diff --git a/test/test_linalg.py b/test/test_linalg.py
index 6fc46663a67e7..4208ada17d877 100644
--- a/test/test_linalg.py
+++ b/test/test_linalg.py
@@ -9003,7 +9003,8 @@ def dims_full_for_fn():
             r1 = fntorch(t0_full, t1, t2)
             self.assertEqual(r0, r1)
 
-    @tf32_on_and_off(0.001)
+    # ROCm 6.4 passes with tf32=on, but 6.4.1 needed tolerance reduced slightly
+    @tf32_on_and_off(0.002 if torch.version.hip else 0.001)
     @bf32_on_and_off(0.001)
     def test_broadcast_batched_matmul(self, device):
         n_dim = random.randint(1, 8)
diff --git a/test/test_transformers.py b/test/test_transformers.py
index 798095e065785..1bd836a3eb15b 100644
--- a/test/test_transformers.py
+++ b/test/test_transformers.py
@@ -3194,6 +3194,8 @@ def _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len, p, seed, offset,
                 fudge_factors['grad_query'] = 650.0
             if dtype == torch.float32:
                 fudge_factors['grad_key'] = 90.0
+                if "gfx95" in torch.cuda.get_device_properties(0).gcnArchName:
+                    fudge_factors['grad_value'] = 15.0
 
         check_out_and_grad(
             (out_ref, out_lp_ref, out),
@@ -3315,6 +3317,8 @@ def _get_mem_eff_drop_mask(batch_size, n_heads, q_len, kv_len, p, seed, offset,
                 fudge_factors['grad_query'] = 650.0
             if dtype == torch.float32:
                 fudge_factors['grad_key'] = 90.0
+                if "gfx95" in torch.cuda.get_device_properties(0).gcnArchName:
+                    fudge_factors['grad_value'] = 15.0
 
         check_out_and_grad(
             (out_ref, out_lp_ref, out),