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>
 

docs/source/en/perf_train_special.mdx

@@ -13,7 +13,7 @@ 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>
 

docs/source/en/perf_train_tpu.mdx

@@ -13,7 +13,7 @@ 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>
 

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)