diff --git a/vibevoice_nodes.py b/vibevoice_nodes.py
index 1b75e2d..0f2fe33 100644
--- a/vibevoice_nodes.py
+++ b/vibevoice_nodes.py
@@ -14,7 +14,7 @@ import comfy.model_patcher
 from comfy.utils import ProgressBar
 from comfy.model_management import throw_exception_if_processing_interrupted
 
-from transformers import set_seed, AutoTokenizer
+from transformers import set_seed, AutoTokenizer, BitsAndBytesConfig
 from .vibevoice.modular.modeling_vibevoice_inference import VibeVoiceForConditionalGenerationInference
 from .vibevoice.processor.vibevoice_processor import VibeVoiceProcessor
 from .vibevoice.processor.vibevoice_tokenizer_processor import VibeVoiceTokenizerProcessor
@@ -80,17 +80,18 @@ def cleanup_old_models(keep_cache_key=None):
 
 class VibeVoiceModelHandler(torch.nn.Module):
     """A torch.nn.Module wrapper to hold the VibeVoice model and processor."""
-    def __init__(self, model_pack_name, attention_mode="eager"):
+    def __init__(self, model_pack_name, attention_mode="eager", use_llm_4bit=False):
         super().__init__()
         self.model_pack_name = model_pack_name
         self.attention_mode = attention_mode
+        self.use_llm_4bit = use_llm_4bit
         self.cache_key = f"{model_pack_name}_attn_{attention_mode}"
         self.model = None
         self.processor = None
         self.size = int(MODEL_CONFIGS[model_pack_name].get("size_gb", 4.0) * (1024**3))
 
     def load_model(self, device, attention_mode="eager"):
-        self.model, self.processor = VibeVoiceLoader.load_model(self.model_pack_name, device , attention_mode)
+        self.model, self.processor = VibeVoiceLoader.load_model(self.model_pack_name, device, attention_mode, use_llm_4bit=self.use_llm_4bit)
         self.model.to(device)
 
 class VibeVoicePatcher(comfy.model_patcher.ModelPatcher):
@@ -180,11 +181,13 @@ class VibeVoiceLoader:
         return attention_mode
 
     @staticmethod
-    def load_model(model_name: str, device, attention_mode: str = "eager"):
+    def load_model(model_name: str, device, attention_mode: str = "eager", use_llm_4bit: bool = False):
         # Validate attention mode
         if attention_mode not in ATTENTION_MODES:
             logger.warning(f"Unknown attention mode '{attention_mode}', falling back to eager")
             attention_mode = "eager"
+            if use_llm_4bit and attention_mode == "flash_attention_2":
+                attention_mode = "sdpa"
         
         # Create cache key that includes attention mode
         cache_key = f"{model_name}_attn_{attention_mode}"
@@ -214,6 +217,16 @@ class VibeVoiceLoader:
         final_attention_mode = VibeVoiceLoader._check_attention_compatibility(
             attention_mode, torch_dtype, device_name
         )
+
+        # Build optional 4-bit config (LLM only)
+        quant_config = None
+        if use_llm_4bit:
+            quant_config = BitsAndBytesConfig(
+                load_in_4bit=True,
+                bnb_4bit_quant_type="nf4",
+                bnb_4bit_use_double_quant=True,
+                bnb_4bit_compute_dtype=torch.bfloat16,
+            )
         
         logger.info(f"Requested attention mode: {attention_mode}")
         if final_attention_mode != attention_mode:
@@ -253,11 +266,13 @@ class VibeVoiceLoader:
         try:
             model = VibeVoiceForConditionalGenerationInference.from_pretrained(
                 model_path,
-                torch_dtype=torch_dtype,
+                torch_dtype=torch.bfloat16 if quant_config else torch_dtype,
                 attn_implementation=final_attention_mode,
-                device_map=device
+                device_map="auto" if quant_config else device,
+                quantization_config=quant_config,   # <- forwarded if supported
             )
             model.eval()
+            setattr(model, "_llm_4bit", bool(quant_config))
             
             # Store with the actual attention mode used (not the requested one)
             LOADED_MODELS[cache_key] = (model, processor)
@@ -381,6 +396,10 @@ class VibeVoiceTTSNode:
                     "default": "Speaker 1: Hello from ComfyUI!\nSpeaker 2: VibeVoice sounds amazing.",
                     "tooltip": "The script for the conversation. Use 'Speaker 1:', 'Speaker 2:', etc. to assign lines to different voices. Each speaker line should be on a new line."
                 }),
+                "quantize_llm_4bit": ("BOOLEAN", {
+                    "default": False, "label_on": "Q4 (LLM only)", "label_off": "Full precision",
+                    "tooltip": "Quantize the Qwen2.5 LLM to 4-bit NF4 via bitsandbytes. Diffusion head stays BF16/FP32."
+                }),
                 "attention_mode": (["eager", "sdpa", "flash_attention_2"], {
                     "default": "sdpa",
                     "tooltip": "Attention implementation: Eager (safest), SDPA (balanced), Flash Attention 2 (fastest but may cause issues on some GPUs like RTX 5090)"
@@ -426,20 +445,20 @@ class VibeVoiceTTSNode:
     FUNCTION = "generate_audio"
     CATEGORY = "audio/tts"
 
-    def generate_audio(self, model_name, text, attention_mode, cfg_scale, inference_steps, seed, do_sample, temperature, top_p, top_k, **kwargs):
+    def generate_audio(self, model_name, text, attention_mode, cfg_scale, inference_steps, seed, do_sample, temperature, top_p, top_k, quantize_llm_4bit, **kwargs):
         if not text.strip():
             logger.warning("VibeVoiceTTS: Empty text provided, returning silent audio.")
             return ({"waveform": torch.zeros((1, 1, 24000), dtype=torch.float32), "sample_rate": 24000},)
 
         # Create cache key that includes attention mode
-        cache_key = f"{model_name}_attn_{attention_mode}"
+        cache_key = f"{model_name}_attn_{attention_mode}_q4_{int(quantize_llm_4bit)}"
         
         # Clean up old models when switching to a different model
         if cache_key not in VIBEVOICE_PATCHER_CACHE:
             # Only keep models that are currently being requested
             cleanup_old_models(keep_cache_key=cache_key)
             
-            model_handler = VibeVoiceModelHandler(model_name, attention_mode)
+            model_handler = VibeVoiceModelHandler(model_name, attention_mode, use_llm_4bit=quantize_llm_4bit)
             patcher = VibeVoicePatcher(
                 model_handler,
                 attention_mode=attention_mode,