Fix Typo in Docs for GPU (#20509)

docs/source/en/perf_train_gpu_many.mdx

@@ -11,7 +11,7 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 
 # Efficient Training on Multiple GPUs
 
-When training on a single GPU is too slow or the model weights don't fit in a single GPUs memory we use a mutli-GPU setup. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. There are several techniques to achieve parallism such as data, tensor, or pipeline parallism. However, there is no one solution to fit them all and which settings works best depends on the hardware you are running on. While the main concepts most likely will apply to any other framework, this article is focused on PyTorch-based implementations.
+When training on a single GPU is too slow or the model weights don't fit in a single GPUs memory we use a multi-GPU setup. Switching from a single GPU to multiple requires some form of parallelism as the work needs to be distributed. There are several techniques to achieve parallism such as data, tensor, or pipeline parallism. However, there is no one solution to fit them all and which settings works best depends on the hardware you are running on. While the main concepts most likely will apply to any other framework, this article is focused on PyTorch-based implementations.
 
 <Tip>
 
@@ -31,7 +31,7 @@ The following is the brief description of the main concepts that will be describ
 4. **Zero Redundancy Optimizer (ZeRO)** - Also performs sharding of the tensors somewhat similar to TP, except the whole tensor gets reconstructed in time for a forward or backward computation, therefore the model doesn't need to be modified. It also supports various offloading techniques to compensate for limited GPU memory.
 5. **Sharded DDP** - is another name for the foundational ZeRO concept as used by various other implementations of ZeRO.
 
-Before diving deeper into the specifics of each concept we first have a look at the rough decision process when training large models on a large infrastructure. 
+Before diving deeper into the specifics of each concept we first have a look at the rough decision process when training large models on a large infrastructure.
 
 ## Scalability Strategy
 

docs/source/en/perf_train_special.mdx

@@ -13,8 +13,8 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 
 <Tip>
 
- Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [mutli-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
+ Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
 
 </Tip>
 
-This document will be completed soon with information on how to train on specialized hardware.
\ No newline at end of file
+This document will be completed soon with information on how to train on specialized hardware.

docs/source/en/perf_train_tpu.mdx

@@ -13,8 +13,8 @@ an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express o
 
 <Tip>
 
- Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [mutli-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
+ Note: Most of the strategies introduced in the [single GPU section](perf_train_gpu_one) (such as mixed precision training or gradient accumulation) and [multi-GPU section](perf_train_gpu_many) are generic and apply to training models in general so make sure to have a look at it before diving into this section.
 
 </Tip>
 
-This document will be completed soon with information on how to train on TPUs.
\ No newline at end of file
+This document will be completed soon with information on how to train on TPUs.

docs/source/en/performance.mdx

@@ -24,7 +24,7 @@ This document serves as an overview and entry point for the methods that could b
 
 ## Training
 
-Training transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where you only have a single GPU, but there is also a section about mutli-GPU and CPU training (with more coming soon).
+Training transformer models efficiently requires an accelerator such as a GPU or TPU. The most common case is where you only have a single GPU, but there is also a section about multi-GPU and CPU training (with more coming soon).
 
 <Tip>
 
@@ -40,7 +40,7 @@ Training large models on a single GPU can be challenging but there are a number
 
 ### Multi-GPU
 
-In some cases training on a single GPU is still too slow or won't fit the large model. Moving to a mutli-GPU setup is the logical step, but training on multiple GPUs at once comes with new decisions: does each GPU have a full copy of the model or is the model itself also distributed? In this section we look at data, tensor, and pipeline parallism.
+In some cases training on a single GPU is still too slow or won't fit the large model. Moving to a multi-GPU setup is the logical step, but training on multiple GPUs at once comes with new decisions: does each GPU have a full copy of the model or is the model itself also distributed? In this section we look at data, tensor, and pipeline parallism.
 
 [Go to multi-GPU training section](perf_train_gpu_many)