diff --git a/single_shot_inference.py b/single_shot_inference.py
index efe9fe06..04dbf2fd 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -357,6 +357,18 @@ class CUDAGraphDecoder:
             
             self.graphs_a[li] = graph_a
             
+            # Verify Graph A capture: replay immediately and check output is non-zero
+            # This catches issues like wrong device, stale data, or broken kernel arguments
+            if li < 3 or (li + 1) % 20 == 0:
+                torch.cuda.set_device(gpu)
+                graph_a.replay()
+                torch.cuda.synchronize()
+                xn_max = self.x_normed_bufs[li].abs().max().item()
+                qh_max = self.q_heads_bufs[li].abs().max().item()
+                print(f"    L{li} GraphA verify: x_normed |X|={xn_max:.4f} q_heads |X|={qh_max:.4f}", flush=True)
+                if xn_max == 0.0:
+                    print(f"    *** L{li} GraphA VERIFY FAILED: x_normed is all zeros! ***", flush=True)
+            
             # ======== Graph B: post-attention + FFN compute ========
             # Input:  X_mid = self.X_mid_bufs[li], F_attn = self.F_attn_bufs[li]
             # Output: X_next → self.x_out_bufs[li]
@@ -395,6 +407,16 @@ class CUDAGraphDecoder:
             
             self.graphs_b[li] = graph_b
             
+            # Verify Graph B capture: replay immediately and check output is non-zero
+            if li < 3 or (li + 1) % 20 == 0:
+                torch.cuda.set_device(gpu)
+                graph_b.replay()
+                torch.cuda.synchronize()
+                xo_max = self.x_out_bufs[li].abs().max().item()
+                print(f"    L{li} GraphB verify: x_out |X|={xo_max:.4f}", flush=True)
+                if xo_max == 0.0:
+                    print(f"    *** L{li} GraphB VERIFY FAILED: x_out is all zeros! ***", flush=True)
+            
             if (li + 1) % 10 == 0:
                 print(f"    Captured {li+1}/{self.n_layers} layer A/B graphs", flush=True)