Impossible to use T5 11b
# 🐛 Bug

## Information

Model I am using (Bert, XLNet ...):
T5 11B param
Language I am using the model on (English, Chinese ...):
English
The problem arises when using:
* [ x] the official example scripts: (give details below)
* [ ] my own modified scripts: (give details below)

The tasks I am working on is:
* [ ] an official GLUE/SQUaD task: (give the name)
* [x ] my own task or dataset: (give details below)

## To reproduce

Steps to reproduce the behavior:
```
summarizer = pipeline(task="summarization", model="t5-11b", tokenizer="t5-11b")
summary = summarizer(
	article,
	min_length=5, 
	max_length=100
	)
print("The Summary, ",summary[0]['summary_text'])
```

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## Expected behavior

As indicated in [T5's repo](https://github.com/google-research/text-to-text-transfer-transformer), they used Mesh Tensorflow, which is (according to them) the only way to run inference T5. This means the default CPU setting and even 1 GPU device setting would result in the following

1. CPU setting: run forever (my experience)
2. GPU setting: OOM (although I haven't tested this).

Meaning the current implementations of 3b and 11b would most likely render useless.

More testing needs to be done on whether this is the same for smaller models.

Get an output
<!-- A clear and concise description of what you would expect to happen. -->

## Environment info
<!-- You can run the command `python transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->
     
- `transformers` version: 2.7.0
- Platform: GCP
- Python version: 3.7.7
- PyTorch version (GPU?): 1.1.0 and no GPU
- Tensorflow version (GPU?):
- Using GPU in script?:
- Using distributed or parallel set-up in script?: no

Update

On a regular CPU machine (not using GPUs), here's the benchmarks you'll need to load these models into memory and run them:

Bart-large-cnn (default): 5GB of RAM
T5-small: 14GB
T5-base: 20GB
T5-large: 31GB
T5-3b: 68GB
T5-11b: 120GB

So, I was initially wrong: **You can run this on CPU, but you'll need a lot of RAM, or try out GPUs** 