sync with #16860

docs/source/en/perf_hardware.mdx

@@ -17,15 +17,15 @@ limitations under the License.
 
 # Custom hardware for training
 
-The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer's excellent [blog post](https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/). 
+The hardware you use to run model training and inference can have a big effect on performance. For a deep dive into GPUs make sure to check out Tim Dettmer's excellent [blog post](https://timdettmers.com/2020/09/07/which-gpu-for-deep-learning/).
 
 Let's have a look at some practical advice for GPU setups.
 
 ## GPU
 When you train bigger models you have essentially three options:
 - bigger GPUs
 - more GPUs
-- more CPU and NVMe (offloaded to by [DeepSpeed-Infinity](deepspeed#nvme-support))
+- more CPU and NVMe (offloaded to by [DeepSpeed-Infinity](main_classes/deepspeed#nvme-support))
 
 Let's start at the case where you have a single GPU.
 
@@ -147,4 +147,4 @@ rm -r /tmp/test-clm; CUDA_VISIBLE_DEVICES=0,1 NCCL_P2P_DISABLE=1 python -m torch
 ```
 
 Hardware: 2x TITAN RTX 24GB each + NVlink with 2 NVLinks (`NV2` in `nvidia-smi topo -m`)
-Software: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`
+Software: `pytorch-1.8-to-be` + `cuda-11.0` / `transformers==4.3.0.dev0`