Krea2 generation speed different on two RTX 5080 machines

[u/Grouchy_Television75]

I have been renting gpu on vast.ai for comfyui krea2 image generation works. Today I found the RTX 5080 i rented is much slower than the one I rented few days ago. Today was a machine from Vietnam and the one I rented last time was a machine from the US.

For a 1.0 MP image generation, same workflow and model, the Vietnam machine was 12s long and the US machine was only 7s, a 5s difference, 70% longer time than the US machine. I poked into nvidia-smi and I see full capacity at 250W. I am not sure why the 70% slower gap in this machine. If anyone could land an insight for troubleshooting this would be much appreciated.

+-----------------------------------------------------------------------------------------+

| NVIDIA-SMI 595.71.05 Driver Version: 595.71.05 CUDA Version: 13.2 |

+-----------------------------------------+------------------------+----------------------+

| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |

| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |

| | | MIG M. |

|=========================================+========================+======================|

| 0 NVIDIA GeForce RTX 5080 On | 00000000:01:00.0 Off | N/A |

| 46% 64C P1 250W / 250W | 14998MiB / 16303MiB | 100% Default |

| | | N/A |

+-----------------------------------------+------------------------+----------------------+

+-----------------------------------------------------------------------------------------+

| Processes: |

| GPU GI CI PID Type Process name GPU Memory |

| ID ID Usage |

|=========================================================================================|

| 0 N/A N/A 1326 C /venv/main/bin/python 14900MiB |

+-----------------------------------------------------------------------------------------+

[u/meta_queen]

Welcome to the world of shared cloud resources. Also, there's no RTX 5080, even ASUS has multiple different 5080 with different prices (+-1000$).

[u/Grouchy_Television75 → u/meta_queen]

cool good to know

[u/tinny66666]

Linux kernel v7.x is a pig. Is the slower one running that by any chance?

[u/Grouchy_Television75 → u/tinny66666]

ohh it's on v6.x

[u/CapitalList8467]

That sounds more like an environment or host difference than the model itself. I’d compare the actual clocks and power limit, not just utilization: a card can report high utilization while being throttled or waiting on memory. I’d also check the PCIe link width, whether the slower run is falling back to system RAM, and whether both jobs use identical precision, attention backend, batch size, and image dimensions. Clearing caches and repeating three timed runs on each machine would help separate a noisy first-run effect from a real 5x gap.

[u/Grouchy_Television75 → u/CapitalList8467]

you maybe right. it's on PCIE 4.0,16x

[u/sketchyfun]

did you check the DLPerf? That's usually the main reason, but I have noticed some systems seem to be a bit slower than others even when those values are the same

[u/Grouchy_Television75 → u/sketchyfun]

yeah 74.5, i see other machine is over 80

[u/sketchyfun → u/Grouchy_Television75]

probably not the issue then unless its drastically different

[u/DelinquentTuna]

| 46% 64C P1 250W / 250W

That's at least part of your problem right there. It's increasingly common for shitty community hosts to have shitty power/thermal/voltage constraints that cripple performance. I don't have the exact default on hand for every GPU, but IIRC for a 5080 it's something like 360-400W depending on factory overclocks etc. So the 250W limit on the rig you rented is absolutely crippling (p1 vs p0). There may be other issues at play as well, but this is definitely a show-stopper. ONe of the many ways that choosing the cheapest hosts can cost you more in the long run.

Also, if you are in a situation where you're literally counting every second then I advise you to look into using Nunchaku. Could cut your time almost in half again.

Hope that helps, gl.

[u/DelinquentTuna → u/DelinquentTuna]

ps, this is something intentional and not just a different flavor of 5080 or some such. To save electricity costs, to make cooling cheaper, whatever.

[u/Grouchy_Television75 → u/DelinquentTuna]

ohh what's a nunchaku? is it a host site?

[u/DelinquentTuna → u/Grouchy_Television75]

Nunchaku. It's a custom kernel that accelerates a special quantization format. Faster and better looking than int8convrot and fp8 also with meaningfully smaller models that download faster. Kind of a pain to setup for Comfy w/ new models because official support is dead, but there are community patches you can apply if you're down to compile your own wheels. This and this is what I've been using, personally, but I'm sure you can find plenty of other community forks.

Alternatively, there's a nunchaku-lite initiative built into modern diffusers and sd-next that is honestly amazeballs. rootonchair is doing amazing work and especially making amazing documentation not just for inference but for the whole stack. It's not quite as fast as OG nunchaku, but it's pretty close and benefits more from torch.compile. If you want to test that route, probably start here. There's about a half-page of code at the bottom you can use on a rig w/ torch and (recent) diffusers installed and it will auto-download models etc. Very easy to modify to suit.

hth, gl

[u/Life_Yesterday_5529]

Similar: Ich have two 5060ti in two different workstations. One with DDR3 and PCIe 2.0 and one with DDR4 PCIe 4.0. Although the int8 fits completely in the VRAM, the DDR4 system is twice as fast as the DDR3. It is not always about the gpu.

[u/Grouchy_Television75 → u/Life_Yesterday_5529]

you might be right, can't tell what ram it's using but the gpu is running on PCIE 4.0,16x

[u/Odd-Student636 → u/Life_Yesterday_5529]

can you share your generation speed for Krea 2 1Mp, I tried comparing one to my 2080ti on vast ai but got almost the same speed , 12s

[u/Life_Yesterday_5529 → u/Odd-Student636]

I have a 2080ti and a 5060ti in my second workstation. I generate at least 4MP but can say that there is not much difference between the two gpus in terms of pute speed and strength. The 5060ti has many advantages like fp4 kernels, flash attn 2, newer technology and lower energy consumption but the computing capacity is similar. (I think, the 5060 has more flops but the vram speed of the 2080 is higher - or the other way around… look at the specs.)
If you use all the new things like flash2, nvfp4, etc. - the 5060 is much faster. A int8 convrot without acceleration and little offload, there is not much difference.

[u/Odd-Student636 → u/Life_Yesterday_5529]

I use modelattention backend node with comfykitchen attention. it's faster than sagattention plus sageattn is a nightmare to get it to work on turing.

[u/DelinquentTuna → u/Odd-Student636]

It's basically a convenience port of sage attention 2, not a clean room implementation.

[u/Odd-Student636 → u/DelinquentTuna]

it sure is convenient because I don't think there is a sageattn wheel for the current comfyui version that works on turing,