diff --git a/scripts/cache_aware_proxy.py b/scripts/cache_aware_proxy.py
index bcb285c..a60f66a 100644
--- a/scripts/cache_aware_proxy.py
+++ b/scripts/cache_aware_proxy.py
@@ -384,14 +384,18 @@ async def _handle_combined(api, req_data, token_ids, input_length, session_id, h
         d_candidate = min(remaining, key=lambda c: c.ongoing_tokens) if remaining else p_candidate
 
         # Cost model: compare co-located vs direct-RDMA-read offload
-        # Co-located: queue on C_s + prefill new tokens on C_s
+        # Co-located cost includes interference: heavy prefill on C_s blocks
+        # its ongoing decode requests, degrading their TPOT.
         cs_queue = best_inst.pending_prefill_tokens / SETTINGS.prefill_throughput
-        colocated_cost = cs_queue + estimated_new / SETTINGS.prefill_throughput
+        prefill_time = estimated_new / SETTINGS.prefill_throughput
+        # Interference penalty: if C_s has decode requests, heavy prefill disrupts them
+        interference = prefill_time * min(best_inst.num_requests, 3) * 0.3
+        colocated_cost = cs_queue + prefill_time + interference
 
-        # Direct RDMA read: D reads C_s's cached blocks via RDMA + D prefills new tokens locally
-        # D's queue + RDMA read time + D local prefill of new tokens only
+        # Direct RDMA read: D reads cached blocks + prefills new tokens locally
+        # C_s is not involved → zero interference on C_s's decode
         d_queue = d_candidate.pending_prefill_tokens / SETTINGS.prefill_throughput
-        offload_cost = d_queue + SETTINGS.rdma_overhead_s + estimated_new / SETTINGS.prefill_throughput
+        offload_cost = d_queue + SETTINGS.rdma_overhead_s + prefill_time
 
         breakdown["cache_ratio"] = cache_ratio
         breakdown["colocated_cost"] = round(colocated_cost, 2)