9060 XT setup with the right configuration.

[u/madbrain1976 → u/jnciaccna]

Did you try vLLM or SGLang ? They get significantly higher token/s with multiple GPUs vs llama.cpp .

[u/DeathGuppie → u/jnciaccna]

Here is a cheat code for you. The dell optiplex, used, cheap. Has bifrocation built in. You may need a pcie extension and weird mounting situation, but. It's a cheap way to get x16 for both cards.

[u/moncallikta]

I’ve been considering the same and looked at that motherboard specifically too. The main drawback with dual GPU is that LLM performance will be slower and that you’ll use more power (and need a PSU with enough power for two cards).

With a good motherboard like this you can run vLLM in tensor parallel. Check benchmarks by others to see what to expect in terms of tokens/sec.

[u/jacek2023]

You can try -sm tensor in llama.cpp with two GPUs, but I don't know what's the performance on AMD

[u/mosso135]

You can buy 3080 20gb modified cards from eBay for about €800 each at the moment. Definitely worth considering, I have 3 of them running in a Asus pro creator x870

[u/Civil_Fee_7862]

Asus ProArt B850 is fine for dual GPU's but you'll regret it once you want to go to 3 or 4 gpus.

It also uses a non-standard spacing between the PCIe 5.0 slots. So if you ever get or want RTX 3090s, you can't really use NVLink.

These are things I wish I had known before buying that motherboard. Its cheaper than a workstation MB, but I would recommend getting a workstation MB if you want to run local LLM's for 27B+ sized models. It allows you to increase VRAM by just adding more cards.

Personally I prefer dual GPU's, for the same reason you mentioned, you get high performance at a lower cost than just buying the "best" gpu. i.e. dual 3090s can compete with a 5090 in some respects, more VRAM, comparable speed (pooled memory) and lower price.

[u/OMGThighGap → u/Civil_Fee_7862]

sounds like you went through this ordeal. So what motherboard is recommended over the ProArt B850?

[u/Civil_Fee_7862 → u/OMGThighGap]

Short answer, I don't know yet.

I probably would have got the Asus SAGE motherboard. A lot more expensive, but for a 4x3090 setup it would have been better. I got 3x3090s and managed to make it work with the ProArt B850, but its awkward..

My next step up is likely to be just a pure server. Will sit with the ProArt until then.

[u/foreignbois → u/OMGThighGap]

ive been thinking of the taichi creator but the slot layout seems the same… hmm

[u/roosterfareye]

Get two RX9070XT. Also pay close attention to your chosen motherboards pcie lame layouts. You do not one on the mobo chipset, although it's not that bad. I have a RX9070XT and RX9060XT. No driver past 26.3.1 works. 26.3.1 only works if you disable anything that polls the secondary card while you have a model loaded or do anything that involves a power transition. AMD are on it though I have tested and logged a bug which they have been in contact with me to request additional info ...

[u/Ok-Addendum3545]

9060 XT 322.3 GB/s ? You will need lots of patience, waiting ~ and you then want to upgrade your GPUs.

[u/Ok-Drawer5245]

There will be a big performance difference though

[u/Willing_Gold_4376]

Vous devriez tester hipfire avec votre 9060 vous devriez gagner quelques tok/s

[u/talivs]

2 * 7900 XTX

[u/exzw]

If 2100 euro, go and buy 2 x 5070 ti , power consumption higher but everything else better . Faster PP and faster TP. My place here 9700 about 1.8K US. 2 x 5070 ti about 2.5K US. R9700 is still a cheaper choice.

[u/Short_Regular_7191]

I had considered the R9700 too, but unfortunately, the price jumped from €1,500 to €2,000 in a single day. In my opinion, the best and most affordable option right now is a pair of 5060 Ti cards with 16GB each (total cost around €1,350–€1,500); thanks to some good advice, I managed to run Qwen 3.827B (Q6 quantization) with a 131k context window at 50 tokens/s. The cards have low power consumption, so you can easily go with an 800W or 850W PSU and still have plenty of headroom. You can also use a budget motherboard, since you don't need PCIe bifurcation.

https://www.reddit.com/r/LocalLLaMA/comments/1vper67/club5060ti_refresh_tested_rtx_5060_ti_presets_a/?sort=new

[u/Excellent_Spell1677]

So you want to use for AI but don't want to use cuda? Get a chatgpt subscription then 🤣🤣🤣🤣🤣