{"id":285,"hash":"1ca449d9033480d710bd0bd9690fab2d3c1d887a4d83fb6d736c33da0b9f61e6","pattern":"CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling cublasCreate(handle)","full_message":"I got the following error when I ran my PyTorch deep learning model in Google Colab\n\n/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in linear(input, weight, bias)\n   1370         ret = torch.addmm(bias, input, weight.t())\n   1371     else:\n-> 1372         output = input.matmul(weight.t())\n   1373         if bias is not None:\n   1374             output += bias\n\nRuntimeError: CUDA error: CUBLAS_STATUS_ALLOC_FAILED when calling `cublasCreate(handle)`\n\nI even reduced batch size from 128 to 64 i.e., reduced to half,  but still, I got this error. Earlier, I ran the same code with a batch size of 128 but didn't get any error like this.","ecosystem":"pypi","package_name":"pytorch","package_version":null,"solution":"No, batch size does not matter in this case.\n\nThe most likely reason is that there is an inconsistency between number of labels and number of output units.\n\nTry printing the size of the final output in the forward pass and check the size of the output\n\nprint(model.fc1(x).size())\n\nHere fc1 would be replaced by the name of your model's last linear layer before returning\n\nMake sure that label.size()  is equal to prediction.size() before calculating the loss\n\nAnd even after fixing that problem, you'll have to restart the GPU runtime (I needed to do this in my case when using a Colab GPU)\n\nThis GitHub issue comment might also be helpful.","confidence":0.95,"source":"stackoverflow","source_url":"https://stackoverflow.com/questions/61473330/cuda-error-cublas-status-alloc-failed-when-calling-cublascreatehandle","votes":54,"created_at":"2026-04-19T04:41:44.174738+00:00","updated_at":"2026-04-19T04:51:56.205560+00:00"}