diff --git a/tests/test_shared_expert.py b/tests/test_shared_expert.py
index f26a0eff..1b12db0d 100644
--- a/tests/test_shared_expert.py
+++ b/tests/test_shared_expert.py
@@ -14,9 +14,7 @@ from safetensors import safe_open
 MODEL_PATH = "/root/nvidia-meeting/DeepSeek-V4-Pro-NVFP4"
 DEVICE = "cuda:0"
 LAYER_IDX = 0
-HIDDEN_SIZE = 7168
-HC_MULT = 4
-HC_DIM = HC_MULT * HIDDEN_SIZE  # 28672
+HIDDEN_SIZE = 7168  # shared expert input dim (from checkpoint weight shapes)
 INTERMEDIATE_SIZE = 3072
 SWIGLU_LIMIT = 10.0
 NUM_TOKENS = 4
@@ -78,9 +76,9 @@ def main():
     down_sf = P(f"{prefix}.down_proj.weight_scale")
     down_gs = P(f"{prefix}.down_proj.weight_scale_2").item()
 
-    print(f"gate_proj: shape={gate_w.shape} gs={gate_gs:.8f}")
-    print(f"up_proj:   shape={up_w.shape} gs={up_gs:.8f}")
-    print(f"down_proj: shape={down_w.shape} gs={down_gs:.8f}")
+    print(f"gate_proj: shape={gate_w.shape} gs={gate_gs:.8f} sf_shape={gate_sf.shape}")
+    print(f"up_proj:   shape={up_w.shape} gs={up_gs:.8f} sf_shape={up_sf.shape}")
+    print(f"down_proj: shape={down_w.shape} gs={down_gs:.8f} sf_shape={down_sf.shape}")
 
     # Stack gate + up into gate_up_proj (same format as MoE L1)
     # gate/up weights are (intermediate, hidden) uint8 packed
@@ -104,7 +102,7 @@ def main():
 
     # Create runner
     runner = CuTeDSLSharedExpertRunner(
-        hidden_size=HC_DIM,
+        hidden_size=HIDDEN_SIZE,
         intermediate_size=INTERMEDIATE_SIZE,
         max_num_tokens=8192,
         device=DEVICE,
@@ -119,7 +117,7 @@ def main():
     runner.finalize_weights()
 
     # Warmup to compute activation global scales
-    dummy = torch.randn(NUM_TOKENS, HC_DIM, dtype=torch.bfloat16, device=DEVICE) * 2.0
+    dummy = torch.randn(NUM_TOKENS, HIDDEN_SIZE, dtype=torch.bfloat16, device=DEVICE) * 2.0
     runner._ensure_initialized()
     runner.compute_activation_global_scales(dummy)
     print(f"Warmup gs: L1={runner._l1_activation_global_scale:.6f} "
@@ -127,7 +125,7 @@ def main():
 
     # Run CuTeDSL
     print("\n--- CuTeDSL Forward ---")
-    hidden = torch.randn(NUM_TOKENS, HC_DIM, dtype=torch.bfloat16, device=DEVICE) * 2.0
+    hidden = torch.randn(NUM_TOKENS, HIDDEN_SIZE, dtype=torch.bfloat16, device=DEVICE) * 2.0
 
     with torch.no_grad():
         output = runner.run(hidden)