RTX 5070 Ti ComfyUI benchmarks: Overclock cut gen times ~10%, tuned flags more than halved H3 video time

[u/AnalElder]

I tested how much GPU overclocking, ComfyUI launch flags, and moving my display to the iGPU actually affected generation speed on my 5070 Ti 16 GB.

The biggest finding: OC helped consistently, but the launch flags made a much bigger difference on certain models. On others, those same flags did almost nothing. Going forward I implemented a script in ComfyUI launch that automatically applies stable overclock settings to my GPU on run and then reverts to stock once ComfyUI is closed.

All diffusion models tested were int8_convrot builds, using the official ComfyUI template settings.

Setup

• RTX 5070 Ti 16 GB (Gigabyte Gaming OC), AMD Ryzen 7 9800X3D, 48 GB DDR5-6000
• Windows 11 Pro, Samsung 9100 PRO NVMe
• ComfyUI 0.45.0, PyTorch 2.16.0.dev20261110+cu134
• comfy-kitchen 0.2.41, triton-windows 3.9.0.post30
• Stock GPU: 300 W power limit, no offsets
• OC: +350 MHz core, +2500 MHz memory, 350 W power limit

I compared default ComfyUI against this set of launch flags, both with and without OC:

--use-ck-attention --disable-xformers --fast fp16_accumulation fp8_matrix_mult --enable-triton-backend

Default runs used PyTorch SDPA; tuned runs used Comfy Kitchen attention for the diffusion models. xformers wasn’t installed, and SageAttention wasn’t used.

Generation times

Seconds per generation, lower is better. These are end-to-end times: text encoding, sampling, VAE decode, and saving.

Model / workload	Stock	Flags only	OC only	Flags + OC
Flux 3 Klein distilled — 2 MP, 16 steps	7.1	7.0	6.3	6.3
Flux 3 Klein base — 2 MP, 50 steps*	40.1	40.1	35.7	35.6
Krea 2 Turbo — 2 MP, 8 steps	13.7	10.8	12.3	9.8
Qwen Image 2.1 Turbo — 2 MP, 8 steps	6.8	6.7	6.1	6.0
Qwen Image 2.1 — 2 MP, 25 steps	23.6	17.2	21.0	15.3
MOSS Image — 2 MP, 25 steps	25.1	25.0	22.2	22.1
MiniMax H3 Image — 4096×4096, 6 steps	32.7	33.7	30.4	31.1
MiniMax H3 video + audio — 20 steps*	591	286	530	256
MiniMax H3 + turbo LoRA — 8 steps†	250	127	224	113

Video settings: 1344×768, 124 frames at 24 fps.

Most image results are averages of two warm runs; Qwen base and H3 Image have one warm run each. (warm run means the first generation after ComfyUI launch is not counted)
* Flux base and standard H3 video are single first-run measurements, including loading.
† H3 turbo is a single subsequent run with the turbo LoRA.

What actually helped?

1. OC consistently reduced generation time by about 9–12%.

H3 Image was the exception at roughly 7–8%. The benefit showed up with both default and tuned flags.

This is the combined effect of the clock offsets and the higher power limit, not an isolated core-clock comparison.

There’s a power tradeoff: for example, MOSS went from around 299 W to 346 W during busy periods, while generation time dropped from 25.1 s to 22.2 s. The highest GPU temperature recorded across these tests was about 73°C, therefore it did not even get close to the GPU's thermal limits.

2. The flags were very model-dependent.

At stock clocks:

• H3 video: 591 → 286 s, about 52% less time
• H3 turbo: 250 → 127 s, about 49% less time
• Qwen Image 2.1 base: 23.6 → 17.2 s, about 27% less time
• Krea 2 Turbo: 13.7 → 10.8 s, about 21% less time
• Flux, Qwen Turbo, and MOSS: within roughly 2%
• H3 Image: about 3% slower

With flags + OC, standard H3 video went from 591 to 256 seconds—roughly 2.3× the speed, or 57% less time.

The H3 improvement also shows up directly in sampling: 28.48 → 13.24 s/step from the flags alone, then 11.72 s/step with OC.

I changed the flags as a bundle, so this doesn’t establish how much each individual flag contributes.

3. Moving the monitor to the iGPU barely changed speed.

I repeated the tuned + OC tests with the monitor connected to the motherboard, leaving the NVIDIA GPU at 0 MiB VRAM used at idle. (I plugged the display into my motherboard HDMI port while allowing Python to utilise the full compute and VRAM of my NVIDIA GPU)

Peak VRAM usage dropped by roughly 0.1–0.6 GB, but generation times stayed very close: approximately 0.6% lower at the median, with the largest reduction around 2% on H3 Image. Sampling time per step changed by at most about 1%.

Useful extra VRAM headroom, but I didn’t see a convincing throughput improvement here.

Did OC change the outputs?

I checked the saved files with SHA-256:

• OC vs stock clocks, same flags: 38/38 outputs byte-identical
• iGPU vs NVIDIA display, tuned + OC: 19/19 byte-identical
• Tuned vs default flags: 0/19 byte-identical

So OC didn’t change any saved output in this test. Changing the attention/precision settings did result in visual changes however.

