diff --git a/tests/debug_wo_a2.py b/tests/debug_wo_a2.py
new file mode 100644
index 00000000..7144d0e1
--- /dev/null
+++ b/tests/debug_wo_a2.py
@@ -0,0 +1,104 @@
+"""Debug: compare NVFP4 grouped GEMM output element-by-element."""
+import torch
+import torch.nn.functional as F
+import sys, os
+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+
+from cutedsl.bridge import quantize_weight_to_nvfp4, quantize_to_nvfp4
+
+torch.cuda.set_device(0)
+torch.manual_seed(42)
+
+G, HPG, HD, OR = 2, 4, 128, 64
+GI = HPG * HD  # 512
+T = 4
+DEVICE = "cuda:0"
+
+o = torch.randn(T, G*HPG, HD, dtype=torch.bfloat16, device=DEVICE) * 2.0
+w = torch.randn(G*OR, GI, dtype=torch.bfloat16, device=DEVICE) * 0.1
+
+# Reference: per-group BF16 matmul
+o_g = o.reshape(T, G, GI)
+z_ref = torch.empty(T, G, OR, dtype=torch.bfloat16, device=DEVICE)
+for g in range(G):
+    z_ref[:, g, :] = o_g[:, g, :] @ w[g*OR:(g+1)*OR, :].T
+
+# Test: quantize/dequantize each weight group and compare
+print("=== Weight quantization test ===")
+for g in range(G):
+    w_g = w[g*OR:(g*OR+OR), :]  # (OR, GI)
+    w_gt = w_g.T  # (GI, OR) for quantize_weight_to_nvfp4
+    w_fp4, w_sf, w_gs = quantize_weight_to_nvfp4(w_gt)
+    
+    # Dequantize to BF16 for reference
+    E2M1_LUT = torch.tensor([0., 0.5, 1., 1.5, 2., 3., 4., 6., -0., -0.5, -1., -1.5, -2., -3., -4., -6.],
+                             dtype=torch.float32, device=DEVICE)
+    packed = w_fp4.view(torch.uint8)
+    lower = E2M1_LUT[(packed & 0x0F).long()]
+    upper = E2M1_LUT[((packed >> 4) & 0x0F).long()]
+    K, N = w_gt.shape
+    unpacked = torch.empty(K, N, dtype=torch.float32, device=DEVICE)
+    unpacked[:, 0::2] = lower
+    unpacked[:, 1::2] = upper
+    K_sf = w_sf.shape[0]
+    sf_expanded = w_sf.float().repeat_interleave(16, dim=0)[:K, :]
+    w_dequant = (unpacked * sf_expanded * w_gs).to(torch.bfloat16)
+    
+    # Compare
+    cos = F.cosine_similarity(w_gt.flatten().unsqueeze(0).float(), w_dequant.flatten().unsqueeze(0).float()).item()
+    print(f"  Group {g}: weight quant cos={cos:.6f} w_gt amax={w_gt.amax():.4f} w_dequant amax={w_dequant.amax():.4f}")
+
+# Test: activation quantization
+print("\n=== Activation quantization test ===")
+o_flat = o_g.reshape(T * G, GI)
+x_fp4, x_sf, gs = quantize_to_nvfp4(o_flat)
+# Dequant
+packed = x_fp4.view(torch.uint8)
+E2M1_LUT = torch.tensor([0., 0.5, 1., 1.5, 2., 3., 4., 6., -0., -0.5, -1., -1.5, -2., -3., -4., -6.],
+                         dtype=torch.float32, device=DEVICE)
+lower = E2M1_LUT[(packed & 0x0F).long()]
+upper = E2M1_LUT[((packed >> 4) & 0x0F).long()]
+unpacked = torch.empty(T*G, GI, dtype=torch.float32, device=DEVICE)
+unpacked[:, 0::2] = lower
+unpacked[:, 1::2] = upper
+K_sf = x_sf.shape[1]
+sf_expanded = x_sf.float().repeat_interleave(16, dim=1)[:T*G, :GI]
+x_dequant = (unpacked * sf_expanded * gs).to(torch.bfloat16)
+cos = F.cosine_similarity(o_flat.flatten().unsqueeze(0).float(), x_dequant.flatten().unsqueeze(0).float()).item()
+print(f"  Activation quant cos={cos:.6f} gs={gs:.6f}")
+
+# Test: the FULL pipeline — quantize weight and activation, then BF16 matmul
+print("\n=== Full pipeline (quantize → dequantize → BF16 matmul) ===")
+z_qdq = torch.empty(T, G, OR, dtype=torch.bfloat16, device=DEVICE)
+for g in range(G):
+    w_g = w[g*OR:(g*OR+OR), :].T  # (GI, OR)
+    w_fp4, w_sf, w_gs = quantize_weight_to_nvfp4(w_g)
+    # Dequant
+    packed = w_fp4.view(torch.uint8)
+    lower = E2M1_LUT[(packed & 0x0F).long()]
+    upper = E2M1_LUT[((packed >> 4) & 0x0F).long()]
+    K, N = w_g.shape
+    unpacked = torch.empty(K, N, dtype=torch.float32, device=DEVICE)
+    unpacked[:, 0::2] = lower
+    unpacked[:, 1::2] = upper
+    K_sf = w_sf.shape[0]
+    sf_expanded = w_sf.float().repeat_interleave(16, dim=0)[:K, :]
+    w_dequant = (unpacked * sf_expanded * w_gs).to(torch.bfloat16)
+    
+    # Quantize activation for this group
+    act = o_g[:, g, :]  # (T, GI)
+    a_fp4, a_sf, a_gs = quantize_to_nvfp4(act)
+    packed = a_fp4.view(torch.uint8)
+    lower = E2M1_LUT[(packed & 0x0F).long()]
+    upper = E2M1_LUT[((packed >> 4) & 0x0F).long()]
+    unpacked = torch.empty(T, GI, dtype=torch.float32, device=DEVICE)
+    unpacked[:, 0::2] = lower
+    unpacked[:, 1::2] = upper
+    K_sf = a_sf.shape[1]
+    sf_expanded = a_sf.float().repeat_interleave(16, dim=1)[:T, :GI]
+    a_dequant = (unpacked * sf_expanded * a_gs).to(torch.bfloat16)
+    
+    z_qdq[:, g, :] = a_dequant @ w_dequant
+
+cos = F.cosine_similarity(z_ref.flatten().unsqueeze(0).float(), z_qdq.flatten().unsqueeze(0).float()).item()
+print(f"  QDQ vs BF16: cosine={cos:.6f}")