diff --git a/tests/pytorch/test_numerics.py b/tests/pytorch/test_numerics.py
index e5813ae1f..21d33cc43 100644
--- a/tests/pytorch/test_numerics.py
+++ b/tests/pytorch/test_numerics.py
@@ -639,6 +639,9 @@ def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_m
         pytest.skip("FP8 parameters are not supported in debug mode.")
     if recipe.float8_block_scaling() and not fp8_block_scaling_available:
         pytest.skip(reason_for_no_fp8_block_scaling)
+    if (IS_HIP_EXTENSION and get_device_compute_capability() == (9, 5) and
+        dtype in (torch.float16, torch.bfloat16) and rocm_attn_backend()[2]):
+        pytest.skip("Test is not supported on GFX950 with current parameters and CK fused attention backend and non-zero dropout.")
 
     config = model_configs[model]
 
@@ -761,6 +764,9 @@ def test_gpt_full_activation_recompute(
         pytest.skip("FP8 parameters are not supported in debug mode.")
     if recipe.float8_block_scaling() and not fp8_block_scaling_available:
         pytest.skip(reason_for_no_fp8_block_scaling)
+    if (IS_HIP_EXTENSION and get_device_compute_capability() == (9, 5) and
+        dtype in (torch.float16, torch.bfloat16) and rocm_attn_backend()[2]):
+        pytest.skip("Test is not supported on GFX950 with current parameters and CK fused attention backend and non-zero dropout.")
 
     config = model_configs[model]
     torch.compiler.reset() # avoid cache size limit overflow
@@ -909,6 +915,10 @@ def _test_e2e_checkpointing(bs, dtype, config, checkpoint=False, steps=10, path=
 @pytest.mark.parametrize("bs", batch_sizes)
 @pytest.mark.parametrize("model", ["126m"])
 def test_gpt_checkpointing(dtype, bs, model):
+    if (IS_HIP_EXTENSION and get_device_compute_capability() == (9, 5) and
+        dtype in (torch.float16, torch.bfloat16) and rocm_attn_backend()[2]):
+        pytest.skip("Test is not supported on GFX950 with current parameters and CK fused attention backend and non-zero dropout.")
+
     config = model_configs[model]
     outputs = _test_e2e_checkpointing(bs, dtype, config, checkpoint=False)
     outputs_checkpoint = _test_e2e_checkpointing(bs, dtype, config, checkpoint=True)
@@ -2410,6 +2420,9 @@ def test_gpt_fp8_parameters(dtype, bs, model, recipe):
         pytest.skip("FP8 parameters are not supported in debug mode.")
     if recipe.float8_block_scaling() and not fp8_block_scaling_available:
         pytest.skip(reason_for_no_fp8_block_scaling)
+    if (IS_HIP_EXTENSION and get_device_compute_capability() == (9, 5) and
+        dtype in (torch.float16, torch.bfloat16) and rocm_attn_backend()[2]):
+        pytest.skip("Test is not supported on GFX950 with current parameters and CK fused attention backend and non-zero dropout.")
 
     config = model_configs[model]
 
@@ -2561,10 +2574,21 @@ def test_transformer_layer_hidden_states_format(dtype, bs, model):
             max_seqlen_kv=config.seq_len,
         )
 
-        torch.testing.assert_close(
-            y_bshd,
-            y_thd.reshape(bs, config.seq_len, config.hidden_size).contiguous(),
-        )
+        if IS_HIP_EXTENSION:
+            # On some GPUs CK fused attention with THD can produce larger error
+            tols = dtype_tols(dtype)
+            tols["atol"] = 1e-3
+            torch.testing.assert_close(
+                y_bshd,
+                y_thd.reshape(bs, config.seq_len, config.hidden_size).contiguous(),
+                **tols,
+            )
+        else:
+
+            torch.testing.assert_close(
+                y_bshd,
+                y_thd.reshape(bs, config.seq_len, config.hidden_size).contiguous(),
+            )
 
 
 @pytest.mark.parametrize("dtype", param_types)