diff --git a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py
index 392fff09115..6f5ed94d0f4 100644
--- a/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py
+++ b/tensorrt_llm/_torch/modules/fused_moe/fused_moe_deepgemm.py
@@ -1,3 +1,4 @@
+import math
 from typing import Dict, List, Optional, Union
 
 import torch
@@ -365,6 +366,10 @@ class DeepGemmFusedMoE(CutlassFusedMoE):
     3. moe_finalize_scale_op: finalize the scale of the output tensor.
     """
 
+    # To reuse pytorch memory segments allocated during graph capture.
+    allocated_buffer_in_graph_pool: dict[str, list[torch.Tensor]] = {}
+    allocated_buffer_in_runtime: dict[str, torch.Tensor] = {}
+
     def __init__(
         self,
         *,
@@ -410,28 +415,94 @@ def __init__(
         )
 
     def get_workspace(self, m_max: int, group_size: int):
+
+        def select_buffer_with_more_elements(
+            graph_buffer: Optional[torch.Tensor],
+            runtime_buffer: Optional[torch.Tensor]
+        ) -> tuple[Optional[torch.Tensor], bool]:
+            if graph_buffer is None:
+                return runtime_buffer, False
+            if runtime_buffer is None:
+                return graph_buffer, True
+            use_graph = runtime_buffer.numel() > graph_buffer.numel()
+            return (runtime_buffer if use_graph else graph_buffer, use_graph)
+
+        def get_empty(tensor_shape: list[int], dtype: torch.dtype,
+                      cache_name: str) -> torch.Tensor:
+            capture_graph = torch.cuda.is_current_stream_capturing()
+            if DeepGemmFusedMoE.allocated_buffer_in_graph_pool is not None:
+                numel_like = math.prod(tensor_shape)
+                runtime_buffer = None
+                if cache_name in DeepGemmFusedMoE.allocated_buffer_in_runtime:
+                    buffer = DeepGemmFusedMoE.allocated_buffer_in_runtime[
+                        cache_name]
+                    numel_buffer = buffer.numel()
+                    runtime_buffer = buffer if numel_buffer >= numel_like else runtime_buffer
+
+                graph_buffer = None
+                # Safely get the list of candidates. Defaults to an empty list if key is missing.
+                candidate_buffers = DeepGemmFusedMoE.allocated_buffer_in_graph_pool.get(
+                    cache_name, [])
+                for buffer in candidate_buffers:
+                    numel_buffer = buffer.numel()
+                    # buffer just needs to be large enough.
+                    if numel_buffer >= numel_like:
+                        graph_buffer = buffer
+                        break
+
+                if capture_graph and graph_buffer is not None:
+                    return graph_buffer[0:numel_like].view(tensor_shape)
+                else:
+                    buffer, use_graph = select_buffer_with_more_elements(
+                        graph_buffer, runtime_buffer)
+                    if buffer is not None:
+                        if not use_graph and capture_graph:
+                            # move the buffer into graph buffers since it's running in graph capturing mode.
+                            DeepGemmFusedMoE.allocated_buffer_in_graph_pool.setdefault(
+                                cache_name, []).append(buffer)
+                            del DeepGemmFusedMoE.allocated_buffer_in_runtime[
+                                cache_name]
+
+                        return buffer[0:numel_like].view(tensor_shape)
+
+            # Reach here, no buffer is found. Then, we will use a new buffer to replace the small one. Release the memory first.
+            if cache_name in DeepGemmFusedMoE.allocated_buffer_in_runtime:
+                del DeepGemmFusedMoE.allocated_buffer_in_runtime[cache_name]
+
+            # If we get here, no suitable buffer was found in the cache. Create a new one.
+            new_buffer = torch.zeros(tensor_shape, device='cuda', dtype=dtype)
+            if DeepGemmFusedMoE.allocated_buffer_in_graph_pool is not None:
+                if capture_graph:
+                    DeepGemmFusedMoE.allocated_buffer_in_graph_pool.setdefault(
+                        cache_name, []).append(new_buffer)
+                else:
+                    DeepGemmFusedMoE.allocated_buffer_in_runtime[
+                        cache_name] = new_buffer
+            return new_buffer
+
         hidden_size = self.hidden_size
         intermediate_size = self.intermediate_size_per_partition
         num_experts = self.expert_size_per_partition
 
         # create workspace
         fp8_dim = max(hidden_size, intermediate_size)
-        workspace_0 = torch.empty((num_experts * m_max * fp8_dim),
-                                  dtype=torch.float8_e4m3fn,
-                                  device='cuda')
-        workspace_1 = torch.empty(
-            (num_experts * m_max * max(intermediate_size * 2, hidden_size)),
+        workspace_0 = get_empty((num_experts * m_max * fp8_dim, ),
+                                dtype=torch.float8_e4m3fn,
+                                cache_name='workspace_0')
+        workspace_1 = get_empty(
+            (num_experts * m_max * max(intermediate_size * 2, hidden_size), ),
             dtype=torch.bfloat16,
-            device='cuda')
+            cache_name='workspace_1')
 
         # create workspace for scaling factors
         m_padded = fp8_utils.align(m_max, 4)
         scale_k = fp8_utils.ceil_div(fp8_dim, group_size)
         scale_k_padded = fp8_utils.align(scale_k, 4)
-        workspace_sf = torch.empty(
-            (num_experts * (scale_k_padded // 4) * m_padded),
+
+        workspace_sf = get_empty(
+            (num_experts * (scale_k_padded // 4) * m_padded, ),
             dtype=torch.int32,
-            device='cuda')
+            cache_name='workspace_sf')
 
         workspace = {
             "workspace_0": workspace_0,