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+.. {#openvino_docs_ops_internal_RMS}
+
+RMS
+===
+
+
+.. meta::
+  :description: Learn about RMS a normalization operation.
+
+**Versioned name**: *RMS*
+
+**Category**: *Normalization*
+
+**Short description**: Calculates Root Mean Square (RMS) normalization of the input tensor.
+
+**Detailed description**
+
+*RMS* operation performs Root Mean Square (RMS) normalization on a given input ``data`` along the last dimension of the input.
+`Reference <https://arxiv.org/abs/1910.07467>`__.
+
+
+.. code-block:: py
+
+    (x / Sqrt(ReduceMean(x^2, -1) + eps)) * scale
+
+
+**Attributes**
+
+* *epsilon*
+
+  * **Description**: A very small value added to the variance for numerical stability. Ensures that division by zero does not occur for any normalized element.
+  * **Range of values**: a positive floating-point number
+  * **Type**: ``float``
+  * **Required**: *yes*
+
+* *output_type*
+
+  * **Description**: The precision for output type conversion, after scaling. It's used for output type compression to f16.
+  * **Range of values**: Supported floating point type: "f16", "undefined"
+  * **Type**: ``string``
+  * **Default value**: "undefined" (means that output type is set to the same as the input type)
+  * **Required**: *no*
+
+
+**Inputs**
+
+* **1**: ``data`` - Input data to be normalized. A tensor of type *T* and arbitrary shape. **Required.**
+
+* **2**: ``scale`` - A tensor of type *T* containing the scale values for . The shape should be broadcastable to the shape of ``data`` tensor. **Required.**
+
+
+**Outputs**
+
+* **1**: Output tensor of the same shape as the ``data`` input tensor and type specified by *output_type* attribute.
+
+**Types**
+
+* *T*: any floating point type.
+
+**Example**
+
+.. code-block:: xml
+   :force:
+
+   <layer ... type="RMS"> <!-- normalization always over the last dimension [-1] -->
+       <data eps="1e-6"/>
+       <input>
+           <port id="0">
+               <dim>12</dim>
+               <dim>25</dim>
+               <dim>512</dim>
+           </port>
+           <port id="1">
+               <dim>512</dim>
+           </port>
+       </input>
+       <output>
+           <port id="2">
+               <dim>12</dim>
+               <dim>25</dim>
+               <dim>512</dim>
+           </port>
+       </output>
+   </layer>