diff --git a/vllm/model_executor/layers/layernorm.py b/vllm/model_executor/layers/layernorm.py
index 7b222f9c431b..37dc4e132fc2 100644
--- a/vllm/model_executor/layers/layernorm.py
+++ b/vllm/model_executor/layers/layernorm.py
@@ -207,11 +207,15 @@ def forward_static(
             x = x + residual
             residual = x.to(orig_dtype)
 
-        if x.shape[-1] != hidden_size:
+        if weight is not None and x.shape[-1] != hidden_size:
             raise ValueError(
                 f"Expected hidden_size to be {hidden_size}, but found: {x.shape[-1]}"
             )
 
+        # When weight is None (weightless norm), use input dim for validation
+        if weight is None:
+            hidden_size = x.shape[-1]
+
         if variance_size_override is None:
             x_var = x
         else:
@@ -266,7 +270,10 @@ def forward_cuda(
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if residual is None and not envs.VLLM_BATCH_INVARIANT:
             return ir.ops.rms_norm(
-                x, self.weight.data, self.variance_epsilon, self.variance_size_override
+                x,
+                self.weight.data if self.has_weight else None,
+                self.variance_epsilon,
+                self.variance_size_override,
             )
 
         if self.variance_size_override is not None:
@@ -313,13 +320,21 @@ def forward_cuda(
                     )
                     return x, residual
 
+        # When has_weight is False, fall back to forward_native which
+        # handles weightless norms via forward_static without allocating
+        # a dummy ones tensor on every call.
+        if not self.has_weight:
+            return self.forward_native(x, residual)
+
         if residual is not None:
             return fused_add_rms_norm(
                 x, residual, self.weight.data, self.variance_epsilon
             )
         else:
             assert envs.VLLM_BATCH_INVARIANT
-            return rms_norm_batch_invariant(x, self.weight.data, self.variance_epsilon)
+            return rms_norm_batch_invariant(
+                x, self.weight.data, self.variance_epsilon
+            )
 
     def forward_hip(
         self,
@@ -328,19 +343,30 @@ def forward_hip(
     ) -> torch.Tensor | tuple[torch.Tensor, torch.Tensor]:
         if residual is None and not envs.VLLM_BATCH_INVARIANT:
             return ir.ops.rms_norm(
-                x, self.weight.data, self.variance_epsilon, self.variance_size_override
+                x,
+                self.weight.data if self.has_weight else None,
+                self.variance_epsilon,
+                self.variance_size_override,
             )
 
         if self.variance_size_override is not None:
             return self.forward_native(x, residual)
 
+        # When has_weight is False, fall back to forward_native which
+        # handles weightless norms via forward_static without allocating
+        # a dummy ones tensor on every call.
+        if not self.has_weight:
+            return self.forward_native(x, residual)
+
         if residual is not None:
             return self.rocm_norm_func_with_add(
                 x, residual, self.weight.data, self.variance_epsilon
             )
         else:
             assert envs.VLLM_BATCH_INVARIANT
-            return rms_norm_batch_invariant(x, self.weight.data, self.variance_epsilon)
+            return rms_norm_batch_invariant(
+                x, self.weight.data, self.variance_epsilon
+            )
 
     def forward_xpu(
         self,