diff --git a/python/sglang/srt/models/qwen3_next.py b/python/sglang/srt/models/qwen3_next.py
index fd0d0e942..cdba9975f 100644
--- a/python/sglang/srt/models/qwen3_next.py
+++ b/python/sglang/srt/models/qwen3_next.py
@@ -518,24 +518,10 @@ class Qwen3HybridLinearDecoderLayer(nn.Module):
                 hidden_act=config.hidden_act,
                 quant_config=quant_config,
             )
-        if getattr(
-            config, "use_gemma_rms_norm", getattr(config, "apply_layernorm_1p", False)
-        ):
-            logger.warning_once(
-                "Using Gemma RMSNorm for input normalization and post attn normalization."
-            )
-            self.input_layernorm = GemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
-            self.post_attention_layernorm = GemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
-        else:
-            self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-            self.post_attention_layernorm = RMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
-
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
         self.layer_communicator = LayerCommunicator(
             layer_scatter_modes=self.layer_scatter_modes,
             input_layernorm=self.input_layernorm,
@@ -685,23 +671,10 @@ class Qwen3HybridAttentionDecoderLayer(nn.Module):
                 hidden_act=config.hidden_act,
                 quant_config=quant_config,
             )
-        if getattr(
-            config, "use_gemma_rms_norm", getattr(config, "apply_layernorm_1p", False)
-        ):
-            logger.warning_once(
-                "Using Gemma RMSNorm for input normalization and post attn normalization."
-            )
-            self.input_layernorm = GemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
-            self.post_attention_layernorm = GemmaRMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
-        else:
-            self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-            self.post_attention_layernorm = RMSNorm(
-                config.hidden_size, eps=config.rms_norm_eps
-            )
+        self.input_layernorm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = GemmaRMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
 
         self.q_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
         self.k_norm = GemmaRMSNorm(self.head_dim, eps=config.rms_norm_eps)
@@ -844,13 +817,7 @@ class Qwen3NextModel(nn.Module):
             config.num_hidden_layers, get_layer, prefix=f"{prefix}.layers"
         )
 
-        if getattr(
-            config, "use_gemma_rms_norm", getattr(config, "apply_layernorm_1p", False)
-        ):
-            logger.warning_once("Using Gemma RMSNorm for final normalization.")
-            self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
-        else:
-            self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.norm = GemmaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
         self.infer_count = 0
 
     def forward(
diff --git a/python/sglang/srt/models/qwen3_next_mtp.py b/python/sglang/srt/models/qwen3_next_mtp.py
index 4630ea300..a9da0867d 100644
--- a/python/sglang/srt/models/qwen3_next_mtp.py
+++ b/python/sglang/srt/models/qwen3_next_mtp.py
@@ -54,15 +54,7 @@ class Qwen3NextForCausalLMMTP(Qwen3NextForCausalLM):
         # (1) do not use_dedicated_mtp_embeddings provided in ckpt since not provided and directly use the target model embeddings
         # (2) hardcode bias=False since not provided
         self.fc = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
-        if getattr(
-            config, "use_gemma_rms_norm", getattr(config, "apply_layernorm_1p", False)
-        ):
-            logger.warning_once(
-                "Using Gemma RMSNorm for input normalization and post attn normalization."
-            )
-            RMSNorm_cls = GemmaRMSNorm
-        else:
-            RMSNorm_cls = RMSNorm
+        RMSNorm_cls = GemmaRMSNorm
         self.pre_fc_norm_embedding = RMSNorm_cls(
             config.hidden_size, config.rms_norm_eps
         )