diff --git a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
index d73e05562951..9a1057e8c051 100644
--- a/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
+++ b/vllm/distributed/kv_transfer/kv_connector/v1/nixl_connector.py
@@ -752,6 +752,7 @@ def __init__(self, vllm_config: VllmConfig, engine_id: str):
         self._use_flashinfer = attn_backend == _Backend.FLASHINFER
         self._use_pallas = attn_backend == _Backend.PALLAS
         self.kv_cache_layout = get_kv_cache_layout()
+        self.host_buffer_kv_cache_layout = self.kv_cache_layout
         logger.debug("Detected attention backend %s", self.backend_name)
         logger.debug("Detected kv cache layout %s", self.kv_cache_layout)
 
@@ -864,6 +865,20 @@ def initialize_host_xfer_buffer(self, kv_caches: dict[str, torch.Tensor]) -> Non
             for layer_name, kv_cache in kv_caches.items():
                 kv_shape = kv_cache.shape
                 kv_dtype = kv_cache.dtype
+                if (
+                    self.kv_cache_layout == "NHD"
+                    and self.vllm_config.kv_transfer_config is not None
+                    and self.vllm_config.kv_transfer_config.enable_permute_local_kv
+                ):
+                    logger.info_once(
+                        "'enable_permute_local_kv' flag is enabled while "
+                        "device KV Layout is NHD. Init host buffer with"
+                        " HND to better support Decode/Prefill TP_ratio > 1."
+                    )
+                    # Since NHD will not support Decode/Prefill TP_ratio > 1,
+                    # we can leverage host_buffer for permute
+                    self.host_buffer_kv_cache_layout = "HND"
+                    kv_shape = tuple(kv_shape[i] for i in [0, 1, 3, 2, 4])
                 xfer_buffers[layer_name] = torch.empty(
                     kv_shape, dtype=kv_dtype, device="cpu"
                 )
@@ -1099,7 +1114,9 @@ def register_kv_caches(self, kv_caches: dict[str, torch.Tensor]):
             num_blocks=self.num_blocks,
             block_lens=self.block_len_per_layer,
             attn_backend_name=self.backend_name,
-            kv_cache_layout=self.kv_cache_layout,
+            kv_cache_layout=self.kv_cache_layout
+            if not self.use_host_buffer
+            else self.host_buffer_kv_cache_layout,
         )
         ready_event = threading.Event()
         self._nixl_handshake_listener_t = threading.Thread(
@@ -1255,7 +1272,12 @@ def _validate_remote_agent_handshake(
         assert not self._use_pallas or tp_ratio == 1, (
             "TPU (pallas_v1) DOES NOT support heterogeneous TP yet."
         )
-        if not self.use_mla and nixl_agent_meta.kv_cache_layout != self.kv_cache_layout:
+        kv_cache_layout = (
+            self.kv_cache_layout
+            if not self.use_host_buffer
+            else self.host_buffer_kv_cache_layout
+        )
+        if not self.use_mla and nixl_agent_meta.kv_cache_layout != kv_cache_layout:
             if (
                 self.kv_transfer_config.enable_permute_local_kv
                 and nixl_agent_meta.kv_cache_layout == "HND"
@@ -1281,9 +1303,6 @@ def _validate_remote_agent_handshake(
             )
             remote_block_size = remote_block_len // (self.slot_size_per_layer[0])
         else:
-            if tp_ratio > 1 and self.device_type == "xpu":
-                # XPU uses NHD, hence it does not support splitting on H
-                raise ValueError("Heterogeneous TP is not supported on XPU")
             # When MLA is not used, this is a list of the same block length
             for block_len in nixl_agent_meta.block_lens:
                 assert block_len == remote_block_len, (
diff --git a/vllm/platforms/xpu.py b/vllm/platforms/xpu.py
index 5799f97b8038..44bb73acbb6e 100644
--- a/vllm/platforms/xpu.py
+++ b/vllm/platforms/xpu.py
@@ -160,6 +160,8 @@ def check_and_update_config(cls, vllm_config: VllmConfig) -> None:
         # check and update parallel config
         parallel_config = vllm_config.parallel_config
         parallel_config.worker_cls = "vllm.v1.worker.xpu_worker.XPUWorker"
+        if vllm_config.kv_transfer_config is not None:
+            vllm_config.kv_transfer_config.enable_permute_local_kv = True
 
         if parallel_config.distributed_executor_backend is None:
             if parallel_config.world_size > 1:
@@ -261,6 +263,10 @@ def insert_blocks_to_device(
     ) -> None:
         """Copy blocks from src_cache to dst_cache on XPU."""
         _src_cache = src_cache[:, src_block_indices]
+        if _src_cache.shape[2:] != dst_cache.shape[2:]:
+            # To support TP_ratio, HOST KV might be initiated with HND
+            # while XPU device KV is with NHD
+            _src_cache = _src_cache.permute(0, 1, 3, 2, 4)
         dst_cache[:, dst_block_indices] = _src_cache.to(dst_cache.device)
 
     @classmethod
@@ -273,4 +279,8 @@ def swap_out_blocks_to_host(
     ) -> None:
         """Copy blocks from XPU to host (CPU)."""
         _src_cache = src_cache[:, src_block_indices]
+        if _src_cache.shape[2:] != dst_cache.shape[2:]:
+            # XPU device KV is with NHD while HOST KV
+            # might be initiated with HND for TP_ratio support
+            _src_cache = _src_cache.permute(0, 1, 3, 2, 4)
         dst_cache[:, dst_block_indices] = _src_cache.cpu()