diff --git a/tensorrt_llm/_torch/modules/fused_moe/communication/communication_factory.py b/tensorrt_llm/_torch/modules/fused_moe/communication/communication_factory.py
index cbcf0502ae93..195622f77fb8 100644
--- a/tensorrt_llm/_torch/modules/fused_moe/communication/communication_factory.py
+++ b/tensorrt_llm/_torch/modules/fused_moe/communication/communication_factory.py
@@ -174,7 +174,16 @@ def create_strategy(
             logger.debug(f"NVLinkTwoSided not available: {e}")
 
         # Try DeepEP (if enabled and weight dtype is bfloat16)
-        if os.environ.get("TRTLLM_CAN_USE_DEEP_EP", "1") == "1" and act_dtype == torch.bfloat16:
+        # Skip DeepEP/DeepEPLowLatency if NVLink symmetric memory init is known to
+        # be broken (detected by NVLinkOneSided workspace init failure). DeepEP also
+        # relies on NVSHMEM/symmetric memory internally, so it would hang during
+        # forward pass if the NVLink memory infrastructure is unavailable.
+        if NVLinkOneSided._WORKSPACE_INIT_FAILED:
+            logger.info(
+                "Skipping DeepEP/DeepEPLowLatency: NVLink symmetric memory "
+                "initialization previously failed (detected via NVLinkOneSided)."
+            )
+        elif os.environ.get("TRTLLM_CAN_USE_DEEP_EP", "1") == "1" and act_dtype == torch.bfloat16:
             try:
                 strategy = DeepEP(
                     mapping,
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..4488f10af759 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 +57,11 @@ class NVLinkOneSided(Communication):
 
     # Single shared workspace/memory across the process
     _WORKSPACE: dict | None = None
+    # Track if workspace initialization (MNNVL + NVSHMEM) has failed, to avoid
+    # repeated attempts and to signal other NVSHMEM-dependent strategies (e.g.
+    # DeepEP) to skip initialization — they share the same NVLink/symmetric
+    # memory infrastructure and will also fail or hang.
+    _WORKSPACE_INIT_FAILED: bool = False
 
     # MetaInfo indices - initialized from C++ constants
     FLAG_VAL_OFFSET_INDEX = None
@@ -224,20 +229,30 @@ def __init__(
         # Initialize or reuse workspace
         MnnvlMemory.initialize()
 
+        if self._WORKSPACE_INIT_FAILED:
+            raise RuntimeError(
+                "NVLinkOneSided: workspace initialization (MNNVL/NVSHMEM) previously "
+                "failed on this node, skipping repeated initialization attempt."
+            )
+
         if self._WORKSPACE is None:
             tllm_logger.info(
                 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,
-            )
+            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 (RuntimeError, AssertionError) as e:
+                NVLinkOneSided._WORKSPACE_INIT_FAILED = True
+                raise RuntimeError(f"NVLinkOneSided workspace initialization failed: {e}") from e
             NVLinkOneSided._WORKSPACE = {
                 "workspace_size_per_rank": self.workspace_size_per_rank,
                 "max_num_tokens_per_rank": self.max_num_tokens_per_rank,
diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt
index 9c19eb7d302d..e4d1251da901 100644
--- a/tests/integration/test_lists/waives.txt
+++ b/tests/integration/test_lists/waives.txt
@@ -222,7 +222,6 @@ accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-dp4-cutl
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v2_kv_cache-dp4-cutlass-auto] SKIP (https://nvbugs/5838211)
 full:A10/unittest/kv_cache_manager_v2_tests/ SKIP (https://nvbugs/5841954)
 examples/test_mistral.py::test_mistral_with_bf16_lora_torch[mistral-7b-v0.1] SKIP (https://nvbugs/5846178)
-accuracy/test_llm_api_pytorch.py::TestDeepSeekV32::test_fp8_blockscale[disable_skip_indexer] SKIP (https://nvbugs/5859886)
 accuracy/test_llm_api_pytorch.py::TestGPTOSS::test_w4_4gpus[v1_kv_cache-tp4-cutlass-fp8] SKIP (https://nvbugs/5651865)
 test_e2e.py::test_trtllm_multimodal_benchmark_serving SKIP (https://nvbugs/5864769)
 accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_fp8_block_scales[mtp=vanilla-fp8kv=False-attention_dp=False-cuda_graph=False-overlap_scheduler=False-torch_compile=False] SKIP (https://nvbugs/5879577)