diff --git a/pyproject.toml b/pyproject.toml
index 6a19203f03..40c7f6bf2b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -97,7 +97,7 @@ export = [
     "onnx>=1.12.0", # ONNX export
     "coremltools>=7.0; platform_system != 'Windows' and python_version <= '3.11'", # CoreML supported on macOS and Linux
     "scikit-learn>=1.3.2; platform_system != 'Windows' and python_version <= '3.11'", # CoreML k-means quantization
-    "openvino>=2024.0.0", # OpenVINO export
+    "openvino>=2024.0.0,<2025.0.0", # OpenVINO export
     "tensorflow>=2.0.0", # TF bug https://github.com/ultralytics/ultralytics/issues/5161
     "tensorflowjs>=3.9.0", # TF.js export, automatically installs tensorflow
     "tensorstore>=0.1.63; platform_machine == 'aarch64' and python_version >= '3.9'", # for TF Raspberry Pi exports
diff --git a/ultralytics/engine/exporter.py b/ultralytics/engine/exporter.py
index 16111ea840..440a21650e 100644
--- a/ultralytics/engine/exporter.py
+++ b/ultralytics/engine/exporter.py
@@ -570,7 +570,7 @@ class Exporter:
     @try_export
     def export_openvino(self, prefix=colorstr("OpenVINO:")):
         """YOLO OpenVINO export."""
-        check_requirements("openvino>=2024.5.0")
+        check_requirements("openvino>=2024.0.0,<2025.0.0")
         import openvino as ov
 
         LOGGER.info(f"\n{prefix} starting export with openvino {ov.__version__}...")
@@ -592,7 +592,7 @@ class Exporter:
             if self.model.task != "classify":
                 ov_model.set_rt_info("fit_to_window_letterbox", ["model_info", "resize_type"])
 
-            ov.runtime.save_model(ov_model, file, compress_to_fp16=self.args.half)
+            ov.save_model(ov_model, file, compress_to_fp16=self.args.half)
             yaml_save(Path(file).parent / "metadata.yaml", self.metadata)  # add metadata.yaml
 
         if self.args.int8:
diff --git a/ultralytics/nn/autobackend.py b/ultralytics/nn/autobackend.py
index 7629fb7ca8..89744ee46d 100644
--- a/ultralytics/nn/autobackend.py
+++ b/ultralytics/nn/autobackend.py
@@ -244,7 +244,7 @@ class AutoBackend(nn.Module):
         # OpenVINO
         elif xml:
             LOGGER.info(f"Loading {w} for OpenVINO inference...")
-            check_requirements("openvino>=2024.0.0")
+            check_requirements("openvino>=2024.0.0,<2025.0.0")
             import openvino as ov
 
             core = ov.Core()
@@ -600,7 +600,7 @@ class AutoBackend(nn.Module):
                     results[userdata] = request.results
 
                 # Create AsyncInferQueue, set the callback and start asynchronous inference for each input image
-                async_queue = self.ov.runtime.AsyncInferQueue(self.ov_compiled_model)
+                async_queue = self.ov.AsyncInferQueue(self.ov_compiled_model)
                 async_queue.set_callback(callback)
                 for i in range(n):
                     # Start async inference with userdata=i to specify the position in results list