diff --git a/megatron/core/inference/contexts/dynamic_context.py b/megatron/core/inference/contexts/dynamic_context.py
index 48e9e9b48f..b0175de6f8 100644
--- a/megatron/core/inference/contexts/dynamic_context.py
+++ b/megatron/core/inference/contexts/dynamic_context.py
@@ -1079,6 +1079,7 @@ def initialize_attention_state(
                 self.padded_active_token_count = min(
                     self.padded_active_token_count, self.max_active_requests
                 )
+        self.padding_slice = slice(active_token_count, self.padded_active_token_count)
 
         # How are we calculating the padded active request count?
         # Case 1: Using cuda graphs:
diff --git a/megatron/core/models/gpt/gpt_model.py b/megatron/core/models/gpt/gpt_model.py
index 25546d3662..4453a8c213 100644
--- a/megatron/core/models/gpt/gpt_model.py
+++ b/megatron/core/models/gpt/gpt_model.py
@@ -33,7 +33,11 @@
 from megatron.core.transformer.spec_utils import ModuleSpec
 from megatron.core.transformer.transformer_block import TransformerBlock
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.utils import WrappedTensor, deprecate_inference_params
+from megatron.core.utils import (
+    WrappedTensor,
+    deprecate_inference_params,
+    is_using_quantization_scales,
+)
 
 
 class GPTModel(LanguageModule):
@@ -386,11 +390,19 @@ def _preprocess(
         else:
             sequence_len_offset = None
 
-        # Wrap decoder_input to allow the decoder (TransformerBlock) to delete the
-        # reference held by this caller function, enabling early garbage collection for
-        # inference. Skip wrapping if decoder_input is logged after decoder completion.
-        if in_inference_mode and not has_config_logger_enabled(self.config):
-            decoder_input = WrappedTensor(decoder_input)
+        if in_inference_mode:
+            # Clear the outputs for padding tokens when using dynamic batching with
+            # quantization scales to avoid corrupting amax calculations
+            if inference_context.is_dynamic_batching() and is_using_quantization_scales(
+                self.config
+            ):
+                decoder_input[inference_context.padding_slice] = 0.0
+
+            # Wrap decoder_input to allow the decoder (TransformerBlock) to delete the
+            # reference held by this caller function, enabling early garbage collection for
+            # inference. Skip wrapping if decoder_input is logged after decoder completion.
+            if not has_config_logger_enabled(self.config):
+                decoder_input = WrappedTensor(decoder_input)
 
         preproc_output = (
             decoder_input,
diff --git a/megatron/core/models/mamba/mamba_model.py b/megatron/core/models/mamba/mamba_model.py
index 378cf7e47d..51b9bd1677 100644
--- a/megatron/core/models/mamba/mamba_model.py
+++ b/megatron/core/models/mamba/mamba_model.py
@@ -16,7 +16,11 @@
 from megatron.core.transformer import TransformerConfig
 from megatron.core.transformer.enums import ModelType
 from megatron.core.transformer.spec_utils import ModuleSpec, build_module
-from megatron.core.utils import WrappedTensor, deprecate_inference_params
+from megatron.core.utils import (
+    WrappedTensor,
+    deprecate_inference_params,
+    is_using_quantization_scales,
+)
 
 
 class MambaModel(LanguageModule):
@@ -201,6 +205,15 @@ def forward(
             pass
         elif self.pre_process:
             decoder_input = self.embedding(input_ids=input_ids, position_ids=position_ids)
+
+            # Clear the outputs for padding tokens when using dynamic batching with
+            # quantization scales to avoid corrupting amax calculations
+            if (
+                in_inference_mode
+                and inference_context.is_dynamic_batching()
+                and is_using_quantization_scales(self.config)
+            ):
+                decoder_input[inference_context.padding_slice] = 0.0
         else:
             # intermediate stage of pipeline
             # decoder will get hidden_states from encoder.input_tensor
diff --git a/megatron/core/transformer/attention.py b/megatron/core/transformer/attention.py
index 1051799db9..d839af3790 100644
--- a/megatron/core/transformer/attention.py
+++ b/megatron/core/transformer/attention.py
@@ -33,6 +33,7 @@
     get_pg_size,
     is_fa_min_version,
     is_te_min_version,
+    is_using_quantization_scales,
     nvtx_range_pop,
     nvtx_range_push,
 )
@@ -949,6 +950,11 @@ def forward(
                 )
                 core_attn_out = rearrange(core_attn_out, 's b h d -> s b (h d)')
 
