diff --git a/vllm_mlx/api/utils.py b/vllm_mlx/api/utils.py
index 795ee39d..a1279fc1 100644
--- a/vllm_mlx/api/utils.py
+++ b/vllm_mlx/api/utils.py
@@ -59,7 +59,7 @@ def clean_output_text(text: str) -> str:
     "llava", "LLaVA",                   # LLaVA models
     "idefics", "Idefics",               # Idefics models
     "paligemma", "PaliGemma",           # PaliGemma
-    "gemma-3", "gemma3",                # Gemma 3 (multimodal)
+    "gemma-3", "gemma3",                # Gemma 3 (multimodal) - FIXED in mlx_vlm/models/gemma3/language.py
     "pixtral", "Pixtral",               # Pixtral
     "molmo", "Molmo",                   # Molmo
     "phi3-vision", "phi-3-vision",      # Phi-3 Vision
diff --git a/vllm_mlx/engine/batched.py b/vllm_mlx/engine/batched.py
index 3aeda361..c63410fb 100644
--- a/vllm_mlx/engine/batched.py
+++ b/vllm_mlx/engine/batched.py
@@ -24,25 +24,20 @@ class MLLMModelWrapper:
     but MLLM models return LanguageModelOutput objects. This wrapper extracts
     the logits from the output.
 
-    Additionally, some models like Gemma 3 require `pixel_values` as a required
-    positional argument, while others like Qwen2-VL make it optional. This wrapper
-    ensures `pixel_values=None` is passed for text-only requests.
+    Also handles Gemma 3's required pixel_values argument by injecting None
+    for text-only requests.
     """
 
     def __init__(self, model):
         self._model = model
+        # Detect if this is a Gemma 3 model (requires pixel_values as positional arg)
+        self._is_gemma3 = hasattr(model, 'model_type') and 'gemma3' in str(getattr(model, 'model_type', '')).lower()
 
     def __call__(self, *args, **kwargs):
-        """Call the model and extract logits from LanguageModelOutput.
-
-        Handles both models where pixel_values is optional (Qwen2-VL) and
-        models where it's required (Gemma 3) by ensuring pixel_values=None
-        is passed for text-only requests.
-        """
-        # For text-only requests, BatchGenerator calls model(input_ids, cache=cache)
-        # But Gemma 3 requires pixel_values as 2nd positional arg.
-        # Inject pixel_values=None if not provided and only input_ids is passed
-        if 'pixel_values' not in kwargs and len(args) == 1:
+        """Call the model and extract logits from LanguageModelOutput."""
+        # Gemma 3 requires pixel_values as a positional argument, unlike Qwen
+        # which makes it optional. Inject pixel_values=None for text-only requests.
+        if self._is_gemma3 and 'pixel_values' not in kwargs:
             kwargs['pixel_values'] = None
 
         output = self._model(*args, **kwargs)
@@ -113,6 +108,18 @@ async def start(self) -> None:
 
         from ..engine_core import EngineConfig, AsyncEngineCore
         from ..scheduler import SchedulerConfig
+        import os
+
+        # Auto-configure Gemma 3 for continuous batching compatibility
+        # Gemma 3's RotatingKVCache with sliding_window causes garbled output
+        # in batch mode due to offset tracking corruption. Force full KVCache.
+        if self._is_mllm and ("gemma-3" in self._model_name.lower() or "gemma3" in self._model_name.lower()):
+            if os.environ.get("GEMMA3_SLIDING_WINDOW") is None:
+                os.environ["GEMMA3_SLIDING_WINDOW"] = "0"
+                logger.info(
+                    "Auto-set GEMMA3_SLIDING_WINDOW=0 for continuous batching compatibility. "
+                    "This uses full KVCache for all layers (~35GB at 50K tokens)."
+                )
 
         # Load model and tokenizer
         if self._is_mllm: