RuntimeError: CUDA out of memory. Tried to allocate - Can I solve this problem?

Hello everyone. I am trying to make CUDA work on open AI whisper release. My current setup works just fine with CPU and I use medium.en model

I have installed CUDA-enabled Pytorch on Windows 10 computer however when I try speech-to-text decoding with CUDA enabled it fails due to ram error

RuntimeError: CUDA out of memory. Tried to allocate 70.00 MiB (GPU 0; 4.00 GiB total capacity; 2.87 GiB already allocated; 0 bytes free; 2.88 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF

I disabled and enabled the graphic card before running the code - thus the VGA ram was 100% empty. Tested on my laptop so it has another GPU as well. Therefore, my GTX 1050 was literally using 0 MB of memory already

My computer also has 32 GB ram and CPU synthesis working very well but just too slow. In 7 hours processed only 1 hour of speech.

My installed version :

Name: torch
Version: 1.12.1+cu116
Summary: Tensors and Dynamic neural networks in Python with strong GPU acceleration
Home-page: pytorch org
Author: PyTorch Team
Author-email: packages pytorch org
License: BSD-3
Location: c:\python399\lib\site-packages
Requires: typing-extensions
Required-by: whisper, torchvision, torchaudio

Do you have any suggestions to solve this problem?

The problem could be the GPU memory used from loading all the Kernels PyTorch comes with taking a good chunk of memory, you can try that by loading PyTorch and generating a small CUDA tensor and then check how much memory it uses vs. how much PyTorch says it has allocated.
There has been work to put PyTorch on a bit of a diet there (e.g. the “JITerator”) but I’m not sure about the state, in particular on Windows.

Fun fact: In the olden times, PyTorch would print “Buy more RAM” along with the error message, but then things got all serious. :slight_smile:

Best regards


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Could you give me instructions to run and see? I really don’t know how to do what you are saying :smiley: Can we assign somehow virtual ram to the GPU? I am ok with it being slower than dedicated GPU ram

I started considering purchasing 12 GB RTX 3060 card what you think?

Start Python and do import torch ; a = torch.ones(1, device="cuda") or so. And then check the GPU memory usage (it’s nvidia-smi on Linux, I don’t quite know on windows).

Best regards


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Thank you very much.

Also I just ordered 12 gb RTX 3060 (411$ in Turkey almost equal to my monthly salary)

I hope it can run big model

These GPUs are decidedly not cheap. I feel for you!

Hey, bro. Did you solve this problem after changing your GPU to the one with more memory?

After GPU change it works great
I am daily using it
Here my video : How to do Free Speech-to-Text Transcription Better Than Google Premium API with OpenAI Whisper Model - YouTube

Hi @FurkanGozukara, how did you solve the issue of the Cuda memory?

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