My biggest concerns with this assessment is the lake of dataset diversity. Sure, you can get one method to outperform another on one or two datasets, but to be able to do so across many datasets, all of various sizes, is much much harder.

From what I can tell, the open source StatsForecast was able to outperform BigQuery for an extremely small dataset (Citibike Trips) and one large dataset (Liquor Sales). Granted the much larger dataset, to me, is much more impressive to outperform upon than the smaller. But to make such a definitive conclusion that Open Source is better than commercial would require testing across a plethora of datasets, all of different sizes, domains, etc.

Where at? Everything I can find is like 3$ . Cheapest I can find is google colab $10 for 7.5 hours, but you’re limited by ram and your node will drop at any given time

I’ll definitely check it out!

Yes, they had 40GB A100 GPUs in 2015

If you’re using Tensorflow, adding multiple GPUs is extremely easy. Just have to call some functions and make a strategy:

https://www.tensorflow.org/guide/distributed_training