diff --git a/dpnp/linalg/dpnp_utils_linalg.py b/dpnp/linalg/dpnp_utils_linalg.py
index 2d7804234dec..0302c4bf20dc 100644
--- a/dpnp/linalg/dpnp_utils_linalg.py
+++ b/dpnp/linalg/dpnp_utils_linalg.py
@@ -532,16 +532,29 @@ def _batched_svd(
             batch_shape_orig,
         )
 
+    if m < n:
+        trans_flag = True
+    else:
+        trans_flag = False
+
     k = min(m, n)
     if compute_uv:
         if full_matrices:
-            u_shape = (m, m) + (batch_size,)
-            vt_shape = (n, n) + (batch_size,)
+            if trans_flag:
+                u_shape = (n, n) + (batch_size,)
+                vt_shape = (m, m) + (batch_size,)
+            else:
+                u_shape = (m, m) + (batch_size,)
+                vt_shape = (n, n) + (batch_size,)
             jobu = ord("A")
             jobvt = ord("A")
         else:
-            u_shape = (m, k) + (batch_size,)
-            vt_shape = (k, n) + (batch_size,)
+            if trans_flag:
+                u_shape = (n, k) + (batch_size,)
+                vt_shape = (m, k) + (batch_size,)
+            else:
+                u_shape = (m, k) + (batch_size,)
+                vt_shape = (k, n) + (batch_size,)
             jobu = ord("S")
             jobvt = ord("S")
     else:
@@ -554,7 +567,10 @@ def _batched_svd(
 
     # Reorder the elements by moving the last two axes of `a` to the front
     # to match fortran-like array order which is assumed by gesvd.
-    a = dpnp.moveaxis(a, (-2, -1), (0, 1))
+    if trans_flag:
+        a = dpnp.moveaxis(a, (-1, -2), (0, 1))
+    else:
+        a = dpnp.moveaxis(a, (-2, -1), (0, 1))
 
     # oneMKL LAPACK gesvd destroys `a` and assumes fortran-like array
     # as input.
@@ -607,6 +623,17 @@ def _batched_svd(
         # gesvd call writes `u_h` and `vt_h` in Fortran order;
         # reorder the axes to match C order by moving the last axis
         # to the front
+        if trans_flag:
+            u = dpnp.moveaxis(u_h, (0, 2), (2, 0))
+            vt = dpnp.moveaxis(vt_h, (0, 2), (2, 0))
+            if a_ndim > 3:
+                u = u.reshape(batch_shape_orig + u.shape[-2:])
+                vt = vt.reshape(batch_shape_orig + vt.shape[-2:])
+            # dpnp.moveaxis can make the array non-contiguous if it is not 2D
+            # Convert to contiguous to align with NumPy
+            u = dpnp.ascontiguousarray(u)
+            vt = dpnp.ascontiguousarray(vt)
+            return vt, s, u
         u = dpnp.moveaxis(u_h, -1, 0)
         vt = dpnp.moveaxis(vt_h, -1, 0)
         if a_ndim > 3:
@@ -2695,6 +2722,13 @@ def dpnp_svd(
             a, uv_type, s_type, full_matrices, compute_uv, exec_q, usm_type
         )
 
+    if m < n:
+        a = a.transpose()
+        trans_flag = True
+    else:
+        a = a
+        trans_flag = False
+
     # oneMKL LAPACK gesvd destroys `a` and assumes fortran-like array as input.
     # Allocate 'F' order memory for dpnp arrays to comply with
     # these requirements.
@@ -2719,13 +2753,21 @@ def dpnp_svd(
     k = min(m, n)
     if compute_uv:
         if full_matrices:
-            u_shape = (m, m)
-            vt_shape = (n, n)
+            if trans_flag:
+                u_shape = (n, n)
+                vt_shape = (m, m)
+            else:
+                u_shape = (m, m)
+                vt_shape = (n, n)
             jobu = ord("A")
             jobvt = ord("A")
         else:
-            u_shape = (m, k)
-            vt_shape = (k, n)
+            if trans_flag:
+                u_shape = (n, k)
+                vt_shape = (m, k)
+            else:
+                u_shape = (m, k)
+                vt_shape = (k, n)
             jobu = ord("S")
             jobvt = ord("S")
     else:
@@ -2763,6 +2805,10 @@ def dpnp_svd(
     if compute_uv:
         # gesvd call writes `u_h` and `vt_h` in Fortran order;
         # Convert to contiguous to align with NumPy
+        if trans_flag:
+            u_h = u_h.transpose()
+            vt_h = vt_h.transpose()
+            return vt_h, s_h, u_h
         u_h = dpnp.ascontiguousarray(u_h)
         vt_h = dpnp.ascontiguousarray(vt_h)
         return u_h, s_h, vt_h
diff --git a/dpnp/tests/third_party/cupy/linalg_tests/test_decomposition.py b/dpnp/tests/third_party/cupy/linalg_tests/test_decomposition.py
index d2c3ac69aacc..43d623f75a54 100644
--- a/dpnp/tests/third_party/cupy/linalg_tests/test_decomposition.py
+++ b/dpnp/tests/third_party/cupy/linalg_tests/test_decomposition.py
@@ -280,12 +280,6 @@ def test_svd_rank2_empty_array_compute_uv_false(self, xp):
             array, full_matrices=self.full_matrices, compute_uv=False
         )
 
-    # The issue was expected to be resolved once CMPLRLLVM-53771 is available,
-    # which has to be included in DPC++ 2024.1.0, but problem still exists
-    # on Windows
-    @pytest.mark.skipif(
-        is_cpu_device() and is_win_platform(), reason="SAT-7145"
-    )
     @_condition.repeat(3, 10)
     def test_svd_rank3(self):
         self.check_usv((2, 3, 4))
@@ -295,9 +289,6 @@ def test_svd_rank3(self):
         self.check_usv((2, 4, 3))
         self.check_usv((2, 32, 32))
 
-    @pytest.mark.skipif(
-        is_cpu_device() and is_win_platform(), reason="SAT-7145"
-    )
     @_condition.repeat(3, 10)
     def test_svd_rank3_loop(self):
         # This tests the loop-based batched gesvd on CUDA (_gesvd_batched)
@@ -345,9 +336,6 @@ def test_svd_rank3_empty_array_compute_uv_false2(self, xp):
             array, full_matrices=self.full_matrices, compute_uv=False
         )
 
-    @pytest.mark.skipif(
-        is_cpu_device() and is_win_platform(), reason="SAT-7145"
-    )
     @_condition.repeat(3, 10)
     def test_svd_rank4(self):
         self.check_usv((2, 2, 3, 4))
@@ -357,9 +345,6 @@ def test_svd_rank4(self):
         self.check_usv((2, 2, 4, 3))
         self.check_usv((2, 2, 32, 32))
 
-    @pytest.mark.skipif(
-        is_cpu_device() and is_win_platform(), reason="SAT-7145"
-    )
     @_condition.repeat(3, 10)
     def test_svd_rank4_loop(self):
         # This tests the loop-based batched gesvd on CUDA (_gesvd_batched)