diff --git a/CHANGES.md b/CHANGES.md
index 44e7568cb8ee..8f5530831266 100644
--- a/CHANGES.md
+++ b/CHANGES.md
@@ -80,6 +80,7 @@
 
 ## Bugfixes
 
+* (Python) RunInference now measures model memory (the `model_byte_size` metric) when `torch` is not installed. It was always 0, because a failed `ModelManager` import also disabled the `resource` module ([#40854](https://github.com/apache/beam/issues/40854)).
 * Fixed X (Java/Python) ([#X](https://github.com/apache/beam/issues/X)).
 
 ## Security Fixes
diff --git a/sdks/python/apache_beam/ml/inference/base.py b/sdks/python/apache_beam/ml/inference/base.py
index 5ee65afa0615..4d5eb4f74c0a 100644
--- a/sdks/python/apache_beam/ml/inference/base.py
+++ b/sdks/python/apache_beam/ml/inference/base.py
@@ -68,10 +68,13 @@
 try:
   # pylint: disable=wrong-import-order, wrong-import-position
   import resource
+except ImportError:
+  resource = None  # type: ignore[assignment]
 
+try:
+  # pylint: disable=wrong-import-order, wrong-import-position
   from apache_beam.ml.inference.model_manager import ModelManager
 except ImportError:
-  resource = None  # type: ignore[assignment]
   ModelManager = None  # type: ignore[assignment]
 
 _NANOSECOND_TO_MILLISECOND = 1_000_000
diff --git a/sdks/python/apache_beam/ml/inference/base_test.py b/sdks/python/apache_beam/ml/inference/base_test.py
index dec1c2906cd2..d4cfce8ec4f4 100644
--- a/sdks/python/apache_beam/ml/inference/base_test.py
+++ b/sdks/python/apache_beam/ml/inference/base_test.py
@@ -1340,6 +1340,24 @@ def test_timing_metrics(self):
     self.assertEqual(load_model_latency.result.count, 1)
     self.assertEqual(load_model_latency.result.mean, 500)
 
+  @unittest.skipIf(
+      sys.platform == 'win32',
+      'The resource module is not available on Windows.')
+  def test_no_resource_warning_when_model_manager_unavailable(self):
+    # ModelManager needs optional dependencies (e.g. torch). RunInference should
+    # still measure model memory with `resource` when ModelManager is missing.
+    with unittest.mock.patch.object(base, 'ModelManager', None), \
+        unittest.mock.patch.object(base.logging, 'warning') as mock_warning:
+      with TestPipeline() as pipeline:
+        _ = (
+            pipeline
+            | beam.Create([1, 5, 3, 10])
+            | base.RunInference(FakeModelHandler()))
+    messages = [str(call) for call in mock_warning.call_args_list]
+    self.assertFalse(
+        any('Resource module is not available' in m for m in messages),
+        messages)
+
   def test_forwards_batch_args(self):
     examples = list(range(100))
     with TestPipeline('FnApiRunner') as pipeline: