diff --git a/benchmarks/README.md b/benchmarks/README.md
index 192d6c4022c83..2aa4a285021f1 100644
--- a/benchmarks/README.md
+++ b/benchmarks/README.md
@@ -6,3 +6,14 @@ You can download the dataset by running:
 ```bash
 wget https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered/resolve/main/ShareGPT_V3_unfiltered_cleaned_split.json
 ```
+
+## Downloading the ShareGPT4V dataset
+
+The json file refers to several image datasets (coco, llava, etc.). The benchmark scripts
+will ignore a datapoint if the referred image is missing.
+```bash
+wget https://huggingface.co/datasets/Lin-Chen/ShareGPT4V/resolve/main/sharegpt4v_instruct_gpt4-vision_cap100k.json
+mkdir coco -p
+wget http://images.cocodataset.org/zips/train2017.zip -O coco/train2017.zip
+unzip coco/train2017.zip -d coco/
+```
diff --git a/benchmarks/benchmark_throughput.py b/benchmarks/benchmark_throughput.py
index 1b41f99ce9dbb..ba69d6ff0b90e 100644
--- a/benchmarks/benchmark_throughput.py
+++ b/benchmarks/benchmark_throughput.py
@@ -9,7 +9,6 @@
 import torch
 import uvloop
 from PIL import Image
-from PIL import Image
 from tqdm import tqdm
 from transformers import (AutoModelForCausalLM, AutoTokenizer,
                           PreTrainedTokenizerBase)
@@ -61,33 +60,6 @@ def _get_prompt_for_image_model(question: str, *, model: str) -> str:
     raise ValueError(f"Unsupported model {model}")
 
 
-def sample_requests(tokenizer: PreTrainedTokenizerBase,
-                    args: argparse.Namespace) -> List[SampleRequest]:
-    dataset_path: str = args.dataset
-    num_requests: int = args.num_prompts
-    fixed_output_len: Optional[int] = args.output_len
-    model: str = args.model
-def _get_prompt_for_image_model(question: str, *, model: str) -> str:
-    """Prepend and append special tokens around the question to form a prompt.
-
-    Args:
-        question: The input question text to wrap with special tokens
-        model: The name of the model being used, to determine which special
-            tokens to add
-
-    Returns:
-        The formatted prompt string with appropriate special tokens for the
-            model
-
-    Raises:
-        ValueError: If an unsupported model name is provided
-    """
-    model = model.lower()
-    if "pixtral" in model:
-        return f"<s>[INST]{question}\n[IMG][/INST]"
-    raise ValueError(f"Unsupported model {model}")
-
-
 def sample_requests(tokenizer: PreTrainedTokenizerBase,
                     args: argparse.Namespace) -> List[SampleRequest]:
     dataset_path: str = args.dataset
@@ -107,7 +79,6 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
 
     # Filter out sequences that are too long or too short
     filtered_dataset: List[SampleRequest] = []
-    for data in dataset:
     for data in dataset:
         if len(filtered_dataset) == num_requests:
             break
@@ -116,25 +87,6 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
         prompt = data["conversations"][0]["value"]
         completion = data["conversations"][1]["value"]
 
-        multi_modal_data: Optional[MultiModalDataDict] = None
-        if "image" in data:
-            multi_modal_data = multi_modal_data or {}
-            image_path = data["image"]
-            # TODO(vllm-project/vllm/issues/9778): Support multiple images.
-            assert isinstance(image_path,
-                              str), "Only support single image input"
-            try:
-                multi_modal_data["image"] = Image.open(image_path).convert(
-                    "RGB")
-            except FileNotFoundError:
-                # Ignore datapoint where asset is missing
-                continue
-            prompt = _get_prompt_for_image_model(question=prompt, model=model)
-
-        # Only keep the first two turns of each conversation.
-        prompt = data["conversations"][0]["value"]
-        completion = data["conversations"][1]["value"]
-
         multi_modal_data: Optional[MultiModalDataDict] = None
         if "image" in data:
             multi_modal_data = multi_modal_data or {}
@@ -167,8 +119,6 @@ def sample_requests(tokenizer: PreTrainedTokenizerBase,
                           prompt_len=prompt_len,
                           expected_output_len=output_len,
                           multi_modal_data=multi_modal_data))
-                          expected_output_len=output_len,
-                          multi_modal_data=multi_modal_data))
 
     return filtered_dataset