Revert "Add GPU acceleration documentation (#2384)"

This reverts commit c3f3fe7.

_ml-commons-plugin/cluster-settings.md

@@ -15,7 +15,7 @@ This page provides an overview of `opensearch.yml` settings that can be configur
 ### Setting
 
 ```
-plugins.ml_commons.only_run_on_ml_node: true
+plugins.ml_commons.only_run_on_ml_node: false
 ```
 
 ### Description

_ml-commons-plugin/model-serving-framework.md

@@ -1,13 +1,13 @@
 ---
 layout: default
 title: Model-serving framework 
-has_children: true
+has_children: false
 nav_order: 110
 ---
 
 # Model-serving framework
 
-The model-serving framework is an experimental feature. For updates on the progress of the model-serving framework, or if you want to leave feedback that could help improve the feature, join the discussion in the [Model-serving framework forum](https://forum.opensearch.org/t/feedback-machine-learning-model-serving-framework-experimental-release/11439).    
+The Model-serving framework is an experimental feature. For updates on the progress of the Neural Search plugin, or if you want to leave feedback that could help improve the feature, join the discussion in the [Model-serving framework forum](https://forum.opensearch.org/t/feedback-machine-learning-model-serving-framework-experimental-release/11439).    
 {: .warning}
 
 ML Commons allows you to serve custom models and use those models to make inferences. For those who want to run their PyTorch deep learning model inside an OpenSearch cluster, you can upload and run that model with the ML Commons REST API.
@@ -31,11 +31,7 @@ Furthermore, files must be saved as zip files before upload. Therefore, to ensur
 
 ### Model size
 
-Most deep learning models are more than 100 MB, making it difficult to fit them into a single document. OpenSearch splits the model file into smaller chunks to be stored in a model index. When allocating machine learning (ML) or data nodes for your OpenSearch cluster, make sure you correctly size your ML nodes so that you have enough memory when making ML inferences.
-
-## GPU acceleration
-
-To achieve better performance within the model-serving framework, you can take advantage of GPU acceleration on your ML node. For more information, see [GPU acceleration]({{site.url}}{{site.baseurl}}/ml-commons-plugin/gpu-acceleration/).
+Most deep learning models are over 100 MBs, making it difficult to fit the model into a single document. OpenSearch splits the model file into smaller chunks to be stored in a model index. When allocating machine learning (ML) or data nodes for your OpenSearch cluster, be aware of the size of your model to prevent any downtime when making inferences. 
 
 
 ## Upload model to OpenSearch

_neural-search-plugin/index.md

@@ -34,7 +34,7 @@ Use `pipeline_name` to create a name for your Neural Search pipeline.
 Field | Data type | Description
 :--- | :--- | :--- 
 description | string | A description of the processor.
-model_id | string | The ID of the model that will be used in the embedding interface. The model must be indexed in OpenSearch before it can be used in Neural Search. For more information, see [Model Serving Framework]({{site.url}}{{site.baseurl}}/ml-commons-plugin/model-serving-framework/)
+model_id | string | The ID of the model that will be used in the embedding interface. The model must be indexed in OpenSearch before it can be used in Neural Search. For more information, see [Model Serving Framework]
 input_field_name | string | The field name used to cache text for text embeddings.
 output_field_name  | string | The name of the field in which output text is stored.