Update README.md

README.md

@@ -116,9 +116,17 @@ The rest of the code is automated by the [Trainer](https://pytorch-lightning.rea
 ![PT to PL](docs/source/_images/lightning_module/pt_trainer.png)
 
 ## Testing Rigour
-All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).
+All the automated code by the Trainer is [tested rigorously with every new PR](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/tests).   
 
-In fact, we also train a few models using a vanilla PyTorch loop and compare with the same model trained using the Trainer to make sure we achieve the EXACT same results. [Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
+For every PR we test all combinations of:    
+- PyTorch 1.3, 1.4, 1.5    
+- Python 3.6, 3.7, 3.8   
+- Linux, OSX, Windows   
+- Multiple GPUs   
+
+**How does performance compare with vanilla PyTorch?**   
+We have tests to ensure we get the EXACT same results in under 600 ms difference per epoch. In reality, lightning adds about a 300 ms overhead per epoch.   
+[Check out the parity tests here](https://github.com/PyTorchLightning/pytorch-lightning/tree/master/benchmarks).
 
 Overall, Lightning guarantees rigorously tested, correct, modern best practices for the automated parts.
 
@@ -329,7 +337,7 @@ Lightning has out-of-the-box integration with the popular logging/visualizing fr
 
 
 ## Running speed
-Migrating to lightning does not mean compromising on speed! You can expect an overhead of about 600 ms per epoch comparing to pure PyTorch.
+Migrating to lightning does not mean compromising on speed! You can expect an overhead of about 300 ms per epoch compared with pure PyTorch.
 
 
 ## Examples