.. _trn1-training-performance:

Trn1/Trn1n Training Performance
===============================

.. contents:: Table of contents
   :local:


*Last update: September 18th, 2025*



.. _NLP:

Encoder Models
--------------

.. csv-table::
   :file: training_data_encoder.csv
   :header-rows: 1


Decoder Models
--------------

.. csv-table::
   :file: training_data_decoder.csv
   :header-rows: 1

.. note::
         **TP (Tensor Parallel), PP (Pipeline Parallel) and DP (Data Parallel)** Topology configuration refers to the degrees of 3D Parallelism (How the model and data is sharded across neuron cores).

         TP and PP are specified in the run script and DP is calculated by dividing **world size**(Number of nodes/instances * Number of neuron cores per instance) by TP * PP degrees.

         For example : TP = 4, PP = 4 and Number of instances is 32 (trn1.32xlarge). The world size will be : 32 (num instances) * 32(neuron cores per instance) = 1024. Now, DP degree = 1024 (World size)/ 4 (TP) * 4 (PP) = 64

         For more information on batch sizes please refer to :ref:`neuron-batching`


Vision Transformer Models
------------------------

.. csv-table::
   :file: training_data_vision_transformers.csv
   :header-rows: 1

.. note::
         Read more about strong vs weak scaling here :ref:`neuron-training-faq`