diff --git a/examples/offline_inference/audio_language.py b/examples/offline_inference/audio_language.py
index a6d0c5d12dd4..4bc2112c0aa1 100755
--- a/examples/offline_inference/audio_language.py
+++ b/examples/offline_inference/audio_language.py
@@ -213,37 +213,6 @@ def run_phi4mm(question: str, audio_count: int) -> ModelRequestData:
     )
 
 
-def run_phi4_multimodal(question: str, audio_count: int) -> ModelRequestData:
-    """
-    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
-    show how to process audio inputs.
-    """
-    model_path = snapshot_download(
-        "microsoft/Phi-4-multimodal-instruct", revision="refs/pr/70"
-    )
-    # Since the vision-lora and speech-lora co-exist with the base model,
-    # we have to manually specify the path of the lora weights.
-    speech_lora_path = os.path.join(model_path, "speech-lora")
-    placeholders = "<|audio|>" * audio_count
-
-    prompts = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
-
-    engine_args = EngineArgs(
-        model=model_path,
-        max_model_len=12800,
-        max_num_seqs=2,
-        enable_lora=True,
-        max_lora_rank=320,
-        limit_mm_per_prompt={"audio": audio_count},
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompt=prompts,
-        lora_requests=[LoRARequest("speech", 1, speech_lora_path)],
-    )
-
-
 # Qwen2-Audio
 def run_qwen2_audio(question: str, audio_count: int) -> ModelRequestData:
     model_name = "Qwen/Qwen2-Audio-7B-Instruct"
@@ -416,7 +385,6 @@ def run_whisper(question: str, audio_count: int) -> ModelRequestData:
     "midashenglm": run_midashenglm,
     "minicpmo": run_minicpmo,
     "phi4_mm": run_phi4mm,
-    "phi4_multimodal": run_phi4_multimodal,
     "qwen2_audio": run_qwen2_audio,
     "qwen2_5_omni": run_qwen2_5_omni,
     "ultravox": run_ultravox,
diff --git a/examples/offline_inference/vision_language.py b/examples/offline_inference/vision_language.py
index dd5b22ae9b0f..dfca7d5c9c9a 100755
--- a/examples/offline_inference/vision_language.py
+++ b/examples/offline_inference/vision_language.py
@@ -1424,41 +1424,6 @@ def run_phi4mm(questions: list[str], modality: str) -> ModelRequestData:
     )
 
 
-# HF format Phi-4-multimodal-instruct
-def run_phi4_multimodal(questions: list[str], modality: str) -> ModelRequestData:
-    """
-    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
-    show how to process image inputs.
-    """
-    assert modality == "image"
-    model_path = snapshot_download(
-        "microsoft/Phi-4-multimodal-instruct", revision="refs/pr/70"
-    )
-    # Since the vision-lora and speech-lora co-exist with the base model,
-    # we have to manually specify the path of the lora weights.
-    vision_lora_path = os.path.join(model_path, "vision-lora")
-    prompts = [
-        f"<|user|><|image|>{question}<|end|><|assistant|>" for question in questions
-    ]
-    engine_args = EngineArgs(
-        model=model_path,
-        max_model_len=5120,
-        max_num_seqs=2,
-        max_num_batched_tokens=12800,
-        enable_lora=True,
-        max_lora_rank=320,
-        # Note - mm_processor_kwargs can also be passed to generate/chat calls
-        mm_processor_kwargs={"dynamic_hd": 16},
-        limit_mm_per_prompt={"image": 1},
-    )
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompts=prompts,
-        lora_requests=[LoRARequest("vision", 1, vision_lora_path)],
-    )
-
-
 # Pixtral HF-format
 def run_pixtral_hf(questions: list[str], modality: str) -> ModelRequestData:
     assert modality == "image"
@@ -1904,7 +1869,6 @@ def run_tarsier2(questions: list[str], modality: str) -> ModelRequestData:
     "paligemma2": run_paligemma2,
     "phi3_v": run_phi3v,
     "phi4_mm": run_phi4mm,
-    "phi4_multimodal": run_phi4_multimodal,
     "pixtral_hf": run_pixtral_hf,
     "qwen_vl": run_qwen_vl,
     "qwen2_vl": run_qwen2_vl,
diff --git a/examples/offline_inference/vision_language_multi_image.py b/examples/offline_inference/vision_language_multi_image.py
index 3c01806baa20..2d7aece527ae 100755
--- a/examples/offline_inference/vision_language_multi_image.py
+++ b/examples/offline_inference/vision_language_multi_image.py
@@ -932,40 +932,6 @@ def load_phi4mm(question: str, image_urls: list[str]) -> ModelRequestData:
     )
 
 
-def load_phi4_multimodal(question: str, image_urls: list[str]) -> ModelRequestData:
-    """
-    Phi-4-multimodal-instruct supports both image and audio inputs. Here, we
-    show how to process multi images inputs.
-    """
-
-    model_path = snapshot_download(
-        "microsoft/Phi-4-multimodal-instruct", revision="refs/pr/70"
-    )
-    # Since the vision-lora and speech-lora co-exist with the base model,
-    # we have to manually specify the path of the lora weights.
-    vision_lora_path = os.path.join(model_path, "vision-lora")
-    engine_args = EngineArgs(
-        model=model_path,
-        max_model_len=4096,
-        max_num_seqs=2,
-        limit_mm_per_prompt={"image": len(image_urls)},
-        enable_lora=True,
-        max_lora_rank=320,
-        # Note - mm_processor_kwargs can also be passed to generate/chat calls
-        mm_processor_kwargs={"dynamic_hd": 4},
-    )
-
-    placeholders = "<|image|>" * len(image_urls)
-    prompt = f"<|user|>{placeholders}{question}<|end|><|assistant|>"
-
-    return ModelRequestData(
-        engine_args=engine_args,
-        prompt=prompt,
-        image_data=[fetch_image(url) for url in image_urls],
-        lora_requests=[LoRARequest("vision", 1, vision_lora_path)],
-    )
-
-
 def load_qwen_vl_chat(question: str, image_urls: list[str]) -> ModelRequestData:
     model_name = "Qwen/Qwen-VL-Chat"
     engine_args = EngineArgs(
@@ -1363,7 +1329,6 @@ def load_glm4_5v_fp8(question: str, image_urls: list[str]) -> ModelRequestData:
     "paddleocr_vl": load_paddleocr_vl,
     "phi3_v": load_phi3v,
     "phi4_mm": load_phi4mm,
-    "phi4_multimodal": load_phi4_multimodal,
     "pixtral_hf": load_pixtral_hf,
     "qwen_vl_chat": load_qwen_vl_chat,
     "qwen2_vl": load_qwen2_vl,