diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/quantize.py b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/quantize.py
index 5105b39855e..b0d27b026cf 100644
--- a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/quantize.py
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/quantize.py
@@ -181,6 +181,8 @@ def get_accuracy(model_name_or_path, tokenizer=None, eval_tasks="mmlu", limit=No
     parser.add_argument("--iters", default=200, type=int, help="iters for autoround.")
     parser.add_argument("--seqlen", default=2048, type=int, help="sequence length for autoround.")
     parser.add_argument("--nsamples", default=128, type=int, help="number of samples for autoround.")
+    parser.add_argument("--batch_size", default=8, type=int, help="batch size for autoround.")
+    parser.add_argument("--gradient_accumulate_steps", default=1, type=int, help="number of gradient accumulation steps for autoround.")
     parser.add_argument("--save", action="store_true", help="whether to save the quantized model")
     parser.add_argument("--export_path", type=str, default="saved_results", help="path to save the quantized model")
     parser.add_argument("--export_format", type=str, default="auto_round", help="format to save the quantized model")
@@ -267,6 +269,7 @@ def load_recipe_results(file_path):
         # preprocess
         if isinstance(args.target_bits, list) and len(args.target_bits) == 1:
             args.target_bits = args.target_bits[0]
+        batch_size_to_accumulate = args.batch_size // args.gradient_accumulate_steps
         config = AutoRoundConfig(
             tokenizer=tokenizer,
             iters=args.iters,
@@ -284,6 +287,8 @@ def load_recipe_results(file_path):
             output_dir=args.export_path,
             device_map=args.device_map,
             layer_config=layer_config if (args.use_recipe or args.quant_lm_head) else None,
+            gradient_accumulate_steps=args.gradient_accumulate_steps,
+            batch_size=batch_size_to_accumulate,
         )
         if isinstance(args.target_bits, list) and len(args.target_bits) > 1:
             args.tune_tasks = args.tasks if args.tune_tasks is None else args.tune_tasks
diff --git a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/run_quant.sh b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/run_quant.sh
index e064465aee0..7ebf4ad2dad 100644
--- a/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/run_quant.sh
+++ b/examples/pytorch/nlp/huggingface_models/language-modeling/quantization/auto_round/llama3/run_quant.sh
@@ -5,6 +5,7 @@
 # Parse command line arguments
 KV_CACHE_DTYPE="auto"
 STATIC_ATTENTION_DTYPE="auto"
+GRADIENT_ACCUMULATE_STEPS=1
 while [[ $# -gt 0 ]]; do
     case $1 in
         --topology=*)
@@ -23,6 +24,10 @@ while [[ $# -gt 0 ]]; do
             OUTPUT_MODEL="${1#*=}"
             shift
             ;;
+        --gradient_accumulate_steps=*)
+            GRADIENT_ACCUMULATE_STEPS="${1#*=}"
+            shift
+            ;;
         --static_kv_dtype=*)
             KV_CACHE_DTYPE="${1#*=}"
             shift
@@ -133,7 +138,7 @@ case "$TOPOLOGY" in
                 ;;
             "mxfp4_mixed")
                 echo "Running Llama 3.3 70B MXFP4 (Mixed with MXFP8) quantization..."
-                CMD="python quantize.py --model_name_or_path \"$INPUT_MODEL\" $COMMON_ARGS --target_bits 5.8 --options \"MXFP4\" \"MXFP8\" --shared_layers \"k_proj\" \"v_proj\" \"q_proj\" --shared_layers \"gate_proj\" \"up_proj\" --export_path \"$OUTPUT_MODEL\""
+                CMD="python quantize.py --model_name_or_path \"$INPUT_MODEL\" $COMMON_ARGS --target_bits 5.8 --options \"MXFP4\" \"MXFP8\" --shared_layers \"k_proj\" \"v_proj\" \"q_proj\" --shared_layers \"gate_proj\" \"up_proj\" --gradient_accumulate_steps \"$GRADIENT_ACCUMULATE_STEPS\" --export_path \"$OUTPUT_MODEL\""
                 echo "Executing command: $CMD"
                 python quantize.py \
                     --model_name_or_path "$INPUT_MODEL" \
@@ -142,6 +147,7 @@ case "$TOPOLOGY" in
                     --options "MXFP4" "MXFP8" \
                     --shared_layers "k_proj" "v_proj" "q_proj" \
                     --shared_layers "gate_proj" "up_proj" \
+                    --gradient_accumulate_steps "$GRADIENT_ACCUMULATE_STEPS" \
                     --export_path "$OUTPUT_MODEL"
                 ;;
             *)