+                # Clear the outputs for padding tokens when using quantization scales
+                # to avoid corrupting amax calculations
+                if is_using_quantization_scales(self.config):
+                    core_attn_out[inference_context.padding_slice] = 0.0
+
         if packed_seq_params is not None and packed_seq_params.qkv_format == 'thd':
             # reshape to same output shape as unpacked case
             # (t, np, hn) -> (t, b=1, h=np*hn)
@@ -960,7 +966,6 @@ def forward(
         # =================
         # Output. [sq, b, h]
         # =================
-
         nvtx_range_push(suffix="linear_proj")
         output, bias = self.linear_proj(core_attn_out)
         nvtx_range_pop(suffix="linear_proj")
diff --git a/megatron/core/utils.py b/megatron/core/utils.py
index 19015af62d..003c44ab29 100644
--- a/megatron/core/utils.py
+++ b/megatron/core/utils.py
@@ -2099,6 +2099,11 @@ def get_asyncio_loop(loop: asyncio.AbstractEventLoop | None = None) -> asyncio.A
     return loop
 
 
+def is_using_quantization_scales(config):
+    """Returns whether the model is using quantization scales based on the config."""
+    return getattr(config, "fp8", False) or getattr(config, "fp4", False)
+
+
 _ASYNC_TASK_STATS = defaultdict(lambda: [0, 0.0])  # cnt, total_time
 
 
diff --git a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_fp8_logitsmatch/golden_values_dev_dgx_h100.json b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_fp8_logitsmatch/golden_values_dev_dgx_h100.json
index cbc5f4fa3a..ed4f7ce423 100644
--- a/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_fp8_logitsmatch/golden_values_dev_dgx_h100.json
+++ b/tests/functional_tests/test_cases/gpt/gpt_dynamic_inference_tp1_pp1_583m_cuda_graphs_fp8_logitsmatch/golden_values_dev_dgx_h100.json
@@ -1,38 +1,38 @@
 {
  "0": {
   "input_prompt": "Time travel to 2008, and go to a bar or a club or one of the myriad disco-basements on the Lower East Side that does not quite know which of those it is. Dance awkwardly in a room full of other glittered-up nerds, and wait for something to happen, buoyed on the feeling that this is the big swollen heart of life, that this is New York like the movies.",
-  "generated_text": " And then you get to the end of the movie, and you realize that this is not New York at all. This is New York at the end",
+  "generated_text": " And that this is the place where you can be yourself, and be yourself in the most beautiful way possible. And that this is the place where you",
   "generated_tokens": [
    3060,
-   2430,
-   1636,
-   2012,
-   1317,
