diff --git a/vllm/benchmarks/datasets.py b/vllm/benchmarks/datasets.py
index fdc4e9175a73c..45b58035ebe32 100644
--- a/vllm/benchmarks/datasets.py
+++ b/vllm/benchmarks/datasets.py
@@ -654,6 +654,9 @@ def get_samples(args, tokenizer) -> list[SampleRequest]:
         elif args.dataset_path in ASRDataset.SUPPORTED_DATASET_PATHS:
             dataset_class = ASRDataset
             args.hf_split = "train"
+        elif args.dataset_path in MLPerfDataset.SUPPORTED_DATASET_PATHS:
+            dataset_class = MLPerfDataset
+            args.hf_split = "train"
         else:
             supported_datasets = set([
                 dataset_name for cls in HuggingFaceDataset.__subclasses__()
@@ -1447,3 +1450,82 @@ class ASRDataset(HuggingFaceDataset):
             )
         self.maybe_oversample_requests(sampled_requests, num_requests)
         return sampled_requests
+
+
+# -----------------------------------------------------------------------------
+# MLPerf Dataset Implementation
+# -----------------------------------------------------------------------------
+
+
+class MLPerfDataset(HuggingFaceDataset):
+    """
+    MLPerf Inference Dataset.
+
+    Dataset on HF:
+    https://huggingface.co/datasets/mgoin/mlperf-inference-llama2-data
+    https://huggingface.co/datasets/mgoin/mlperf-inference-llama3.1-data
+
+    Each record contains:
+      - "system_prompt": system role instruction.
+      - "question": user question.
+      - "output": reference answer.
+
+    We combine the system prompt and question into a chat-formatted prompt
+    (using the tokenizer's chat template) and set the expected output length to
+    the tokenized length of the provided reference answer.
+    """
+
+    SUPPORTED_DATASET_PATHS = {
+        "mgoin/mlperf-inference-llama2-data",
+        "mgoin/mlperf-inference-llama3.1-data",
+    }
+
+    def sample(
+        self,
+        tokenizer: PreTrainedTokenizerBase,
+        num_requests: int,
+        output_len: Optional[int] = None,
+        **kwargs,
+    ) -> list[SampleRequest]:
+        # Force dynamic output length based on reference completion.
+        dynamic_output = output_len is None
+        sampled_requests: list[SampleRequest] = []
+
+        for item in self.data:
+            if len(sampled_requests) >= num_requests:
+                break
+
+            system_prompt = item["system_prompt"]
+            question = item["question"]
+            reference_answer = item["output"]
+
+            # Build chat-style prompt using tokenizer template, if available.
+            messages = [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": question},
+            ]
+            prompt_formatted = tokenizer.apply_chat_template(
+                messages, add_generation_prompt=True, tokenize=False
+            )
+            prompt_len = len(tokenizer(prompt_formatted).input_ids)
+
+            # Determine output length from reference answer tokens.
+            ref_out_len = len(
+                tokenizer(reference_answer, add_special_tokens=False).input_ids
+            )
+            expected_output_len = ref_out_len if dynamic_output else output_len
+
+            # Validate sequence lengths.
+            if not is_valid_sequence(prompt_len, expected_output_len):
+                continue
+
+            sampled_requests.append(
+                SampleRequest(
+                    prompt=prompt_formatted,
+                    prompt_len=prompt_len,
+                    expected_output_len=expected_output_len,
+                )
+            )
+
+        self.maybe_oversample_requests(sampled_requests, num_requests)
+        return sampled_requests