diff --git a/examples/offline_inference/audio_language.py b/examples/offline_inference/audio_language.py
index e9878382b..f7106d7ea 100755
--- a/examples/offline_inference/audio_language.py
+++ b/examples/offline_inference/audio_language.py
@@ -89,6 +89,34 @@ def run_gemma3n(question: str, audio_count: int) -> ModelRequestData:
     )
 
 
+# GLM-ASR
+def run_glmasr(question: str, audio_count: int) -> ModelRequestData:
+    model_name = "zai-org/GLM-ASR-Nano-2512"
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+
+    # GLM-ASR uses <|pad|> token for audio
+    audio_placeholder = "<|pad|>" * audio_count
+
+    messages = [{"role": "user", "content": f"{audio_placeholder}{question}"}]
+    prompt = tokenizer.apply_chat_template(
+        messages, tokenize=False, add_generation_prompt=True
+    )
+
+    engine_args = EngineArgs(
+        model=model_name,
+        trust_remote_code=True,
+        max_model_len=4096,
+        max_num_seqs=2,
+        limit_mm_per_prompt={"audio": audio_count},
+    )
+
+    return ModelRequestData(
+        engine_args=engine_args,
+        prompt=prompt,
+    )
+
+
 # Granite Speech
 def run_granite_speech(question: str, audio_count: int) -> ModelRequestData:
     # NOTE - the setting in this example are somewhat different from what is
@@ -358,34 +386,6 @@ def run_voxtral(question: str, audio_count: int) -> ModelRequestData:
     )
 
 
-# GLM-ASR
-def run_glmasr(question: str, audio_count: int) -> ModelRequestData:
-    model_name = "zai-org/GLM-ASR-Nano-2512"
-
-    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-
-    # GLM-ASR uses <|pad|> token for audio
-    audio_placeholder = "<|pad|>" * audio_count
-
-    messages = [{"role": "user", "content": f"{audio_placeholder}{question}"}]
-    prompt = tokenizer.apply_chat_template(
-        messages, tokenize=False, add_generation_prompt=True
-    )
-
-    engine_args = EngineArgs(
-        model=model_name,
-        trust_remote_code=True,
-        max_model_len=4096,
-        max_num_seqs=2,
-        limit_mm_per_prompt={"audio": audio_count},
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompt=prompt,
-    )
-
-
 # Whisper
 def run_whisper(question: str, audio_count: int) -> ModelRequestData:
     assert audio_count == 1, "Whisper only support single audio input per prompt"
diff --git a/vllm/model_executor/models/glmasr.py b/vllm/model_executor/models/glmasr.py
index ac5ec84de..9f9fda431 100644
--- a/vllm/model_executor/models/glmasr.py
+++ b/vllm/model_executor/models/glmasr.py
@@ -181,6 +181,12 @@ class GlmAsrEncoderAttention(nn.Module):
 
         # Use vLLM's ApplyRotaryEmb CustomOp
         # enforce_enable=True ensures the op is always enabled (important for ViT)
+        rope_params = getattr(config, "rope_parameters", None)
+        if rope_params:
+            partial_rotary_factor = rope_params.get("partial_rotary_factor", 0.5)
+        else:
+            partial_rotary_factor = getattr(config, "partial_rotary_factor", 0.5)
+        self.rotary_dim = int(self.head_dim * partial_rotary_factor)
         self.apply_rotary_emb = ApplyRotaryEmb(enforce_enable=True)
 
         # Use vLLM's MMEncoderAttention for hardware-optimized attention
@@ -226,8 +232,12 @@ class GlmAsrEncoderAttention(nn.Module):
         # Apply rotary position embeddings using vLLM's ApplyRotaryEmb
         # ApplyRotaryEmb expects x: [batch, seq, heads, head_dim]
         # cos/sin: [seq_len, rotary_dim/2]
-        q = self.apply_rotary_emb(q, rotary_pos_emb_cos, rotary_pos_emb_sin)
-        k = self.apply_rotary_emb(k, rotary_pos_emb_cos, rotary_pos_emb_sin)
+        q[..., : self.rotary_dim] = self.apply_rotary_emb(
+            q[..., : self.rotary_dim], rotary_pos_emb_cos, rotary_pos_emb_sin
+        )
+        k[..., : self.rotary_dim] = self.apply_rotary_emb(
+            k[..., : self.rotary_dim], rotary_pos_emb_cos, rotary_pos_emb_sin
+        )
 
         # MMEncoderAttention expects [batch, seq, num_heads, head_dim]
         # It handles GQA internally via repeat_interleave