-   1278,
-   2362,
-   1307,
+   1455,
+   1593,
+   1395,
    1278,
-   16070,
+   3535,
+   2478,
+   1636,
+   1710,
+   1402,
+   14019,
    1044,
    1321,
-   1636,
-   23067,
+   1402,
+   14019,
+   1294,
+   1278,
+   2725,
+   15568,
+   3039,
+   4171,
+   1046,
+   3060,
    1455,
    1593,
    1395,
-   1605,
-   3140,
-   5152,
-   1513,
-   1747,
-   1046,
-   2409,
-   1395,
-   3140,
-   5152,
-   1513,
    1278,
-   2362
+   3535,
+   2478,
+   1636
   ],
   "latency": 0.2963709831237793,
   "cuda_graph_request_count_map": {
@@ -143,35 +143,35 @@
    -4.629369258880615,
    -3.4186267852783203,
    -1.9727531671524048,
-   -2.354729652404785,
-   -1.474542498588562,
-   -2.48478364944458,
-   -1.7641210556030273,
-   -1.1853944063186646,
-   -2.8624324798583984,
-   -0.5740103125572205,
-   -0.4542185962200165,
-   -1.4300930500030518,
-   -0.8807456493377686,
-   -0.4597663879394531,
-   -0.9252307415008545,
-   -1.648141860961914,
-   -0.44453874230384827,
-   -1.818476915359497,
-   -0.5714479088783264,
-   -1.2115143537521362,
-   -1.0910619497299194,
-   -0.0023161747958511114,
-   -1.3206473588943481,
-   -0.008621376007795334,
-   -0.7551823854446411,
-   -0.9404395818710327,
-   -0.07279698550701141,
-   -0.9365248680114746,
-   -0.03344438225030899,
-   -1.9720849990844727,
-   -1.3928067684173584,
-   -0.7453650832176208
+   -2.3787546157836914,
+   -2.1559927463531494,
+   -0.3042203187942505,
+   -1.5030715465545654,
+   -2.2185328006744385,
+   -1.163987398147583,
+   -1.6270570755004883,
+   -1.382636547088623,
+   -2.0105135440826416,
+   -1.4725666046142578,
+   -0.8826082944869995,
+   -1.2427284717559814,
+   -1.966317892074585,
+   -2.320446252822876,
+   -1.6230499744415283,
+   -0.9565537571907043,
+   -1.8637971878051758,
+   -2.4722466468811035,
+   -1.0165706872940063,
+   -1.5226590633392334,
+   -0.9222670793533325,
+   -0.3918261229991913,
+   -0.4523355960845947,
+   -0.2231457382440567,
+   -0.014109030365943909,
+   -0.36200955510139465,
+   -0.2032179832458496,
+   -0.043058376759290695,
+   -0.09619185328483582
   ]
  },
  "throughput": [
@@ -184,4 +184,4 @@
   100.30260782227111,
   100.30996418216475
  ]
-}
\ No newline at end of file
+}
diff --git a/tests/unit_tests/models/test_gpt_model.py b/tests/unit_tests/models/test_gpt_model.py
index 6936cfbe60..469a04f6f3 100644
--- a/tests/unit_tests/models/test_gpt_model.py
+++ b/tests/unit_tests/models/test_gpt_model.py
@@ -8,9 +8,13 @@
 import torch
 from packaging import version
 from pytest import approx
+from transformer_engine.pytorch.fp8 import check_fp8_support
 
