Update README.md (#175)

Small text changes

Author: ronendar

docs/gpu-sharing/mps/README.md

@@ -1,15 +1,15 @@
 # GPU Sharing with MPS
-The KAI Scheduler supports GPU sharing by efficiently allocating a single GPU device to multiple pods.
+KAI Scheduler supports GPU sharing by efficiently scheduling multiple pods to a single GPU device.
 
-The Multi-Process Service (MPS) is an alternative, binary-compatible implementation of the CUDA Application Programming Interface (API). The MPS runtime architecture is designed to transparently enable co-operative multi-process CUDA applications, typically MPI jobs, to utilize Hyper-Q capabilities on the latest NVIDIA (Kepler-based) Tesla and Quadro GPUs.
+Multi-Process Service (MPS) is an alternative, binary-compatible implementation of the CUDA Application Programming Interface (API). MPS runtime architecture is designed to transparently enable co-operative multi-process CUDA applications, typically MPI jobs, to utilize Hyper-Q capabilities on the latest NVIDIA GPUs.
 See the [NVIDIA MPS documentation](https://docs.nvidia.com/deploy/mps/index.html) for more details.
 
-There are multiple ways to enable MPS in a Kubernetes cluster. This README focuses on how to use MPS with the KAI Scheduler.
+There are multiple ways to enable MPS in a Kubernetes cluster. This README focuses on how to use MPS with KAI Scheduler.
 
 ### Prerequisites
 To use GPU sharing, ensure the following requirements are met:
 * KAI Scheduler is installed and running in your cluster, with gpu-sharing feature enabled.
-2. The MPS server is running on all GPU-enabled hosts (`nvidia-cuda-mps-control`), with the `CUDA_MPS_PIPE_DIRECTORY` environment variable set to `/tmp/nvidia-mps`.
+2. MPS server is running on all GPU-enabled hosts (`nvidia-cuda-mps-control`), with the `CUDA_MPS_PIPE_DIRECTORY` environment variable set to `/tmp/nvidia-mps`.
 
 ### MPS Enabled PODs
 To submit a pod that can share a GPU device and connect to the MPS server, run the following command:
@@ -24,8 +24,8 @@ If the MPS server on the host is configured with a custom `CUDA_MPS_PIPE_DIRECTO
 
 For additional MPS-related environment variables, refer to the [NVIDIA MPS documentation](https://docs.nvidia.com/deploy/mps/index.html#environment-variables).
 
-### Running MPS Server as Pods in the Cluster
-If you're running the MPS server as pods on GPU nodes, you must ensure that workload pods are scheduled to the same nodes.
+### Running MPS Server as a Pod in the Cluster
+If you're running the MPS server as a pod on a GPU node, you must ensure that the workload pods are scheduled to the same nodes.
 To achieve this, label the relevant nodes and apply node affinity or a node selector to the workload pods.
 
 For example:
@@ -34,4 +34,4 @@ For example:
 ```
 nodeSelector:
     nvidia.mps/enabled: true
-```
+```