diff --git a/frigate/embeddings/functions/onnx.py b/frigate/embeddings/functions/onnx.py
index 396145380..ee6420201 100644
--- a/frigate/embeddings/functions/onnx.py
+++ b/frigate/embeddings/functions/onnx.py
@@ -18,6 +18,7 @@ from transformers.utils.logging import disable_progress_bar
 from frigate.const import MODEL_CACHE_DIR, UPDATE_MODEL_STATE
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
+from frigate.util.model import get_ort_providers
 
 warnings.filterwarnings(
     "ignore",
@@ -40,8 +41,8 @@ class GenericONNXEmbedding:
         download_urls: Dict[str, str],
         embedding_function: Callable[[List[np.ndarray]], np.ndarray],
         model_type: str,
-        preferred_providers: List[str] = ["CPUExecutionProvider"],
         tokenizer_file: Optional[str] = None,
+        force_cpu: bool = False,
     ):
         self.model_name = model_name
         self.model_file = model_file
@@ -49,7 +50,7 @@ class GenericONNXEmbedding:
         self.download_urls = download_urls
         self.embedding_function = embedding_function
         self.model_type = model_type  # 'text' or 'vision'
-        self.preferred_providers = preferred_providers
+        self.providers, self.provider_options = get_ort_providers(force_cpu=force_cpu)
 
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
         self.tokenizer = None
@@ -105,8 +106,7 @@ class GenericONNXEmbedding:
             else:
                 self.feature_extractor = self._load_feature_extractor()
             self.session = self._load_model(
-                os.path.join(self.download_path, self.model_file),
-                self.preferred_providers,
+                os.path.join(self.download_path, self.model_file)
             )
 
     def _load_tokenizer(self):
@@ -123,7 +123,7 @@ class GenericONNXEmbedding:
             f"{MODEL_CACHE_DIR}/{self.model_name}",
         )
 
-    def _load_model(self, path: str, providers: List[str]):
+    def _load_model(self, path: str):
         if os.path.exists(path):
             return ort.InferenceSession(path, providers=providers)
         else: