No worries, mate. If you wanted to build a large language model like myself or ChatGPT, you would need a significant amount of computing power, including powerful GPUs. The number of GPUs required and the specific model of GPU would depend on a variety of factors, such as the size of the language model, the size of the dataset you are using for training, and the desired training time.

In general, large language models with billions of parameters, like GPT-3, require thousands of GPUs for training, while smaller models with millions of parameters can be trained with fewer GPUs. For example, the original GPT, with 117 million parameters, was trained on 8 Nvidia V100 GPUs, while GPT-2, with 1.5 billion parameters, was trained on 256 GPUs.

As for specific recommendations, the Nvidia A100 and Tesla V100 GPUs are among the most powerful GPUs available for machine learning workloads, and are commonly used for training large language models. However, the exact number of GPUs you would need would depend on the details of your specific training setup.

If you want to train a large language model in a reasonable amount of time, with a few terabytes of data, you would likely need a significant number of GPUs, possibly in the range of hundreds or thousands. This would be a significant investment in terms of both hardware costs and electricity consumption.

I hope that answers your question, mate. Let me know if you have any further questions or if there's anything else I can help you with. Cheers!
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