# Using multiple GPUs for zero-shot-classification's pipeline with bart-large-mnli model

**URL:** <https://discuss.huggingface.co/t/using-multiple-gpus-for-zero-shot-classifications-pipeline-with-bart-large-mnli-model/77249>\
**Category:** 🤗Transformers\
**Created:** [March 13, 2024, 11:32am UTC](https://discuss.huggingface.co/t/using-multiple-gpus-for-zero-shot-classifications-pipeline-with-bart-large-mnli-model/77249 "2024-03-13T11:32:10Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![naveenmarthala](https://avatars.discourse-cdn.com/v4/letter/n/76d3ee/32.png) [@naveenmarthala](https://discuss.huggingface.co/u/naveenmarthala)\
**Post date:** [March 13, 2024, 11:32am UTC](https://discuss.huggingface.co/t/using-multiple-gpus-for-zero-shot-classifications-pipeline-with-bart-large-mnli-model/77249/1 "2024-03-13T11:32:10Z")

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I am using [facebook’s bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) for zero-shot-classification. I have 4 “Nvidia Tesla V100-PCIE-16GB” GPUs available in my environment. I have around 500K different texts in a pandas dataframe, I would like to pass to get predictions for some classes. I am currently using pandas apply and each row/text takes 1.5 second to process and I see 27% usage using 1966MiB from 16384MiB available and the remaining 3 GPUs are not being used at all. How do I pass multiple texts at a time to efficiently process all of my data.