diff --git a/tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_one_sided.py b/tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_one_sided.py
index e37d5db10819..95deb58956d0 100644
--- a/tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_one_sided.py
+++ b/tensorrt_llm/_torch/modules/fused_moe/communication/nvlink_one_sided.py
@@ -57,6 +57,7 @@ class NVLinkOneSided(Communication):
 
     # Single shared workspace/memory across the process
     _WORKSPACE: dict | None = None
+    _WORKSPACE_INIT_FAILED: bool = False
 
     # MetaInfo indices - initialized from C++ constants
     FLAG_VAL_OFFSET_INDEX = None
@@ -168,6 +169,14 @@ def __init__(
                 transfer (halves NVLink bandwidth usage, output precision is preserved).
                 Corresponds to model_config.use_low_precision_moe_combine.
         """
+        # Skip if workspace initialization previously failed to avoid repeated
+        # MnnvlMemory allocations that leak CUDA physical memory (held alive by
+        # exception traceback references), which can exhaust GPU memory.
+        if NVLinkOneSided._WORKSPACE_INIT_FAILED:
+            raise RuntimeError(
+                "NVLinkOneSided workspace initialization previously failed; skipping retry."
+            )
+
         super().__init__(mapping)
 
         if self.mapping.world_size != self.ep_size:
@@ -229,15 +238,26 @@ def __init__(
                 f"NVLinkOneSided: Allocating workspace with size {self.workspace_size_per_rank} bytes."
                 f"ep_rank: {self.ep_rank}, ep_size: {self.ep_size}, top_k: {self.top_k}, max_num_tokens_per_rank: {self.max_num_tokens_per_rank}"
             )
-            mnnvl_mem = MnnvlMemory(mapping, self.workspace_size_per_rank)
-            workspace = mnnvl_mem.as_torch_strided_tensor(torch.uint8)
-            metainfo = torch.ops.trtllm.moe_a2a_initialize(
-                workspace,
-                self.ep_rank,
-                self.ep_size,
-                self.max_num_tokens_per_rank,
-                self.eplb_stats_num_experts,
-            )
+            mnnvl_mem = None
+            workspace = None
+            try:
+                mnnvl_mem = MnnvlMemory(mapping, self.workspace_size_per_rank)
+                workspace = mnnvl_mem.as_torch_strided_tensor(torch.uint8)
+                metainfo = torch.ops.trtllm.moe_a2a_initialize(
+                    workspace,
+                    self.ep_rank,
+                    self.ep_size,
+                    self.max_num_tokens_per_rank,
+                    self.eplb_stats_num_experts,
+                )
+            except Exception:
+                # Release CUDA physical memory immediately to prevent leak.
+                # Without explicit cleanup, MnnvlMemory objects stay alive
+                # (held by exception traceback references) until GC runs.
+                workspace = None
+                mnnvl_mem = None
+                NVLinkOneSided._WORKSPACE_INIT_FAILED = True
+                raise
             NVLinkOneSided._WORKSPACE = {
                 "workspace_size_per_rank": self.workspace_size_per_rank,
                 "max_num_tokens_per_rank": self.max_num_tokens_per_rank,