diff --git a/torchtitan/config/job_config.py b/torchtitan/config/job_config.py
index a2247aa21..36b0f139d 100644
--- a/torchtitan/config/job_config.py
+++ b/torchtitan/config/job_config.py
@@ -182,6 +182,9 @@ class Training:
     loaded from this path instead of downloaded.
     """
 
+    synthetic_data: bool = False
+    """Use synthetic data"""
+
     local_batch_size: int = 8
     """Local batch size (i.e., per-device batch size)"""
 
diff --git a/torchtitan/datasets/hf_datasets.py b/torchtitan/datasets/hf_datasets.py
index 0e30f8fe5..811ae66c8 100644
--- a/torchtitan/datasets/hf_datasets.py
+++ b/torchtitan/datasets/hf_datasets.py
@@ -7,20 +7,53 @@
 from dataclasses import dataclass
 
 from functools import partial
-from typing import Any, Callable
+from typing import Any, Callable, Iterator, Tuple, Dict
 
 import torch
 
 from datasets import Dataset, load_dataset
 from datasets.distributed import split_dataset_by_node
 from torch.distributed.checkpoint.stateful import Stateful
-from torch.utils.data import IterableDataset
+from torch.utils.data import IterableDataset, DataLoader
 
 from torchtitan.components.dataloader import ParallelAwareDataloader
 from torchtitan.components.tokenizer import BaseTokenizer
 from torchtitan.config import JobConfig
 from torchtitan.tools.logging import logger
 
+class SyntheticIterableDataset(IterableDataset):
+    def __init__(
+        self,
+        batch_size: int,
+        seq_len: int,
+        vocab_size: int,
+        device: torch.device,
+        dtype: torch.dtype = torch.long,
+    ):
+        self.batch_size = batch_size
+        self.seq_len = seq_len
+        self.vocab_size = vocab_size
+        self.device = device
+        self.dtype = dtype
+
+    def __iter__(self) -> Iterator[Tuple[Dict[str, torch.Tensor], torch.Tensor]]:
+        while True:
+            inputs = torch.randint(
+                low=0,
+                high=self.vocab_size,
+                size=(self.seq_len,),
+                device=self.device,
+                dtype=self.dtype,
+            )
+            labels = torch.randint(
+                low=0,
+                high=self.vocab_size,
+                size=(self.seq_len,),
+                device=self.device,
+                dtype=self.dtype,
+            )
+            yield {"input": inputs}, labels
+
 
 def _load_c4_dataset(dataset_path: str, split: str):
     """Load C4 dataset with default configuration."""
@@ -185,7 +218,35 @@ def build_hf_dataloader(
     dataset_path = job_config.training.dataset_path
     batch_size = job_config.training.local_batch_size
     seq_len = job_config.training.seq_len
-
+    # If synthetic_data is True, return a synthetic dataloader:
+    device = torch.device(f"cuda:{dp_rank}") if torch.cuda.is_available() else torch.device("cpu")
+    if getattr(job_config.training, "synthetic_data", False):
+        # Use tokenizer.vocab_size if available; otherwise fallback
+        if tokenizer is not None and hasattr(tokenizer, "vocab_size"):
+            vocab_sz = tokenizer.vocab_size
+        else:
+            vocab_sz = getattr(job_config.model, "vocab_size", 30000)
+
+        synthetic_ds = SyntheticIterableDataset(
+            seq_len=seq_len,
+            batch_size=batch_size,
+            vocab_size=vocab_sz,
+            device=device,
+            dtype=torch.long,
+        )
+        logger.warning(
+            f"Using SYNTHETIC data → batch_size={batch_size}, seq_len={seq_len}, vocab_size={vocab_sz}"
+        )
+        return ParallelAwareDataloader(
+            dataset=synthetic_ds,
+            dp_rank=dp_rank,
+            dp_world_size=dp_world_size,
+            batch_size=batch_size,
+        )
+        
+    # Otherwise, build the normal HuggingFaceDataset pipeline:
+    dataset_name = job_config.training.dataset
+    dataset_path = job_config.training.dataset_path
     hf_ds = HuggingFaceDataset(
         dataset_name=dataset_name,
         dataset_path=dataset_path,