diff --git a/include/tvm/runtime/ndarray.h b/include/tvm/runtime/ndarray.h
index d530ef587782..b6a4cfe453c1 100644
--- a/include/tvm/runtime/ndarray.h
+++ b/include/tvm/runtime/ndarray.h
@@ -365,6 +365,15 @@ static inline bool IsContiguous(const DLTensor& arr) {
   int64_t expected_stride = 1;
   for (int32_t i = arr.ndim; i != 0; --i) {
     int32_t k = i - 1;
+    if (arr.shape[k] == 1) {
+      // Skip stride check if shape[k] is 1, where the dimension is contiguous
+      // regardless of the value of stride.
+      //
+      // For example, PyTorch will normalize stride to 1 if shape is 1 when exporting
+      // to DLPack.
+      // More context: https://github.com/pytorch/pytorch/pull/83158
+      continue;
+    }
     if (arr.strides[k] != expected_stride) return false;
     expected_stride *= arr.shape[k];
   }
diff --git a/tests/cpp/ndarray_test.cc b/tests/cpp/ndarray_test.cc
new file mode 100644
index 000000000000..cd5c75410aae
--- /dev/null
+++ b/tests/cpp/ndarray_test.cc
@@ -0,0 +1,73 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one
+ * or more contributor license agreements.  See the NOTICE file
+ * distributed with this work for additional information
+ * regarding copyright ownership.  The ASF licenses this file
+ * to you under the Apache License, Version 2.0 (the
+ * "License"); you may not use this file except in compliance
+ * with the License.  You may obtain a copy of the License at
+ *
+ *   http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing,
+ * software distributed under the License is distributed on an
+ * "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ * KIND, either express or implied.  See the License for the
+ * specific language governing permissions and limitations
+ * under the License.
+ */
+
+#include <dmlc/logging.h>
+#include <gtest/gtest.h>
+#include <tvm/runtime/ndarray.h>
+
+using namespace tvm;
+
+TEST(NDArrayTest, IsContiguous_ContiguousStride) {
+  auto array = runtime::NDArray::Empty({5, 10}, DataType::Float(32), {kDLCPU});
+  DLManagedTensor* managed_tensor = array.ToDLPack();
+
+  int64_t strides[] = {10, 1};
+  managed_tensor->dl_tensor.strides = strides;
+
+  ICHECK(runtime::IsContiguous(managed_tensor->dl_tensor));
+
+  managed_tensor->deleter(managed_tensor);
+}
+
+TEST(NDArrayTest, IsContiguous_NullStride) {
+  auto array = runtime::NDArray::Empty({5, 10}, DataType::Float(32), {kDLCPU});
+  DLManagedTensor* managed_tensor = array.ToDLPack();
+
+  managed_tensor->dl_tensor.strides = nullptr;
+
+  ICHECK(runtime::IsContiguous(managed_tensor->dl_tensor));
+
+  managed_tensor->deleter(managed_tensor);
+}
+
+TEST(NDArrayTest, IsContiguous_AnyStrideForSingular) {
+  auto array = runtime::NDArray::Empty({5, 1, 10}, DataType::Float(32), {kDLCPU});
+  DLManagedTensor* managed_tensor = array.ToDLPack();
+
+  int64_t strides[] = {10, 1, 1};  // strides[1] is normalized to 1 because shape[1] == 1.
+  managed_tensor->dl_tensor.strides = strides;
+
+  ICHECK(runtime::IsContiguous(managed_tensor->dl_tensor));
+
+  managed_tensor->dl_tensor.strides = nullptr;
+  managed_tensor->deleter(managed_tensor);
+}
+
+TEST(NDArrayTest, IsContiguous_UncontiguousStride) {
+  auto array = runtime::NDArray::Empty({5, 1, 10}, DataType::Float(32), {kDLCPU});
+  DLManagedTensor* managed_tensor = array.ToDLPack();
+
+  int64_t strides[] = {1, 1, 1};
+  managed_tensor->dl_tensor.strides = strides;
+
+  ICHECK(!runtime::IsContiguous(managed_tensor->dl_tensor));
+
+  managed_tensor->dl_tensor.strides = nullptr;
+  managed_tensor->deleter(managed_tensor);
+}