diff --git a/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py b/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
index cdff4a0036d1..b04ff248a326 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron/gpt_full_te_layer_autocast_spec.py
@@ -15,8 +15,8 @@
 from importlib.metadata import version
 from typing import Any, Callable, Optional
 
-import packaging
 import torch
+from pkg_resources import packaging
 
 from nemo.collections.nlp.modules.common.megatron.utils import ApexGuardDefaults
 from nemo.collections.nlp.parts import utils_funcs
@@ -30,6 +30,7 @@
 try:
     from megatron.core import parallel_state, tensor_parallel
     from megatron.core.fusions.fused_layer_norm import FusedLayerNorm
+    from megatron.core.transformer.cuda_graphs import CudaGraphManager
     from megatron.core.transformer.spec_utils import ModuleSpec
     from megatron.core.transformer.transformer_block import TransformerBlockSubmodules, get_num_layers_to_build
     from megatron.core.transformer.transformer_layer import BaseTransformerLayer
@@ -235,6 +236,10 @@ def __init__(self, config, layer_number=1, hidden_dropout=None):
             transformer_layer_args["ub_atomic_gemm_rs"] = config.tp_comm_atomic_rs
         super().__init__(**transformer_layer_args)
 
+        if self.config.enable_cuda_graph and self.training:
+            assert not config.cpu_offloading and config.recompute_granularity is None, "Cudagraphs not supported"
+            self.add_module('cudagraph_manager', CudaGraphManager())
+
     # Called by MCore's TransformerBlock.forward
     # megatron/core/transformer/transformer_block.py
     def forward(
@@ -261,8 +266,8 @@ def forward(
         self.is_first_microbatch = False
         context = None
 
-        # CUDA graph requires returned values to be Tensors
-        if self.config.enable_cuda_graph and self.training:
+        # External CUDA graph requires returned values to be Tensors
+        if hasattr(self.config, 'external_cuda_graph') and self.config.external_cuda_graph and self.training:
             return hidden_states
         return hidden_states, context
 
@@ -318,6 +323,11 @@ def sharded_state_dict(self, prefix: str = '', sharded_offsets: tuple = (), meta
 
         return sharded_state_dict
 
+    def __call__(self, *args, **kwargs):
+        if hasattr(self, 'cudagraph_manager'):
+            return self.cudagraph_manager(self, args, kwargs)
+        return super().__call__(*args, **kwargs)
+
 
 # Use this spec to use the full Transformer layer from Transformer Engine
 def get_gpt_full_te_layer_autocast_spec(transformer_config) -> ModuleSpec:
diff --git a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
index 1ae05b58890a..e3d426ec9275 100644
--- a/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
+++ b/nemo/collections/nlp/models/language_modeling/megatron_gpt_model.py
@@ -832,6 +832,7 @@ def training_step(self, dataloader_iter):
                     module = module.module
                 if not self.mcore_gpt:
                     module = module.language_model
+
                 if hasattr(module, 'embedding'):
                     for param in module.embedding.parameters():
                         param.data_ptr()
@@ -2115,6 +2116,13 @@ def build_transformer_config(self) -> TransformerConfig:
         else:
             raise ValueError(f"fp8 enabled but fp8_format (fp8_e4m3 | fp8_hybrid) is not set.")
 
+        if self.cfg.get('enable_cuda_graph', False):
+            assert HAVE_TE, "Transformer Engine is required for cudagraphs."
+            assert self.cfg.get(
+                'use_te_rng_tracker', False
+            ), "Transformer engine's RNG tracker is required for cudagraphs, this can be enabled with \
+                'use_te_rng_tracker=True'."
+
         # any configs that are not in the nemo model config will be added here
         model_specific_configs = {
             'layernorm_zero_centered_gamma': layernorm_zero_centered_gamma,
@@ -2132,6 +2140,7 @@ def build_transformer_config(self) -> TransformerConfig:
             'moe_z_loss_coeff': self.cfg.get('moe_z_loss_coeff', None),  # 1e-3 would be a good start value for z-loss
             'moe_input_jitter_eps': self.cfg.get('moe_input_jitter_eps', None),
             'moe_token_dropping': self.cfg.get('moe_token_dropping', False),  # TODO: Support token dropping.
+            'enable_cuda_graph': self.cfg.get('enable_cuda_graph', False),
         }
         if model_specific_configs['num_moe_experts'] is not None:
             assert mcore_supports_moe(), 'Megatron-core >= v0.5.0 is required for MoE'