diff --git a/single_shot_inference.py b/single_shot_inference.py
index 29d841a0..6f3d34fa 100644
--- a/single_shot_inference.py
+++ b/single_shot_inference.py
@@ -158,7 +158,7 @@ class CUDAGraphDecoder:
     def pre_allocate(self, cfg, attn_mhcs, ffn_mhcs, attn_norms, ffn_norms,
                      kv_caches, compressors, indexers, moe_runners, se_runners,
                      routers, prod_lins, layer_w, rope_caches, hc_head,
-                     final_norm_w, lm_head_lin):
+                     final_norm_w, lm_head_lin, comp_rope_caches=None):
         """Pre-allocate all I/O buffers with fixed addresses."""
         for li in range(self.n_layers):
             dev = self.devices[li % self.num_gpus]
@@ -170,7 +170,7 @@ class CUDAGraphDecoder:
     def capture(self, cfg, attn_mhcs, ffn_mhcs, attn_norms, ffn_norms,
                 kv_caches, compressors, indexers, moe_runners, se_runners,
                 routers, prod_lins, layer_w, rope_caches, hc_head,
-                final_norm_w, lm_head_lin, positions, token_id):
+                final_norm_w, lm_head_lin, positions, token_id, comp_rope_caches=None):
         """Capture CUDA graphs for all layers + lm_head.
         
         Must be called after one warmup step so that:
@@ -199,7 +199,9 @@ class CUDAGraphDecoder:
                     compressors.get(li), indexers.get(li),
                     moe_runners.get(li), se_runners.get(li), routers.get(li),
                     prod_lin=prod_lins.get(li),
-                    _use_fused_rmsnorm_quantize=True
+                    _use_fused_rmsnorm_quantize=True,
+                    comp_rope_cos=comp_rope_caches[gpu][0] if comp_rope_caches else None,
+                    comp_rope_sin=comp_rope_caches[gpu][1] if comp_rope_caches else None,
                 )
                 # Copy output to fixed buffer
                 self.x_out_bufs[li].copy_(X_out)
@@ -855,7 +857,10 @@ def forward_attention(x_normed, w, li, cfg, rope_cos, rope_sin,
             rope_bf16 = comp_kv_fp32[:, nope_dim:].bfloat16().contiguous()  # (n_comp, 64) BF16
             # Apply RoPE on BF16 rope dims (existing BF16 RoPE kernel)
             rope_3d = rope_bf16.unsqueeze(1)  # (n_comp, 1, 64)
-            rope_3d = _apply_rope(rope_3d, comp_pos, rope_cos, rope_sin, rd)
+            # Use compress_rope_theta cache for compressed entries if available
+            crc = comp_rope_cos if comp_rope_cos is not None else rope_cos
+            crs = comp_rope_sin if comp_rope_sin is not None else rope_sin
+            rope_3d = _apply_rope(rope_3d, comp_pos, crc, crs, rd)
             rope_bf16 = rope_3d.squeeze(1)  # (n_comp, 64) BF16
             # Quantize non-RoPE part FP32 → FP8_E4M3
             nope_fp8, nope_scale = kv_mod.quantize_fp8_e4m3_from_fp32(nope_fp32)
@@ -1527,6 +1532,7 @@ def main():
                                   moe_runners.get(li), se_runners.get(li), routers.get(li),
                                   prod_lin=prod_lins.get(li),
                                   _use_fused_rmsnorm_quantize=not _args.no_fused_rmsnorm,
+                                  comp_rope_cos=comp_rope_caches[gpu][0], comp_rope_sin=comp_rope_caches[gpu][1],
                                   )
             except Exception as e:
                 torch.cuda.synchronize()
@@ -1607,6 +1613,7 @@ def main():
                               _profile_detail=(profile and step == 1),
                               _profile_times=cuda_layer_events if (profile and step == 1) else None,
                               _use_fused_rmsnorm_quantize=not _args.no_fused_rmsnorm,
+                              comp_rope_cos=comp_rope_caches[gpu][0], comp_rope_sin=comp_rope_caches[gpu][1],
                               )
         X = X.to('cuda:0'); torch.cuda.set_device(0)
         t_layers = time.perf_counter()