 from megatron.core import parallel_state
 from megatron.core.hyper_comm_grid import HyperCommGrid
+from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
+from megatron.core.inference.inference_request import DynamicInferenceRequest
+from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.models.gpt.gpt_layer_specs import (
     get_gpt_layer_with_transformer_engine_spec,
     get_mlp_module_spec,
@@ -18,8 +22,9 @@
 from megatron.core.models.gpt.gpt_model import GPTModel
 from megatron.core.process_groups_config import ProcessGroupCollection
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
+from megatron.core.transformer.module import Float16Module
 from megatron.core.transformer.transformer_config import TransformerConfig
-from megatron.core.utils import is_te_min_version
+from megatron.core.utils import is_fa_min_version, is_te_min_version
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -333,3 +338,108 @@ def test_gpt_model_with_custom_pg(self, tp_size, dp_size, cp_size):
         assert logits.shape[0] == sequence_length
         assert logits.shape[1] == micro_batch_size
         assert logits.shape[2] == self.gpt_model.config.hidden_size
+
+
+class TestGPTWithDynamicInference:
+    """Tests GPTModel with dynamic inference."""
+
+    @torch.inference_mode()
+    def setup_method(self, method):
+        fp8_available, reason_for_no_fp8 = check_fp8_support()
+        if not fp8_available:
+            pytest.skip(reason_for_no_fp8)
+
+        os.environ.pop('NVTE_FUSED_ATTN', None)
+        os.environ.pop('NVTE_FLASH_ATTN', None)
+        os.environ.pop('NVTE_UNFUSED_ATTN', None)
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+
+        transformer_config = TransformerConfig(
+            num_layers=4,
+            hidden_size=64,
+            num_attention_heads=8,
+            use_cpu_initialization=True,
+            params_dtype=torch.bfloat16,
+            bf16=True,
+            fp8="hybrid",
+            fp8_recipe="tensorwise",
+        )
+
+        self.gpt_model = GPTModel(
+            config=transformer_config,
+            transformer_layer_spec=get_gpt_layer_with_transformer_engine_spec(),
+            vocab_size=128,
+            max_sequence_length=DynamicInferenceContext.TOKEN_ROUNDER,
+            parallel_output=True,
+        )
+        self.gpt_model = Float16Module(self.gpt_model.config, self.gpt_model)
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @torch.inference_mode()
+    def test_dynamic_inference_padding_with_fp8(self):
+        """
+        Tests that logits for padded tokens are zeroed out for fp8 inference.
+        """
+        self.gpt_model.cuda()
+        self.gpt_model.eval()
+        config = self.gpt_model.config
+
+        inference_context = DynamicInferenceContext(
+            params_dtype=config.params_dtype,
+            num_layers=config.num_layers,
+            kv_channels=config.hidden_size // config.num_attention_heads,
+            num_attention_heads=config.num_attention_heads,
+            max_sequence_length=self.gpt_model.module.max_sequence_length,
+            buffer_size_gb=1.0,
+            block_size_tokens=256,
+            materialize_only_last_token_logits=False,
+        )
+
+        # Add a request with 10 tokens. Since 10 is not a multiple of 64,
+        # this will create padding up to the padded length of 64.
+        active_token_count = 10
+        request = DynamicInferenceRequest(
+            request_id=0,
+            prompt_tokens=torch.arange(0, active_token_count, dtype=torch.long, device='cuda'),
+            sampling_params=SamplingParams(num_tokens_to_generate=1),
+        )
+        inference_context.add_request(request)
+
+        # Prepares the context, including calculating the padded token count.
+        inference_context.initialize_attention_state()
+
+        assert inference_context.active_token_count == active_token_count
+        assert inference_context.padded_active_token_count == DynamicInferenceContext.TOKEN_ROUNDER
+
+        # Prepare inputs for the forward pass.
+        padded_token_count = inference_context.padded_active_token_count
+        input_ids, position_ids = inference_context.current_input_and_position_ids()
+
+        # Run the forward pass with inference parameters.
+        logits = self.gpt_model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=None,
+            inference_context=inference_context,
+            runtime_gather_output=True,
+        )
+
+        # Verify the output shape.
+        assert logits.shape[0] == 1
+        assert logits.shape[1] == padded_token_count
+        assert logits.shape[2] == self.gpt_model.module.vocab_size
+
+        # Extract the logits corresponding to the padding tokens (from index 10 to 63).
+        padding_start_idx = inference_context.active_token_count
+        padding_end_idx = inference_context.padded_active_token_count
+        padding_logits = logits[0, padding_start_idx:padding_end_idx, :]
+
+        # Assert that all padding logits are zero.
+        assert torch.all(padding_logits == 0.0), "Logits for padding tokens are not all zero."
diff --git a/tests/unit_tests/models/test_mamba_model.py b/tests/unit_tests/models/test_mamba_model.py
index ca42ae496b..1d05f6e1ac 100644
--- a/tests/unit_tests/models/test_mamba_model.py
+++ b/tests/unit_tests/models/test_mamba_model.py
@@ -1,18 +1,29 @@
 # Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
 
