# Bitsandbytes and CUBLAS\_STATUS\_NOT\_INITIALIZED error

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**Category:** Beginners\
**Created:** [August 20, 2025, 5:22pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fbitsandbytes-and-cublas-status-not-initialized-error%2F167455 "2025-08-20T17:22:32Z")\
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**Author:** ![John6666](https://sea2.discourse-cdn.com/hellohellohello/user_avatar/discuss.huggingface.co/john6666/32/27664_2.png) [@John6666](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Fu%2FJohn6666)%5C
**Post date:** [August 28, 2025, 12:41pm UTC](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fbitsandbytes-and-cublas-status-not-initialized-error%2F167455%2F18 "2025-08-28T12:41:58Z")

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> on Kaggle T4x2

To avoid errors by having PyTorch recognize only a single GPU in that environment:  
`os.environ["CUDA_VISIBLE_DEVICES"] = "0"`

Instead, when aiming to fully utilize all GPUs, [it’s preferable to explicitly rerun the script using a multi-GPU-compatible backend from Accelerate or similar tools](/static-proxy?url=https%3A%2F%2Fdiscuss.huggingface.co%2Ft%2Fmodel-size-and-vram-requirements%2F167761%2F4). This approach handles advanced operations internally, making it more complex. I believe Kaggle has documentation on this…

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