diff --git a/tests/debug_wo_a3.py b/tests/debug_wo_a3.py
new file mode 100644
index 00000000..b276179c
--- /dev/null
+++ b/tests/debug_wo_a3.py
@@ -0,0 +1,67 @@
+"""Debug: diagnose wo_a grouped GEMM issue step by step."""
+import torch
+import torch.nn.functional as F
+import sys, os
+sys.path.insert(0, "/root/nvfp4-megamoe-kernel")
+
+from cutedsl.wo_a_grouped_linear import CuTeDSLNvfp4WoA
+from cutedsl.bridge import quantize_weight_to_nvfp4, quantize_to_nvfp4, quantize_activation_nvfp4
+
+torch.cuda.set_device(0)
+torch.manual_seed(42)
+
+# Small dimensions
+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
+print(f"z_ref amax={z_ref.amax():.4f} shape={z_ref.shape}")
+print(f"z_ref[0, 0, :8] = {z_ref[0, 0, :8]}")
+
+# Step 1: verify weight quantization per-group
+print("\n=== Weight quant ===")
+for g in range(G):
+    w_g = w[g*OR:(g+1)*OR, :].T  # (GI, OR)
+    w_fp4, w_sf, w_gs = quantize_weight_to_nvfp4(w_g)
+    print(f"  Group {g}: w_g shape={w_g.shape} w_fp4 shape={w_fp4.shape} w_sf shape={w_sf.shape} gs={w_gs:.6f}")
+
+# Step 2: test runner directly (bypass custom op)
+runner = CuTeDSLNvfp4WoA(
+    n_local_groups=G, heads_per_group=HPG, head_dim=HD,
+    o_lora_rank=OR, max_num_tokens=8, device=DEVICE,
+)
+runner.set_bf16_weight(w)
+runner.finalize_weights()
+runner._ensure_initialized()
+
+# Compute activation gs
+with torch.no_grad():
+    _, _, gs = quantize_to_nvfp4(o_g.reshape(T, GI)[:1])
+print(f"\nActivation gs from sample: {gs:.6f}")
+print(f"Runner gs: {runner._activation_global_scale:.6f}")
+
+runner._activation_global_scale = gs  # use the right one
+
+# Call _run_impl directly
+with torch.no_grad():
+    z_out = runner._run_impl(o)
+print(f"\nz_out shape={z_out.shape} amax={z_out.amax():.4f}")
+print(f"z_out[0, 0, :8] = {z_out[0, 0, :8]}")
+
+# Per-group comparison
+for g in range(G):
+    cos = F.cosine_similarity(z_ref[:, g, :].flatten().unsqueeze(0).float(),
+                               z_out[:, g, :].flatten().unsqueeze(0).float()).item()
+    print(f"  Group {g}: cosine={cos:.6f} ref_amax={z_ref[:, g, :].amax():.4f} out_amax={z_out[:, g, :].amax():.4f}")
+
+cos = F.cosine_similarity(z_ref.flatten().unsqueeze(0).float(), z_out.flatten().unsqueeze(0).float()).item()
+print(f"\nOverall cosine={cos:.6f}")