+import os
 from datetime import timedelta
 
 import pytest
 import torch
+from transformer_engine.pytorch.fp8 import check_fp8_support
 
 from megatron.core import parallel_state
 from megatron.core.hyper_comm_grid import HyperCommGrid
 from megatron.core.inference.contexts import BaseInferenceContext, StaticInferenceContext
+from megatron.core.inference.contexts.dynamic_context import DynamicInferenceContext
+from megatron.core.inference.inference_request import DynamicInferenceRequest
+from megatron.core.inference.sampling_params import SamplingParams
 from megatron.core.models.mamba.mamba_layer_specs import mamba_stack_spec
 from megatron.core.models.mamba.mamba_model import MambaModel
 from megatron.core.tensor_parallel.random import model_parallel_cuda_manual_seed
 from megatron.core.transformer import TransformerConfig
-from megatron.core.utils import divide, is_torch_min_version
+from megatron.core.transformer.module import Float16Module
+from megatron.core.utils import (
+    divide,
+    get_mamba_inference_state_config_from_model,
+    is_fa_min_version,
+    is_torch_min_version,
+)
 from tests.unit_tests.test_utilities import Utils
 
 
@@ -218,3 +229,115 @@ def test_with_custom_process_groups(self, tmp_path, tp_size, cp_size, pp_size):
         assert logits.shape[0] == micro_batch_size
         assert logits.shape[1] == sequence_length
         assert logits.shape[2] == divide(model.vocab_size, tp_size)
+
+
+class TestMambaWithDynamicInference:
+    """Tests MambaModel with dynamic inference."""
+
+    @torch.inference_mode()
+    def setup_method(self, method):
+        fp8_available, reason_for_no_fp8 = check_fp8_support()
+        if not fp8_available:
+            pytest.skip(reason_for_no_fp8)
+
+        Utils.initialize_model_parallel(1, 1)
+        model_parallel_cuda_manual_seed(123)
+
+        transformer_config = TransformerConfig(
+            num_layers=3,  # 1 Mamba layer, 1 attention layer, 1 MLP layer
+            hidden_size=256,
+            mamba_num_heads=16,
+            num_attention_heads=16,
+            use_cpu_initialization=True,
+            params_dtype=torch.bfloat16,
+            bf16=True,
+            fp8="hybrid",
+            fp8_recipe="tensorwise",
+        )
+
+        self.mamba_model = MambaModel(
+            config=transformer_config,
+            mamba_stack_spec=mamba_stack_spec,
+            vocab_size=128,
+            max_sequence_length=DynamicInferenceContext.TOKEN_ROUNDER,
+            hybrid_attention_ratio=0.3,
+            hybrid_mlp_ratio=0.3,
+            parallel_output=True,
+        )
+        self.mamba_model = Float16Module(self.mamba_model.config, self.mamba_model)
+
+    def teardown_method(self, method):
+        Utils.destroy_model_parallel()
+
+    @pytest.mark.internal
+    @pytest.mark.skipif(
+        not is_fa_min_version("2.7.3"), reason="need latest flash attn for dynamic batching"
+    )
+    @torch.inference_mode()
+    def test_dynamic_inference_padding_with_fp8(self):
+        """
+        Tests that logits for padded tokens are zeroed out for fp8 inference.
+        """
+        self.mamba_model.cuda()
+        self.mamba_model.eval()
+        config = self.mamba_model.config
+
+        # Mamba specific: Retrieve inference state config
+        mamba_inference_state_config = get_mamba_inference_state_config_from_model(
+            self.mamba_model.module
+        )
+
+        inference_context = DynamicInferenceContext(
+            params_dtype=config.params_dtype,
+            num_layers=config.num_layers,
+            kv_channels=config.hidden_size // config.num_attention_heads,
+            num_attention_heads=config.num_attention_heads,
+            max_sequence_length=self.mamba_model.module.max_sequence_length,
+            buffer_size_gb=1.0,
+            block_size_tokens=256,
+            materialize_only_last_token_logits=False,
+            mamba_inference_state_config=mamba_inference_state_config,
+            use_cuda_graphs_for_non_decode_steps=False,
+        )
+
+        # Add a request with 10 tokens. Since 10 is not a multiple of the rounder,
+        # this will create padding up to the padded length.
+        active_token_count = 10
+        request = DynamicInferenceRequest(
+            request_id=0,
+            prompt_tokens=torch.arange(0, active_token_count, dtype=torch.long, device='cuda'),
+            sampling_params=SamplingParams(num_tokens_to_generate=1),
+        )
+        inference_context.add_request(request)
+
+        # Prepares the context, including calculating the padded token count.
+        inference_context.initialize_attention_state()
+
+        assert inference_context.active_token_count == active_token_count
+        assert inference_context.padded_active_token_count == DynamicInferenceContext.TOKEN_ROUNDER
+
+        # Prepare inputs for the forward pass.
+        padded_token_count = inference_context.padded_active_token_count
+        input_ids, position_ids = inference_context.current_input_and_position_ids()
+
+        # Run the forward pass with inference parameters.
+        logits = self.mamba_model.forward(
+            input_ids=input_ids,
+            position_ids=position_ids,
+            attention_mask=None,
+            inference_context=inference_context,
+            runtime_gather_output=True,
+        )
+
+        # Verify the output shape.
+        assert logits.shape[0] == 1
+        assert logits.shape[1] == padded_token_count
+        assert logits.shape[2] == self.mamba_model.module.vocab_size
+
+        # Extract the logits corresponding to the padding tokens.
+        padding_start_idx = inference_context.active_token_count
+        padding_end_idx = inference_context.padded_active_token_count
+        padding_logits = logits[0, padding_start_idx:padding_end_idx, :]
+
+        # Assert that all padding logits are zero.
+        assert torch.all(padding_logits == 0.0), "Logits for padding tokens are not all zero."