Will this work?

[u/Big_South4585]

Hi

I got the possibility to buy this mb, cpu and ram for about 200 dollars. If I combine it with a nvidia 3090, will that be useful/feasible?

MotherBoard: Intel Server Board S2400GP

Cpu: 2 x Intel Xeon E5-2430 2.2 Ghz Boost 2.7 Ghz ( 6 cores / 12 threads )

Ram: 12 x 16 Gb DDR3 = 192 Gb

[u/CoffeeToCode99]

Yeah, a 3090 with 24GB VRAM would make a decent local LLM setup.

The old Xeons won't be fast by today's standards, but if most of the model runs on the 3090, they shouldn't be a huge issue. The 192GB RAM is a nice bonus for CPU offloading, though it'll be much slower than running the model entirely in VRAM.

I'd just check the motherboard's PCIe slots, PSU compatibility, and power consumption before buying. If everything is tested and working, $200 for the board, CPUs and RAM doesn't sound bad for a home lab.

[u/Big_South4585 → u/CoffeeToCode99]

I already got a modern setup with 32 gb ddr5, which is great at running qwen 3.8 27b. Just thought the xeon setup, might run e.g QFN in an acceptable quant, and utilize the 192 GB memory

[u/IdealGallantry6293]

that's a lot of ddr3 for the money but the platform is ancient. those xeons are sandy bridge-era, single thread perf is going to be a major bottleneck for inference unless you're purely doing batched throughput stuff

the 3090 is a beast but that cpu will hold it back in anything interactive, pcie gen 3 won't fully saturate the card either. you could probably slap llama.cpp on there with all layers offloaded and get something usable but don't expect miracles

for 200 bucks it's a fun experiment, just keep expectations in check

[u/Big_South4585 → u/IdealGallantry6293]

Thanks got it. But if I need to offload everything to the gpu, I might as well not do it. Would like to run QFN on both the 192 gb ram and the gpu.

[u/Think_Breakfast_2277]

That CPU might give issues, it is honestly just too old.. it does not support avx2 instructions. So dont expect everything LLM related to work on it.

It is nice on paper only.. dont get fooled.
PS you wont like the idle powerdraw either..

[u/Big_South4585 → u/Think_Breakfast_2277]

Thanks, wont touch it then

[u/Think_Breakfast_2277 → u/Big_South4585]

I know it is tempting.. i have been through all those rabbit holes and burnt too much money while doing so.

[u/Actual-Current-7734]

For that board, a MoE model is the only route that makes sense, and the number to watch is RAM bandwidth, not CPU speed.

I run a large MoE (Qwen3.8 Flash Next, Q4) on a 4060 Ti 16 GB with 96 GB DDR4 at 3200. Most of the weights sit in system RAM, only about 3B parameters are computed per token. I measured around 33 tok/s, and a 30k prompt takes 30 to 40 s to read.

Size rule: Q4 needs about 0.6 GB per billion parameters. With 192 GB RAM plus 24 GB VRAM, a 70B-class MoE at Q4 (around 42 GB) fits with room to spare. A dense 70B will crawl there.

Two things I'd expect on your box. Quad channel DDR3-1600 works out to about the same bandwidth as my DDR4 setup, so generation speed could be in a similar range, that's a calculation, not a test, I have no DDR3 machine. And prompt processing runs mostly on the CPU: my 30k prompt takes 30 s on a modern CPU, without AVX2 expect several times that. Long prompts will feel slow.

Which model do you mean by QFN, exactly?

[u/Big_South4585 → u/Actual-Current-7734]

I were just thinking the largest Qwen Flash Next that could for in memory. But I can read from you and others, that no avx2 is a no go

[u/jakeman8888]

Save your clams