diff --git a/vllm/distributed/elastic_ep/elastic_execute.py b/vllm/distributed/elastic_ep/elastic_execute.py
index 24979b62af6d..163cec47e4dd 100644
--- a/vllm/distributed/elastic_ep/elastic_execute.py
+++ b/vllm/distributed/elastic_ep/elastic_execute.py
@@ -566,3 +566,45 @@ def receive_expert_mapping(self) -> tuple[torch.Tensor, int, int]:
     def prepare_new_worker(self) -> None:
         with set_current_vllm_config(self.worker.vllm_config):
             prepare_communication_buffer_for_model(self.worker.model_runner.get_model())
+
+    def rewarm_workspace(self) -> None:
+        # Must run on every DP sibling in lockstep: _dummy_run calls
+        # coordinate_batch_across_dp whenever data_parallel_size > 1
+        # (gpu_model_runner.py:3663), which deadlocks if any rank skips it.
+
+        # Save and clear block tables so profile_run/compile_or_warm_up_model
+        # don't write dummy slot mappings into real KV-cache blocks (mirrors
+        # switch_and_prepare's pattern).
+        multi_block_table = self.worker.model_runner.input_batch.block_table
+        saved_block_tables: list[tuple[torch.Tensor, torch.Tensor]] = []
+        for bt in multi_block_table.block_tables:
+            saved_block_tables.append(
+                (bt.block_table.gpu.clone(), bt.block_table.cpu.clone())
+            )
+        multi_block_table.clear()
+
+        # _ensure_workspace_size allocates a fresh tensor on grow, leaving
+        # captured CUDA graphs with stale data pointers; drop graphs before
+        # re-warm so captures realign with the resized buffer.
+        self._release_cuda_graphs()
+        unlock_workspace()
+
+        # Grow the MoE workspace at max_num_tokens.
+        # compile_or_warm_up_model alone only exercises cudagraph-capture
+        # sizes (≤64 tokens for this test) and leaves the workspace at
+        # ~10-14 MB; the post-all-to-all per-rank token count under real
+        # post-reshuffle routing needs hundreds of MB. Use _dummy_run
+        # directly (rather than profile_run) with skip_eplb=True so dummy
+        # routing doesn't pollute the just-rebalanced EPLB stats — same
+        # convention compile_or_warm_up_model itself uses.
+        runner = self.worker.model_runner
+        runner._dummy_run(runner.max_num_tokens, is_profile=True, skip_eplb=True)
+        self.worker.compile_or_warm_up_model()
+
+        lock_workspace()
+
+        for bt, (saved_gpu, saved_cpu) in zip(
+            multi_block_table.block_tables, saved_block_tables
+        ):
+            bt.block_table.gpu.copy_(saved_gpu)
+            bt.block_table.cpu.copy_(saved_cpu)
diff --git a/vllm/distributed/elastic_ep/elastic_state.py b/vllm/distributed/elastic_ep/elastic_state.py
index bace771a2ab6..256efe46a4a4 100644
--- a/vllm/distributed/elastic_ep/elastic_state.py
+++ b/vllm/distributed/elastic_ep/elastic_state.py
@@ -538,6 +538,11 @@ def _eplb_reshuffle(self):
         self.model_executor.collective_rpc(
             "elastic_ep_execute", args=("perform_eplb_reshuffle",)
         )
+        # Reshuffle changes per-rank token routing; the locked MoE workspace
+        # may now be too small. Rewarm covers both new and existing engines.
+        self.model_executor.collective_rpc(
+            "elastic_ep_execute", args=("rewarm_workspace",)
+        )
         assert self.new_dp_group is not None
         if self.new_dp_group.rank() == 0:
             logger.info("[Elastic EP] EPLB reshuffle completed")
diff --git a/vllm/v1/engine/utils.py b/vllm/v1/engine/utils.py
index 1f0b9bbb19d5..a28c1366dd84 100644
--- a/vllm/v1/engine/utils.py
+++ b/vllm/v1/engine/utils.py
@@ -308,6 +308,18 @@ def get_device_indices(
     return value
 
 
+def _apply_dp_identity_suffix(dp_vllm_config, dp_rank: int) -> None:
+    # Ray actor names (RayExecutorV2) and KV-connector engine_ids must
+    # be unique across sibling DP engines or registration collides.
+    # Use the global DP rank, not a node-local rank, since sibling DP
+    # engines can span multiple nodes.
+    dp_vllm_config.instance_id = f"{dp_vllm_config.instance_id}_dp{dp_rank}"
+    if dp_vllm_config.kv_transfer_config is not None:
+        dp_vllm_config.kv_transfer_config.engine_id = (
+            f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{dp_rank}"
+        )
+
+
 class CoreEngineActorManager:
     """
     Utility class to handle creation, readiness, and shutdown
@@ -404,20 +416,10 @@ def __init__(
         ):
             dp_vllm_config = copy.deepcopy(vllm_config)
             if dp_size > 1:
-                # Append the DP rank to instance_id so that per-engine
-                # identifiers (e.g. Ray actor names in RayExecutorV2) are
-                # unique across DP replicas.
-                dp_vllm_config.instance_id = f"{dp_vllm_config.instance_id}_dp{index}"
+                _apply_dp_identity_suffix(dp_vllm_config, index)
             dp_vllm_config.parallel_config.placement_group = pg
             local_client = index < local_engine_count
 
-            if dp_size > 1 and dp_vllm_config.kv_transfer_config is not None:
-                # modify the engine_id and append the local_dp_rank to it to ensure
-                # that the kv_transfer_config is unique for each DP rank.
-                dp_vllm_config.kv_transfer_config.engine_id = (
-                    f"{dp_vllm_config.kv_transfer_config.engine_id}_dp{local_index}"
-                )
-
             # Ray XPU known issue: dpctl initializes the GPU runtime early, so
             # setting device env vars in Ray actor's initialization method
             # will not affect device selection. See:
@@ -789,6 +791,8 @@ def scale_up_elastic_ep(
         for i, (pg, local_rank) in enumerate(zip(placement_groups, local_dp_ranks)):
             rank = cur_data_parallel_size + i
             dp_vllm_config = copy.deepcopy(cur_vllm_config)
+            if new_data_parallel_size > 1:
+                _apply_dp_identity_suffix(dp_vllm_config, rank)
             dp_vllm_config.parallel_config.data_parallel_size = new_data_parallel_size
             dp_vllm_config.parallel_config.placement_group = pg