diff --git a/examples/autoround/ddp/ddp_qwen3_example.py b/examples/autoround/ddp/ddp_qwen3_example.py
new file mode 100644
index 0000000000..f7ec5b8756
--- /dev/null
+++ b/examples/autoround/ddp/ddp_qwen3_example.py
@@ -0,0 +1,156 @@
+"""
+This script is adapted to use DDP functionality with AutoRound.
+run this with `torchrun --nproc_per_node=2 ddp_qwen3_example.py`
+or change nproc_per_node to your desired configuration
+
+Example usage:
+torchrun --nproc_per_node=2 ddp_qwen3_example.py \
+    --model Qwen/Qwen3-8B \
+    --nsamples 128 \
+    --iters 100 \
+    --disable_torch_compile \
+    --deterministic
+"""
+import argparse
+import os
+
+import torch
+import torch.distributed as dist
+from compressed_tensors.offload import dispatch_model, init_dist, load_offloaded_model
+from loguru import logger
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+
+
+def fix_everything(seed=42):
+    import random
+
+    import numpy as np
+
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+
+
+def config_deterministic():
+    torch.use_deterministic_algorithms(True, warn_only=False)
+    os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
+    fix_everything()
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="AutoRound Quantization with DDP support"
+    )
+    parser.add_argument(
+        "--model",
+        type=str,
+        default="Qwen/Qwen3-8B",
+        help="Model name or path",
+    )
+    parser.add_argument(
+        "--scheme",
+        type=str,
+        default="W4A16",
+        help="Quantization scheme (W4A16, MXFP8, MXFP4, etc.)",
+    )
+    parser.add_argument("--iters", type=int, default=200, help="Number of iterations")
+    parser.add_argument("--nsamples", type=int, default=128, help="Number of samples")
+    parser.add_argument(
+        "--disable_torch_compile",
+        action="store_true",
+        help="Disable torch.compile for model acceleration during quantization",
+    )
+    parser.add_argument(
+        "--deterministic",
+        action="store_true",
+        help="Enable deterministic mode for reproducibility",
+    )
+    args = parser.parse_args()
+
+if args.deterministic:
+    config_deterministic()
+
+model_id = args.model
+
+###### DDP MODEL LOAD CHANGE #####
+init_dist()
+with load_offloaded_model():
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id, dtype="auto", device_map="auto_offload"
+    )
+##################################
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+# Select calibration dataset.
+NUM_CALIBRATION_SAMPLES = args.nsamples
+MAX_SEQUENCE_LENGTH = 2048
+ITERS = args.iters
+
+
+# Get aligned calibration dataset.
+from auto_round.calib_dataset import get_dataset  # noqa: E402
+
+# Note: Make sure model are loaded before importing auto-round related code.
+# This requirement will be lifted once switching to new release of auto-round which
+# includes below fix:
+from llmcompressor.modifiers.autoround import AutoRoundModifier  # noqa: E402
+
+ds = get_dataset(
+    tokenizer=tokenizer,
+    seqlen=MAX_SEQUENCE_LENGTH,
+    nsamples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Configure the quantization algorithm to run.
+#   * quantize the weights to 4 bit with AutoRound with a group size 128
+recipe = AutoRoundModifier(
+    targets="Linear",
+    scheme=args.scheme,
+    ignore=[
+        "lm_head",
+        "re:.*mlp.gate$",
+    ],
+    iters=ITERS,
+    enable_torch_compile=not args.disable_torch_compile,
+)
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    shuffle_calibration_samples=False,
+)
+
+rank = dist.get_rank()
+logger.info(f"[Rank {rank}] Quantization completed")
+# Confirm generations of the quantized model look sane.
+logger.info("\n\n")
+logger.info("========== SAMPLE GENERATION ==============")
+dispatch_model(model)
+sample = tokenizer("Hello my name is", return_tensors="pt")
+sample = {key: value.to(model.device) for key, value in sample.items()}
+output = model.generate(**sample, max_new_tokens=100)
+logger.info(tokenizer.decode(output[0]))
+logger.info("==========================================\n\n")
+
+logger.info("Saving...")
+# Save to disk compressed.
+SAVE_DIR = (
+    model_id.rstrip("/").split("/")[-1]
+    + f"-{args.scheme}-AutoRound"
+    + f"-iters{args.iters}-nsamples{args.nsamples}"
+    + "-DDP"
+    + str(dist.get_world_size())
+)
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
+logger.info(f"Saved to {SAVE_DIR}")
+
+dist.destroy_process_group()
diff --git a/src/llmcompressor/modifiers/autoround/base.py b/src/llmcompressor/modifiers/autoround/base.py
index 9d4d5e1f38..bcfe36b783 100644
--- a/src/llmcompressor/modifiers/autoround/base.py
+++ b/src/llmcompressor/modifiers/autoround/base.py
@@ -280,6 +280,7 @@ def apply_autoround(self, state, subgraph):
             align_module_device(decoding_layer),
             suspend_offloading(wrapped_model),
         ):
+            self._update_device_map_for_dp(kwargs)
             ar = AutoRound(
                 model=wrapped_model,
                 **kwargs,
@@ -349,6 +350,13 @@ def get_unquantized_layer_names(self, wrapped_model: torch.nn.Module) -> List[st
                 unquantized_layers.append(name)
         return unquantized_layers
 
+    def _update_device_map_for_dp(self, ar_kwargs):
+        if torch.distributed.is_initialized():
+            rank = torch.distributed.get_rank()
+            ar_kwargs["device_map"] = (
+                f"cuda:{rank}" if torch.cuda.is_available() else "cpu"
+            )
+
     def _unwrapper_quantized_layer(self, model: torch.nn.Module):
         # auto-round will return WrapperWALayer if activation is quantized
         for name, module in model.named_modules():