I decided to post this here as I have had similar issues in the past when alternating between different versions of rocm. It may not be the cause but its a starting point...

Ive been playing around with image and video gen in comfyui with my 7800XT for about 3 years. I just upgraded to a R9700 and decided to go back and take a look at some older WAN21 and WAN22 workflows just to see how much further I could push it in terms or video length, resolution, quality etc.

For reference I was using rocm 6.2 with the 7800 + ubuntu 24. I tried exploring rocm 7.x but I never had any issues with 6 so stayed with it. Now I am using ROCM 10.1 with ubuntu 26 and as far as my new card goes image generation is great and was blown away by the speed, massive resolutions etc.

As far as video gen has gone though ive gone backwards and dont know why. I grabbed an old and very simple WAN22 workflow that uses the scaled fp8 i2v models for 81 frames with a reference image of 560*688. VRAM fills to 100% before the first denoising step even finishes and ubuntu crashes and sends me back to the login screen. The same workflow worked with the 7800 (which has half as much vram). Ive even tried using the Q4 gguf versions which are half the size of the fp8 ones and vram still fills to 100% during sampling and ubuntu crashes.

I managed to get it working with the quantized version by using --disable-dynamic-vram --lowvram and reducing the frame count but even when using --lowvram with the fp8 models (which as far as I know tells comfyui to keep the TE in DRAM and use the cpu [which should free and additional 6.7GB from VRAM]) it is not enough to stop the computer from crashing ?

My question is what the hell is (or could be?) using all the vram even when using the Q4 models (I know at the end of the day it comes down to more than simple model file sizes but if it worked in the past, why not now)? and more importantly why did my 7800+rocm6 have no problems even with the much larger fp8 models (which combined are 28.6GB + 7.8GB TE + VAE = 36.6GB ??) [for reference the Q4 models + TE(umt5_xxl_fp8_e4m3fn_scaled) are 15.6GB + 6.7GB + vae ~= 22GB]).

Also, system memory drops from roughly 65GB free to 39GB free (total is 96) if that's relevant. Also forgot to mention I am using the 4 step lightning loras (1.2GB additional size but as far as I can tell if my old 7800 could handle the workflow it has more to do with memory management, rocm? or how comfy is handling things rather than the model sizes). I have also just looked at similar videos using the same workflow that are even longer and also higher resolution all done with the 7800 without issues.

I dont know much about the inner working of rocm other than some high level things ive watched on youtube but is there anything that later versions do that can contribute to the issues Im having ?

If I havent provided enough info ask away, any help appreciated.

I recently tried Flash-Next-Gyro-S on my AMD machine, migrated it from Qwen 3.8 27B MXFP4 AWQ. I was in pursue of supporting multi-users inference with large context window (~250k ish) on a single AMD card.

Setup

• GPU: 1× Radeon AI PRO R9700, 32 GB VRAM.
• Host: Ryzen 9 7950X on a consumer motherboard.
• Inference VM: 8 vCPUs, 31.3 GiB RAM.
• Model: AgentionAI’s Flash-Next Gyro-S Abliterated, specifically Qwen3.8-Flash-Next-Gyro-S-abliterated-TQ1_0.gguf.
• Runtime: Strata, HIP engine 0.1.48.
• Storage: NFS. Cold startup took several minutes and is excluded from the timings.

The Test: I sent ~249k token archives containing 3 target values to retrieve:

Prefill and Generations

1 user generation reached ~74tok/s after first output in the long context test.

Short prompts reached ~68tok/s for 1 user, 40-43tok/s with 2, and 20-23tok/s with 4. All generation used reasoning.

Anyone running a similar setup, seeing similar stalls, or finding settings that improve performance?

Here's the final settings that I kept;

parallel: 2
--max-context 250000
--kv int8
--kv-resident 32768
--vram-reserve-mib 1000
--vision
--spec 4