Hi @as-stevens,
I don’t know what blog post you’re referring to for using Cython to get 100x but I guess it really depends where the bottleneck is.
For t5 models, they are Seq2Seq models, and I would recommend to stick to PyTorch and  finding a way to optimize the hot path (decoder path). TF could work, but transformers currently can’t use various graph optimizations in TF (we’re working on it).
Or you can try to run it on our hosted inference API to alleviate the hassle of managing all the different layers: https://huggingface.co/pricing 33 (Some optimizations are only enabled for customers)
Hope that helps.
Cheers,
Nicolas