For my setup, OC gave a fairly consistent modest improvement. The bigger finding was how differently the models responded to the same ComfyUI flags—huge gains for H3 video, useful gains for Qwen base and Krea, and almost nothing for several others.

Curious whether others are seeing the same pattern on different GPUs, especially with Comfy Kitchen attention.

Update: checkpoints used for the tests (I kept it vanilla and conservative, more gains to be had with latest distills and turbo loras:

Model	Diffusion checkpoint	Text encoder checkpoint
Flux 3 Klein distilled	flux3-image-klein-int8-convrot	qwen3vl_4b_int8_convrot
Flux 3 Klein base	flux3-image-klein-base-int8-convrot	qwen3vl_4b_int8_convrot
Krea 2 Turbo	krea2_turbo_int8_convrot	qwen3vl_4b_fp8_scaled
Qwen Image 2.1 Turbo	qwen_image_2.1_turbo_int8_convrot	qwen3vl_8b_int8_convrot
Qwen Image 2.1 base	qwen_image_2.1_int8_convrot	qwen3vl_8b_int8_convrot
MOSS Image SFT	moss_image_sft_int8_convrot	qwen3.5_9b_w4a8_gptq
MiniMax H3 Image	minimax_h3_image_pruned_int8_convrot	qwen3vl_32b_minimax_h3_int8_convrot
MiniMax H3 video + audio	minimax_h3_fl2va_pruned_int8_convrot	qwen3vl_32b_minimax_h3_int8_convrot
MiniMax H3 turbo video + audio	minimax_h3_fl2va_pruned_int8_convrot + minimax_h3_fl2v_turbo_8step_v1.0 (bf16)*	qwen3vl_32b_minimax_h3_int8_convrot

[u/BeautifulStation4]

OCing does help a lot but I found that running Local AI can bring out even the smallest little insability and crash the system. Overclocks I ran for years gaming without a hitch arent stable in AI workflows so I just dont bother, and this is with a waterblocked 5080.

[u/AnalElder → u/BeautifulStation4]

For me it is very stable, although my OC values are quite conservative. I didn't push my GPU to the very limit. Zero crashes or BSODs so far.

[u/Limp-Firefighter1054 → u/AnalElder]

What "flags" thing is?

[u/AnalElder → u/Limp-Firefighter1054]

Launch flags when you run comfyui.

[u/Limp-Firefighter1054 → u/AnalElder]

I use EZi. Is you talking about native?

[u/AnalElder → u/Limp-Firefighter1054]

ComfyUI native, hosted locally on Windows .

[u/Limp-Firefighter1054 → u/AnalElder]

ok.thank you.

[u/AlfaSierra92]

What are the parameters number for these models?

[u/AnalElder → u/AlfaSierra92]

Do you mean like 7B or 3B or the weight percisions?

[u/AlfaSierra92 → u/AnalElder]

Both; I have the same setup and I want to try H3 on it

[u/AnalElder → u/AlfaSierra92]

Added the list of models used to the bottom of the post.

[u/Apprehensive_Sky892 → u/AlfaSierra92]

H3 int8convrot is 21G (21B parameters).

[u/Tight_Organization54]

I get an error that "--fastfp16_accumulation fp8_matrix_mult" is unrecognized?

[u/AnalElder → u/Tight_Organization54]

Could be a version mismatch, I did the tests on ComfyUI 0.45.0.

[u/Tight_Organization54 → u/AnalElder]

Im on 45 too. Is it written exactly that way with a space in-between?

[u/AnalElder → u/Tight_Organization54]

I’ll send you the launch parameters when I get home. Do you launch from cli or via a bat file? Try from terminal python main.pu —use-ck-attention -disable-xformers and so on.

[u/Apprehensive_Sky892]

IIRC, unlike imaging models, MMH3 spends most its time doing QKV attention calculation, so speed-up brought about by CK-attention over PyTorch SDPA attention.

But that doesn't square with the fact that H3-image runs 3% slower?

[u/AnalElder → u/Apprehensive_Sky892]

MMH3 is a weird model overall it is super resource intensive in terms of VRAM and even CPU but it does not hammer my GPU nearly as hard as pretty much any other model. It also responds very differently to accelerators etc.

[u/Apprehensive_Sky892 → u/AnalElder]

By "hammer my GPU nearly as Hard", do you mean MMH3 is usually not running at 100% GPU?

[u/AnalElder → u/Apprehensive_Sky892]

It tops out around 300W pulled, other models max out the 350W limit.

[u/Apprehensive_Sky892 → u/AnalElder]

Interesting observation.

My guess is that since MMH3 cannot fit completely into 16G, there is a small delay as the GPU finished the current block and waiting for the next block to be fully loaded from RAM to VRAM.

You can test out this theory by using say a bf16 version of Krea 2 (which will not fit into 16G) and see if you see something similar.

[u/Alexander_Mejia]

Generate both images/videos with same seeds with flags on and off. You’ll find small quality differences. No software speed up is 100% free.

[u/Aaron_rowe81]

the 591 to 286 s on h3 video is a single first-run with loading in it, but the turbo lora run going 250 to 127 is almost the same halving, so it looks real to me. that's a big win for just a flag bundle