diff --git a/frigate/embeddings/onnx/face_embedding.py b/frigate/embeddings/onnx/face_embedding.py
index 10d5627d9..da3f468d4 100644
--- a/frigate/embeddings/onnx/face_embedding.py
+++ b/frigate/embeddings/onnx/face_embedding.py
@@ -6,12 +6,12 @@ import os
 import numpy as np
 
 from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.base_runner import get_optimized_runner
 from frigate.log import redirect_output_to_logger
 from frigate.util.downloader import ModelDownloader
 
 from ...config import FaceRecognitionConfig
 from .base_embedding import BaseEmbedding
-from .runner import ONNXModelRunner
 
 try:
     from tflite_runtime.interpreter import Interpreter
@@ -148,7 +148,7 @@ class ArcfaceEmbedding(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 device=self.config.device or "GPU",
             )
diff --git a/frigate/embeddings/onnx/jina_v1_embedding.py b/frigate/embeddings/onnx/jina_v1_embedding.py
index d327fa8ba..51c075aa3 100644
--- a/frigate/embeddings/onnx/jina_v1_embedding.py
+++ b/frigate/embeddings/onnx/jina_v1_embedding.py
@@ -7,6 +7,7 @@ import warnings
 # importing this without pytorch or others causes a warning
 # https://github.com/huggingface/transformers/issues/27214
 # suppressed by setting env TRANSFORMERS_NO_ADVISORY_WARNINGS=1
+from frigate.detectors.base_runner import BaseModelRunner, get_optimized_runner
 from transformers import AutoFeatureExtractor, AutoTokenizer
 from transformers.utils.logging import disable_progress_bar
 
@@ -16,7 +17,6 @@ from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
 
 from .base_embedding import BaseEmbedding
-from .runner import ONNXModelRunner
 
 warnings.filterwarnings(
     "ignore",
@@ -125,7 +125,7 @@ class JinaV1TextEmbedding(BaseEmbedding):
                 clean_up_tokenization_spaces=True,
             )
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
             )
@@ -170,7 +170,7 @@ class JinaV1ImageEmbedding(BaseEmbedding):
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
         self.feature_extractor = None
-        self.runner: ONNXModelRunner | None = None
+        self.runner: BaseModelRunner | None = None
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -203,7 +203,7 @@ class JinaV1ImageEmbedding(BaseEmbedding):
                 f"{MODEL_CACHE_DIR}/{self.model_name}",
             )
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
             )
diff --git a/frigate/embeddings/onnx/jina_v2_embedding.py b/frigate/embeddings/onnx/jina_v2_embedding.py
index 50b503d76..6077b2faa 100644
--- a/frigate/embeddings/onnx/jina_v2_embedding.py
+++ b/frigate/embeddings/onnx/jina_v2_embedding.py
@@ -6,6 +6,7 @@ import os
 
 import numpy as np
 from PIL import Image
+from frigate.detectors.base_runner import get_optimized_runner
 from transformers import AutoTokenizer
 from transformers.utils.logging import disable_progress_bar, set_verbosity_error
 
@@ -15,7 +16,6 @@ from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
 
 from .base_embedding import BaseEmbedding
-from .runner import ONNXModelRunner
 
 # disables the progress bar and download logging for downloading tokenizers and image processors
 disable_progress_bar()
@@ -125,7 +125,7 @@ class JinaV2Embedding(BaseEmbedding):
                 clean_up_tokenization_spaces=True,
             )
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
             )
diff --git a/frigate/embeddings/onnx/lpr_embedding.py b/frigate/embeddings/onnx/lpr_embedding.py
index 1b5b9acd0..88b0ae07b 100644
--- a/frigate/embeddings/onnx/lpr_embedding.py
+++ b/frigate/embeddings/onnx/lpr_embedding.py
@@ -7,11 +7,11 @@ import numpy as np
 
 from frigate.comms.inter_process import InterProcessRequestor
 from frigate.const import MODEL_CACHE_DIR
+from frigate.detectors.base_runner import BaseModelRunner, get_optimized_runner
 from frigate.types import ModelStatusTypesEnum
 from frigate.util.downloader import ModelDownloader
 
 from .base_embedding import BaseEmbedding
-from .runner import ONNXModelRunner
 
 warnings.filterwarnings(
     "ignore",
@@ -47,7 +47,7 @@ class PaddleOCRDetection(BaseEmbedding):
         self.model_size = model_size
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
-        self.runner: ONNXModelRunner | None = None
+        self.runner: BaseModelRunner | None = None
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -76,7 +76,7 @@ class PaddleOCRDetection(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
                 self.model_size,
@@ -107,7 +107,7 @@ class PaddleOCRClassification(BaseEmbedding):
         self.model_size = model_size
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
-        self.runner: ONNXModelRunner | None = None
+        self.runner: BaseModelRunner | None = None
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -136,7 +136,7 @@ class PaddleOCRClassification(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
                 self.model_size,
@@ -168,7 +168,7 @@ class PaddleOCRRecognition(BaseEmbedding):
         self.model_size = model_size
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
-        self.runner: ONNXModelRunner | None = None
+        self.runner: BaseModelRunner | None = None
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -197,7 +197,7 @@ class PaddleOCRRecognition(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
                 self.model_size,
@@ -229,7 +229,7 @@ class LicensePlateDetector(BaseEmbedding):
         self.model_size = model_size
         self.device = device
         self.download_path = os.path.join(MODEL_CACHE_DIR, self.model_name)
-        self.runner: ONNXModelRunner | None = None
+        self.runner: BaseModelRunner | None = None
         files_names = list(self.download_urls.keys())
         if not all(
             os.path.exists(os.path.join(self.download_path, n)) for n in files_names
@@ -258,7 +258,7 @@ class LicensePlateDetector(BaseEmbedding):
             if self.downloader:
                 self.downloader.wait_for_download()
 
-            self.runner = ONNXModelRunner(
+            self.runner = get_optimized_runner(
                 os.path.join(self.download_path, self.model_file),
                 self.device,
                 self.model_size,