diff --git a/AI_Web_Scraper/ai_agent.py b/AI_Web_Scraper/ai_agent.py
index efa6952..a952882 100644
--- a/AI_Web_Scraper/ai_agent.py
+++ b/AI_Web_Scraper/ai_agent.py
@@ -106,26 +106,105 @@ class AIAgent:
                 print("bitsandbytes/transformers 호환 문제로 양자화를 비활성화합니다.")
                 quant_args = {}
 
-        # 1차 시도: device_map="auto" 로 로드
+        # 메모리 제한/오프로딩 설정
+        mm_cfg = model_settings.get('max_memory', {}) if isinstance(model_settings.get('max_memory', {}), dict) else {}
+        # normalize memory strings to GiB (accelerate accepts both, but unify)
+        def _norm_mem(v):
+            if not isinstance(v, str):
+                return v
+            return v.replace('GB', 'GiB').replace('gb', 'GiB')
+        max_memory = {}
+        if 0 in mm_cfg or 'gpu' in mm_cfg:
+            max_memory[0] = _norm_mem(mm_cfg.get(0, mm_cfg.get('gpu', '30GiB')))
+        if 'cpu' in mm_cfg:
+            max_memory['cpu'] = _norm_mem(mm_cfg.get('cpu', '60GiB'))
+        offload_folder = os.path.join(os.path.dirname(self.config_path), 'offload')
+        os.makedirs(offload_folder, exist_ok=True)
+
+        # 1차 시도: device_map="auto" + max_memory 로 로드
         try:
-            self.tokenizer = AutoTokenizer.from_pretrained(model_source)
+            self.tokenizer = AutoTokenizer.from_pretrained(model_source, trust_remote_code=True)
+            load_kwargs = dict(
+                device_map="auto",
+                low_cpu_mem_usage=True,
+                offload_folder=offload_folder,
+                offload_state_dict=True,
+                trust_remote_code=True,
+            )
+            if dtype is not None:
+                load_kwargs["torch_dtype"] = dtype
+            if max_memory:
+                load_kwargs["max_memory"] = max_memory
+
+            # use_quantization=True면 8bit 우선 시도 (repo의 다른 양자화 경로 우회)
+            if use_quantization:
+                try:
+                    from transformers import BitsAndBytesConfig
+                    load_kwargs["quantization_config"] = BitsAndBytesConfig(
+                        load_in_8bit=True,
+                        llm_int8_enable_fp32_cpu_offload=True
+                    )
+                    print("8bit 양자화 적용 (1차 시도)")
+                except Exception as _:
+                    # bitsandbytes 사용 불가 시 양자화 미적용으로 진행
+                    print("bitsandbytes 사용 불가: 비양자화로 1차 시도 진행")
+
             self.model = AutoModelForCausalLM.from_pretrained(
                 model_source,
-                device_map="auto",
-                torch_dtype=dtype if dtype is not None else None,
-                low_cpu_mem_usage=True,
-                **quant_args
+                **load_kwargs
             )
         except Exception as e1:
-            print(f"device_map=auto 로드 실패: {e1}\nCPU로 폴백합니다.")
-            # 2차 시도: CPU 강제 로드
-            self.tokenizer = AutoTokenizer.from_pretrained(model_source)
-            self.model = AutoModelForCausalLM.from_pretrained(
-                model_source,
-                device_map={"": "cpu"},
-                torch_dtype=torch.float32,
-                low_cpu_mem_usage=False
-            )
+            print(f"device_map=auto 로드 실패: {e1}")
+            # 2차 시도: 8-bit 양자화로 재시도 (가능 시, 1차에서 적용 안된 경우)
+            tried_int8 = False
+            if not use_quantization:
+                try:
+                    from transformers import BitsAndBytesConfig
+                    print("8bit 양자화로 재시도합니다...")
+                    self.tokenizer = AutoTokenizer.from_pretrained(model_source, trust_remote_code=True)
+                    retry_kwargs = dict(
+                        device_map="auto",
+                        low_cpu_mem_usage=True,
+                        offload_folder=offload_folder,
+                        offload_state_dict=True,
+                        trust_remote_code=True,
+                        quantization_config=BitsAndBytesConfig(
+                            load_in_8bit=True,
+                            llm_int8_enable_fp32_cpu_offload=True
+                        )
+                    )
+                    if dtype is not None:
+                        retry_kwargs["torch_dtype"] = dtype
+                    if max_memory:
+                        retry_kwargs["max_memory"] = max_memory
+
+                    self.model = AutoModelForCausalLM.from_pretrained(
+                        model_source,
+                        **retry_kwargs
+                    )
+                    tried_int8 = True
+                except Exception as e_int8:
+                    print(f"8bit 재시도 실패: {e_int8}")
+
+            if not tried_int8:
+                print("CPU로 폴백합니다.")
+                try:
+                    import torch, gc
+                    torch.cuda.empty_cache()
+                    gc.collect()
+                except Exception:
+                    pass
+
+                # CPU 강제 로드
+                self.tokenizer = AutoTokenizer.from_pretrained(model_source)
+                self.model = AutoModelForCausalLM.from_pretrained(
+                    model_source,
+                    device_map={"": "cpu"},
+                    torch_dtype=torch.float32,
+                    low_cpu_mem_usage=False,
+                    quantization_config=None,
+                    trust_remote_code=True
+                )
 
         # 파이프라인 생성
         pad_id = self.tokenizer.eos_token_id if getattr(self.tokenizer, 'eos_token_id', None) is not None else None
diff --git a/AI_Web_Scraper/config.json b/AI_Web_Scraper/config.json
index 3dc8347..4f597bf 100644
--- a/AI_Web_Scraper/config.json
+++ b/AI_Web_Scraper/config.json
@@ -16,7 +16,7 @@
     "drive_mount_path": "/content/drive/MyDrive/model_Dev/data"
   },
   "model_settings": {
-    "use_quantization": false,
+    "use_quantization": true,
     "quantization_bits": 8,
     "torch_dtype": "auto",
     "max_memory": {