diff --git a/python/sglang/srt/layers/layernorm.py b/python/sglang/srt/layers/layernorm.py
index d7b4cbd1f..7fde05894 100644
--- a/python/sglang/srt/layers/layernorm.py
+++ b/python/sglang/srt/layers/layernorm.py
@@ -140,6 +140,8 @@ class RMSNorm(MultiPlatformOp):
         post_residual_addition: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             out, _, residual_out = torch_npu.npu_add_rms_norm(
                 residual, x, self.weight.data, self.variance_epsilon
             )
@@ -155,6 +157,8 @@ class RMSNorm(MultiPlatformOp):
         if residual is not None:
             residual_out = torch.empty_like(x)
             output = torch.empty_like(x)
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             fused_add_rms_norm(
                 output,
                 x,
@@ -178,6 +182,8 @@ class RMSNorm(MultiPlatformOp):
         if residual is not None:
             out = torch.empty_like(x)
             residual_out = torch.empty_like(x)
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             fused_add_rms_norm(
                 out, x, residual_out, residual, self.weight.data, self.variance_epsilon
             )
@@ -244,6 +250,8 @@ class RMSNorm(MultiPlatformOp):
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if _is_cpu_amx_available:
             if residual is not None:
+                if post_residual_addition is not None:
+                    residual = residual + post_residual_addition
                 torch.ops.sgl_kernel.fused_add_rmsnorm_cpu(
                     x, residual, self.weight.data, self.variance_epsilon
                 )
@@ -263,6 +271,8 @@ class RMSNorm(MultiPlatformOp):
         if self.variance_size_override is not None:
             return self.forward_native(x, residual, post_residual_addition)
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             fused_add_rmsnorm(x, residual, self.weight.data, self.variance_epsilon)
             return x, residual
         out = rmsnorm(x, self.weight.data, self.variance_epsilon)
@@ -284,6 +294,8 @@ class RMSNorm(MultiPlatformOp):
             )
 
             if get_tensor_model_parallel_world_size() > 1:
+                if post_residual_addition is not None:
+                    residual = residual + post_residual_addition
                 fused_result = flashinfer_allreduce_residual_rmsnorm(
                     input_tensor=x,
                     residual=residual,
@@ -389,6 +401,8 @@ class GemmaRMSNorm(MultiPlatformOp):
         post_residual_addition: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             gemma_fused_add_rmsnorm(
                 x, residual, self.weight.data, self.variance_epsilon
             )
@@ -404,6 +418,8 @@ class GemmaRMSNorm(MultiPlatformOp):
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         orig_dtype = x.dtype
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             x = x + residual
             residual = x
 
@@ -430,6 +446,8 @@ class GemmaRMSNorm(MultiPlatformOp):
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if _is_cpu_amx_available:
             if residual is not None:
+                if post_residual_addition is not None:
+                    residual = residual + post_residual_addition
                 torch.ops.sgl_kernel.gemma_fused_add_rmsnorm_cpu(
                     x, residual, self.weight.data, self.variance_epsilon
                 )
@@ -446,6 +464,8 @@ class GemmaRMSNorm(MultiPlatformOp):
         post_residual_addition: Optional[torch.Tensor] = None,
     ) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             x = x + residual
             residual = x
 
diff --git a/python/sglang/srt/layers/quantization/modelslim/modelslim.py b/python/sglang/srt/layers/quantization/modelslim/modelslim.py
index b9ba88fca..95aa1fc9d 100644
--- a/python/sglang/srt/layers/quantization/modelslim/modelslim.py
+++ b/python/sglang/srt/layers/quantization/modelslim/modelslim.py
@@ -50,6 +50,8 @@ def npu_wrapper_rmsnorm_forward(func):
         if not x.is_contiguous():
             x = x.contiguous()
         if residual is not None:
+            if post_residual_addition is not None:
+                residual = residual + post_residual_addition
             out, residual_out = add_rmsnorm_bias(
                 x,
                 residual,