diff --git a/vllm/v1/attention/backends/mla/common.py b/vllm/v1/attention/backends/mla/common.py
index b09541dbf791..406e83bad305 100755
--- a/vllm/v1/attention/backends/mla/common.py
+++ b/vllm/v1/attention/backends/mla/common.py
@@ -1645,6 +1645,33 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
             # Convert from (L, N, P) to (N, P, L)
             self.W_UK_T = W_UK.permute(1, 2, 0)
 
+    def _concat_k_nope_k_pe(
+        self, k_nope: torch.Tensor, k_pe: torch.Tensor
+    ) -> torch.Tensor:
+        """
+        Efficiently concatenate k_nope and k_pe tensors along the last dimension.
+
+        This function avoids the performance penalty of torch.cat with expanded
+        non-contiguous tensors by pre-allocating the output and using direct copies.
+
+        Args:
+            k_nope: Tensor of shape [..., nope_dim]
+            k_pe: Tensor to broadcast and concatenate, typically shape [..., 1, pe_dim]
+                or [..., pe_dim]
+
+        Returns:
+            Tensor of shape [..., nope_dim + pe_dim]
+        """
+        k = torch.empty(
+            (*k_nope.shape[:-1], k_nope.shape[-1] + k_pe.shape[-1]),
+            dtype=k_nope.dtype,
+            device=k_nope.device,
+        )
+        # Direct copies with efficient broadcasting
+        k[..., : k_nope.shape[-1]] = k_nope
+        k[..., k_nope.shape[-1] :] = k_pe
+        return k
+
     def _compute_prefill_context(
         self,
         q: torch.Tensor,
@@ -1681,7 +1708,7 @@ def _compute_prefill_context(
             )
             k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-            k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
+            k = self._concat_k_nope_k_pe(k_nope, k_pe)
 
             attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
                 prefill=prefill_metadata,
@@ -1785,7 +1812,7 @@ def _context_parallel_compute_prefill_context(
                 -1, self.num_heads, self.qk_nope_head_dim + self.v_head_dim
             )
             k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
-            k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
+            k = self._concat_k_nope_k_pe(k_nope, k_pe)
 
             attn_output, attn_softmax_lse = self._run_prefill_context_chunk(
                 prefill=prefill_metadata,
@@ -1834,7 +1861,7 @@ def _forward_prefill(
         )
         k_nope, v = kv_nope.split([self.qk_nope_head_dim, self.v_head_dim], dim=-1)
 
-        k = torch.cat((k_nope, k_pe.expand((*k_nope.shape[:-1], -1))), dim=-1)
+        k = self._concat_k_nope_k_pe(k_nope, k_pe)
 
         output_prefill = self._run_prefill_new_tokens(
             prefill=attn_metadata.prefill,