diff --git a/src/sagemaker/remote_function/client.py b/src/sagemaker/remote_function/client.py
index 90729da8e1..1785f15892 100644
--- a/src/sagemaker/remote_function/client.py
+++ b/src/sagemaker/remote_function/client.py
@@ -185,6 +185,7 @@ def remote(
           methods that are not available via PyPI or conda. Default value is ``False``.
 
         instance_count (int): The number of instances to use. Defaults to 1.
+          NOTE: Remote function does not support instance_count > 1
 
         instance_type (str): The Amazon Elastic Compute Cloud (EC2) instance type to use to run
           the SageMaker job. e.g. ml.c4.xlarge. If not provided, a ValueError is thrown.
@@ -255,6 +256,12 @@ def _remote(func):
         @functools.wraps(func)
         def wrapper(*args, **kwargs):
 
+            if instance_count > 1:
+                raise ValueError(
+                    "Remote function do not support training on multi instances. "
+                    + "Please provide instance_count = 1"
+                )
+
             RemoteExecutor._validate_submit_args(func, *args, **kwargs)
 
             job_settings = _JobSettings(
@@ -574,6 +581,7 @@ def __init__(
               and methods that are not available via PyPI or conda. Default value is ``False``.
 
             instance_count (int): The number of instances to use. Defaults to 1.
+              NOTE: Remote function does not support instance_count > 1
 
             instance_type (str): The Amazon Elastic Compute Cloud (EC2) instance type to use to run
               the SageMaker job. e.g. ml.c4.xlarge. If not provided, a ValueError is thrown.
@@ -647,6 +655,12 @@ def __init__(
         if self.max_parallel_jobs <= 0:
             raise ValueError("max_parallel_jobs must be greater than 0.")
 
+        if instance_count > 1:
+            raise ValueError(
+                "Remote function do not support training on multi instances. "
+                + "Please provide instance_count = 1"
+            )
+
         self.job_settings = _JobSettings(
             dependencies=dependencies,
             pre_execution_commands=pre_execution_commands,
diff --git a/tests/unit/sagemaker/remote_function/test_client.py b/tests/unit/sagemaker/remote_function/test_client.py
index f8cd528505..fede42dab1 100644
--- a/tests/unit/sagemaker/remote_function/test_client.py
+++ b/tests/unit/sagemaker/remote_function/test_client.py
@@ -373,6 +373,17 @@ def square(x):
         square(5)
 
 
+def test_decorator_instance_count_greater_than_one():
+    @remote(image_uri=IMAGE, s3_root_uri=S3_URI, instance_count=2)
+    def square(x):
+        return x * x
+
+    with pytest.raises(
+        ValueError, match=r"Remote function do not support training on multi instances."
+    ):
+        square(5)
+
+
 @patch("sagemaker.remote_function.client._JobSettings")
 @patch("sagemaker.remote_function.client._Job.start")
 def test_decorator_underlying_job_timed_out(mock_start, mock_job_settings):
@@ -626,6 +637,14 @@ def test_executor_fails_to_start_job(mock_start, *args):
     assert future_2.done()
 
 
+def test_executor_instance_count_greater_than_one():
+    with pytest.raises(
+        ValueError, match=r"Remote function do not support training on multi instances."
+    ):
+        with RemoteExecutor(max_parallel_jobs=1, s3_root_uri="s3://bucket/", instance_count=2) as e:
+            e.submit(job_function, 1, 2, c=3, d=4)
+
+
 @patch("sagemaker.remote_function.client._API_CALL_LIMIT", new=API_CALL_LIMIT)
 @patch("sagemaker.remote_function.client._JobSettings")
 @patch("sagemaker.remote_function.client._Job.start")