diff --git a/tests/integration/test_lists/waives.txt b/tests/integration/test_lists/waives.txt
index 6c0b5aedd27..e1e580ab9fa 100644
--- a/tests/integration/test_lists/waives.txt
+++ b/tests/integration/test_lists/waives.txt
@@ -491,8 +491,6 @@ triton_server/test_triton.py::test_llava_onevision[llava_onevision] SKIP (https:
 triton_server/test_triton.py::test_gpt_ib_lad[gpt-ib-lad] SKIP (https://nvbugs/5775223)
 accuracy/test_llm_api_pytorch.py::TestQwen3_30B_A3B::test_nvfp4[dep4_latency_moe_trtllm-torch_compile=True] SKIP (https://nvbugs/5740377)
 unittest/_torch/modules/test_fused_moe.py::test_fused_moe_fp8_blockwise_cute_dsl_multi_gpu[MoEWeightLoadingMode.FUSED_GATE_UP_PROJ-DefaultMoeRoutingMethod-1] SKIP (https://nvbugs/5775256)
-unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py::test_ep_shard[3-2] SKIP (https://nvbugs/5777041)
-unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py::test_ep_shard[8-2] SKIP (https://nvbugs/5777041)
 disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[llama-3.1-8b-instruct-hf-fp8] SKIP (https://nvbugs/5769890)
 disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[DeepSeek-V3-Lite-bf16] SKIP (https://nvbugs/5769890)
 disaggregated/test_disaggregated.py::test_disaggregated_benchmark_on_diff_backends[llama-v3-8b-hf] SKIP (https://nvbugs/5769890,https://nvbugs/5748683)
diff --git a/tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py b/tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py
index 8c034799ad7..a1e642db318 100644
--- a/tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py
+++ b/tests/unittest/_torch/auto_deploy/unit/multigpu/transformations/library/test_ep_sharding.py
@@ -31,10 +31,6 @@ def _run_ep_shard_job(num_experts: int, rank: int, world_size: int) -> None:
     ).to(device=device, dtype=torch.bfloat16)
     x = model.get_input(device=device, dtype=torch.bfloat16)
 
-    if world_size > num_experts:
-        print(f"world_size {world_size} > num_experts {num_experts}, skipping test")
-        return
-
     def _get_expected_num_params(rank: int, world_size: int, num_p_og: int) -> int:
         if world_size <= 1:
             return num_p_og
@@ -141,9 +137,11 @@ def _run_pattern_detection_job(num_experts: int, rank: int, world_size: int) ->
     run_sharding_pattern_detection_test(detected_transformations, expected_transformations)
 
 
-@pytest.mark.parametrize("device_count", get_device_counts())
+@pytest.mark.parametrize("device_count", get_device_counts([2, 8]))
 @pytest.mark.parametrize("num_experts", [3, 8])
 def test_ep_shard(device_count: int, num_experts: int):
+    if device_count > num_experts:
+        pytest.skip(f"world_size {device_count} > num_experts {num_experts}")
     dist_common.spawn_multiprocess_job(
         job=partial(_run_ep_shard_job, num_experts),
         size=device_count,