Fixes to links

Signed-off-by: Pablo Ribalta Lorenzo <[email protected]>

PyTorch/Segmentation/nnUNet/README.md

@@ -562,7 +562,7 @@ The following sections provide details on how to achieve the same performance an
 
 #### Training accuracy results
 
-##### Training accuracy: NVIDIA DGX A100  (8x A100 40GB)
+##### Training accuracy: NVIDIA DGX A100 40G
 
 Our results were obtained by running the `python scripts/train.py --gpus {1,8} --fold {0,1,2,3,4} --dim {2,3} --batch_size <bsize> [--amp]` training scripts and averaging results in the PyTorch 20.12 NGC container on NVIDIA DGX-1 with (8x V100 16GB) GPUs.
 
@@ -586,9 +586,9 @@ Our results were obtained by running the `python scripts/train.py --gpus {1,8} -
 
 #### Training performance results
 
-##### Training performance: NVIDIA DGX A100 (8x A100 80GB)
+##### Training performance: NVIDIA DGX A100 40G
 
-Our results were obtained by running the `python scripts/benchmark.py --mode train --gpus {1,8} --dim {2,3} --batch_size <bsize> [--amp]` training script in the NGC container on NVIDIA DGX A100 (8x A100 80GB) GPUs. Performance numbers (in volumes per second) were averaged over an entire training epoch.
+Our results were obtained by running the `python scripts/benchmark.py --mode train --gpus {1,8} --dim {2,3} --batch_size <bsize> [--amp]` training script in the NGC container on NVIDIA DGX A100 40G GPUs. Performance numbers (in volumes per second) were averaged over an entire training epoch.
 
 | Dimension | GPUs | Batch size / GPU  | Throughput - mixed precision [img/s] | Throughput - TF32 [img/s] | Throughput speedup (TF32 - mixed precision) | Weak scaling - mixed precision | Weak scaling - TF32 |
 |:-:|:-:|:--:|:------:|:------:|:-----:|:-----:|:-----:|
@@ -632,9 +632,9 @@ To achieve these same results, follow the steps in the [Quick Start Guide](#quic
 
 #### Inference performance results
 
-##### Inference performance: NVIDIA DGX A100 (1x A100 80GB)
+##### Inference performance: NVIDIA DGX A100 40G
 
-Our results were obtained by running the `python scripts/benchmark.py --mode predict --dim {2,3} --batch_size <bsize> [--amp]` inferencing benchmarking script in the PyTorch 20.10 NGC container on NVIDIA DGX A100 (1x A100 80GB) GPU.
+Our results were obtained by running the `python scripts/benchmark.py --mode predict --dim {2,3} --batch_size <bsize> [--amp]` inferencing benchmarking script in the PyTorch 20.10 NGC container on NVIDIA DGX A100 40G GPU.
 
 
 FP16