diff --git a/dimos/models/segmentation/edge_tam.py b/dimos/models/segmentation/edge_tam.py
index 88ec707b1c..e706b4c6d8 100644
--- a/dimos/models/segmentation/edge_tam.py
+++ b/dimos/models/segmentation/edge_tam.py
@@ -53,6 +53,7 @@ class EdgeTAMProcessor(Detector):
     _frame_count: int
     _is_tracking: bool
     _buffer_size: int
+    _device: str
 
     def __init__(
         self,
@@ -62,8 +63,14 @@ def __init__(
         if not local_config_path.exists():
             raise FileNotFoundError(f"EdgeTAM config not found at {local_config_path}")
 
-        if not torch.cuda.is_available():
-            raise RuntimeError("EdgeTAM requires a CUDA-capable GPU")
+        if torch.cuda.is_available():
+            self._device = "cuda"
+        elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+            self._device = "mps"
+        else:
+            raise RuntimeError(
+                "EdgeTAM requires a CUDA-capable GPU or an Apple Silicon MPS/Metal backend."
+            )
 
         cfg = OmegaConf.load(local_config_path)
 
@@ -98,7 +105,7 @@ def __init__(
         if unexpected_keys:
             raise RuntimeError("Unexpected keys in checkpoint")
 
-        self._predictor = self._predictor.to("cuda")
+        self._predictor = self._predictor.to(self._device)
         self._predictor.eval()
 
         self._inference_state = None
@@ -124,7 +131,7 @@ def _prepare_frame(self, image: Image) -> torch.Tensor:
         img_np /= img_std
 
         img_tensor = torch.from_numpy(img_np).permute(2, 0, 1).float()
-        img_tensor = img_tensor.cuda()
+        img_tensor = img_tensor.to(self._device)
 
         return img_tensor