# CUDA error: out of memory - (Minimax only)

> Source: <https://discuss.huggingface.co/t/cuda-error-out-of-memory-minimax-only/178571#post_1>
> Published: 2026-08-11 06:33:02+00:00

Hi!

i only get the following error message with minimax

Wan 2.2 is running normal without any memory error

- To create a public link, set
`share=True`

in `launch()`

.

Loading Model ‘ckpts\MiniMax-H3-FL2VA-pruned_rank8_int8_convrot.safetensors’ …

Loading Text Encoder ‘ckpts\Qwen3-VL-32B-Instruct\Qwen3-VL-32B-Instruct-layer50_quanto_bf16_int8.safetensors’ …

Loading MiniMax H3 Video VAE ‘ckpts\MiniMax-H3-video_vae_fp16.safetensors’…

************ Memory Management for the GPU Poor (mmgp 3.7.12) by DeepBeepMeep ************

Switching to partial pinning since full requirements for pinned models is 51105.5 MB while estimated available reservable RAM is 26184.6 MB. You may increase the value of parameter ‘perc_reserved_

mem_max’ to a value higher than 0.40 to force full pinnning.

Partial pinning of data of ‘transformer’ to reserved RAM

Found 200 tied weights for a total of 0.00 MB, last : blocks.49.attn.q_proj.output_scale ↔ blocks.49.attn.v_proj.output_scale

The model was partially pinned to reserved RAM: 100 large blocks spread across 18547.98 MB

Hooked to model ‘transformer’ (MiniMaxH3Model)

Async loading plan for model ‘transformer’ : base size of 58.22 MB will be preloaded with a 370.96 MB async circular shuttle

Partial pinning of data of ‘text_encoder’ to reserved RAM

Unable to pin more tensors for this model as the maximum reservable memory has been reached (5331.80).

The model was partially pinned to reserved RAM: 24 large blocks spread across 5331.80 MB

Hooked to model ‘text_encoder’ (Qwen3VLTextModel)

Async loading plan for model ‘text_encoder’ : base size of 1483.75 MB will be preloaded with a 465.16 MB async circular shuttle

Unable to pin data of ‘vision_encoder’ to reserved RAM as there is no reserved RAM left. Transfer speed from RAM to VRAM may be slower.

Hooked to model ‘vision_encoder’ (Qwen3VLVisionModel)

Unable to pin data of ‘vae’ to reserved RAM as there is no reserved RAM left. Transfer speed from RAM to VRAM may be slower.

Hooked to model ‘vae’ (ModuleDict)

Unable to pin data of ‘video_encoder’ to reserved RAM as there is no reserved RAM left. Transfer speed from RAM to VRAM may be slower.

Hooked to model ‘video_encoder’ (ModuleDict)

Unable to pin data of ‘audio_vae’ to reserved RAM as there is no reserved RAM left. Transfer speed from RAM to VRAM may be slower.

Hooked to model ‘audio_vae’ (MiniMaxH3AudioVAE)

Traceback (most recent call last):

File “C:\pinokio\api\wan.git\app\wgp.py”, line 7589, in generate_media

samples = wan_model.generate(

^^^^^^^^^^^^^^^^^^^

File “C:\pinokio\api\wan.git\app\models\minimax_h3\pipeline.py”, line 31, in wrapped

return method(self, *args, **kwargs)

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File “C:\pinokio\api\wan.git\app\venv\Lib\site-packages\torch\utils_contextlib.py”, line 124, in decorate_context

return func(*args, **kwargs)

^^^^^^^^^^^^^^^^^^^^^

File “C:\pinokio\api\wan.git\app\models\minimax_h3\pipeline.py”, line 407, in generate

self._add_image_condition(image_start, 0, presentation, visual_latents, keyframes)

File “C:\pinokio\api\wan.git\app\models\minimax_h3\pipeline.py”, line 267, in _add_image_condition

latent = self._encode_video(video)

^^^^^^^^^^^^^^^^^^^^^^^^^

File “C:\pinokio\api\wan.git\app\models\minimax_h3\pipeline.py”, line 231, in _encode_video

return self.vae.encode_condition(video.unsqueeze(0).to(device=self.device, dtype=self.vae._model_dtype), keep_all_latents=keep_all_latents).cpu()

^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^

File “C:\pinokio\api\wan.git\app\venv\Lib\site-packages\torch\utils_device.py”, line 109, in **torch_function**

return func(*args, **kwargs)

^^^^^^^^^^^^^^^^^^^^^

torch.AcceleratorError: CUDA error: out of memory

Search for `cudaErrorMemoryAllocation' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. For debugging consider passing CUDA_LAUNCH_BLOCKING=1 Compile with `

TORCH_USE_CUDA_DSA` to enable device-side assertions.

Error Queue autosaved successfully to error_queue.zip