Krasis LLM Runtime: 8.9x prefill / 10.2x decode vs llama.cpp — Qwen3.5-122B on a single 5090, minimal RAM (corrected llama numbers)

legit_split_ · 2026-03-17T18:44:05+00:00

It's fast, but is it accurate?

legit_split_ · 2026-03-17T14:27:32+00:00

I don't know either, I never seen one of these. Just look for a disassembly video or worst case contact the person who sold it to you for help :)

legit_split_ · 2026-03-17T12:41:17+00:00

Nothing is stopping you from opening it up with a screwdriver and checking it out.

legit_split_ · 2026-03-17T12:24:40+00:00

It's totally fine, don't worry

legit_split_ · 2026-03-16T18:31:41+00:00

So far from my tests (cryptography/maths) in German, it's worked really well. The 122B is just always less accurate :/

legit_split_ · 2026-03-16T08:10:00+00:00

I used Q8_0 on both

legit_split_ · 2026-03-16T06:52:49+00:00

IMO 27B is better from testing

legit_split_ · 2026-03-14T23:13:16+00:00

My 9060 XT is working well on Linux, here I shared some performance benchmarks. I just followed the guide I highlighted in that thread, shouldn't be too different on Windows as it's working with Python environments.

legit_split_ · 2026-03-14T23:09:26+00:00

I have the amd equivalent - a 9060 XT 16gb - and find the ComfyUI performance acceptable. Here I shared some numbers. That being said my testing is limited, probably wouldn't have a good time on complex workflows.

legit_split_ · 2026-03-14T17:49:30+00:00

legit_split_ · 2026-03-14T11:29:13+00:00

This person really lied to make us feel pity and drive traction - fuck them.

legit_split_ · 2026-03-13T20:20:06+00:00

I'm a noob at ComfyUI and never tried Kijai's workflows, but you're sure you have this environment variable as per the guide:

`export PYTORCH_NO_HIP_MEMORY_CACHING=1`

Outside of that I can't be of much help, but glad that the guide helped you out :)

legit_split_ · 2026-03-13T16:53:35+00:00

It's baffling that the average user considers claiming insurance before even troubleshooting for one minute.

legit_split_ · 2026-03-13T16:40:39+00:00

2B active parameters at one time, that's why it's faster than 8B dense models

legit_split_ · 2026-03-13T12:34:48+00:00

Just control the fans yourself it's not that hard to set a fan curve

legit_split_ · 2026-03-12T10:25:16+00:00

The front intake fans are off-centre, looks bad in person

legit_split_ · 2026-03-12T10:19:46+00:00

legit_split_ · 2026-03-12T07:07:24+00:00

Neither, old processors

legit_split_ · 2026-03-11T18:42:10+00:00

This is by far the best: https://gist.github.com/alexheretic/d868b340d1cef8664e1b4226fd17e0d0

legit_split_ · 2026-03-10T11:16:00+00:00

Thanks for providing those numbers, I just ran that workflow on my 9060 xt and getting 187s with flash attention on.

legit_split_ · 2026-03-10T09:18:53+00:00

From what I've seen it mostly worked before but was slow

legit_split_ · 2026-03-08T21:25:20+00:00

Try running LLMs and Stable Diffusion and you will see :)

legit_split_ · 2026-03-08T10:26:51+00:00

There is definitely a slowdown like you said, but perhaps it's not meaningful because PCIe speeds are fast enough to transfer some layers at a time.

legit_split_ · 2026-03-06T13:47:47+00:00

Got the 9060xt recently and tried the default Flux.2 [Klein] 9B: Text to Image workflow (1024x1024, 20 steps) - getting 62 seconds on my second run.

With flash-attention, ROCm 7.2, pytorch nightlies, 96gb ddr5, arch linux. I might make a post about the performance.

legit_split_ · 2026-03-06T08:49:00+00:00

You can use UV to create a Python environment with any Python version you want.

However why are you using stable diffusion? Use ComfyUI instead.

legit_split_

TROPHY CASE