diff --git a/test/prototype/mx_formats/test_kernels.py b/test/prototype/mx_formats/test_kernels.py
index 1729901933..4b6586b385 100644
--- a/test/prototype/mx_formats/test_kernels.py
+++ b/test/prototype/mx_formats/test_kernels.py
@@ -442,8 +442,8 @@ def triton_to_mxfp8_dim0_reference(
     not is_sm_at_least_89(),
     reason="float8 in triton requires CUDA capability 8.9 or greater",
 )
-@pytest.mark.parametrize("M", (256, 2048))
-@pytest.mark.parametrize("K", (256, 2048))
+@pytest.mark.parametrize("M", (128, 256))
+@pytest.mark.parametrize("K", (128, 256))
 def test_triton_mxfp8_dim1_randn(M, K):
     x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
     x_mx_ref, x_s_ref = triton_to_mxfp8_dim1_reference(x, block_size=32)
@@ -457,8 +457,8 @@ def test_triton_mxfp8_dim1_randn(M, K):
     not is_sm_at_least_100(),
     reason="mxfp8 requires CUDA capability 10.0 or greater",
 )
-@pytest.mark.parametrize("M", (256, 2048, 131072))
-@pytest.mark.parametrize("K", (256, 5120, 7168))
+@pytest.mark.parametrize("M", (128, 256))
+@pytest.mark.parametrize("K", (128, 256))
 def test_triton_mxfp8_dim0_randn(M, K):
     x = torch.randn(M, K, dtype=torch.bfloat16, device="cuda")
     x_mx_ref, x_s_ref = triton_to_mxfp8_dim0_reference(x, block_size=32)
@@ -473,7 +473,7 @@ def test_triton_mxfp8_dim0_randn(M, K):
     reason="mxfp8 requires CUDA capability 10.0 or greater",
 )
 def test_triton_mxfp8_dim0_zeros():
-    x = torch.zeros(8192, 5120, dtype=torch.bfloat16, device="cuda")
+    x = torch.zeros(128, 256, dtype=torch.bfloat16, device="cuda")
     x_mx_ref, x_s_ref = triton_to_mxfp8_dim0_reference(x, block_size=32)
     x_mx_t, x_s_t = triton_to_mxfp8_dim0(x, inner_block_size=32)
     assert not x_mx_t.isnan().any(), "quantized tensor should not contain NaNs"
@@ -486,8 +486,8 @@ def test_triton_mxfp8_dim0_zeros():
     not is_sm_at_least_100(),
     reason="mxfp8 requires CUDA capability 10.0 or greater",
 )
-@pytest.mark.parametrize("M", (256, 2048, 131072))
-@pytest.mark.parametrize("K", (256, 5120, 7168))
+@pytest.mark.parametrize("M", (128, 256))
+@pytest.mark.parametrize("K", (128, 256))
 @pytest.mark.parametrize("orig_dtype", (torch.float32, torch.bfloat16))
 def test_triton_mxfp8_dequant_dim0(M, K, orig_dtype):
     x = torch.zeros(M, K, dtype=orig_dtype, device="cuda")
@@ -529,8 +529,8 @@ def test_rearrange(shape):
     not is_sm_at_least_100(),
     reason="MXFP8 requires CUDA capability 10.0 or greater",
 )
-@pytest.mark.parametrize("M", (32, 64, 2048))
-@pytest.mark.parametrize("K", (32, 64, 2048))
+@pytest.mark.parametrize("M", (32, 256))
+@pytest.mark.parametrize("K", (32, 256))
 @pytest.mark.parametrize("input_dtype", (torch.float32, torch.bfloat16))
 @pytest.mark.parametrize(
     "scaling_mode", (ScaleCalculationMode.FLOOR, ScaleCalculationMode.RCEIL)
diff --git a/test/prototype/mx_formats/test_mx_linear.py b/test/prototype/mx_formats/test_mx_linear.py
index c858657af6..49343c6608 100644
--- a/test/prototype/mx_formats/test_mx_linear.py
+++ b/test/prototype/mx_formats/test_mx_linear.py
@@ -238,9 +238,11 @@ def test_activation_checkpointing():
     "recipe_name",
     [
         "mxfp8_emulated",
-        "mxfp4_emulated",
         "mxfp8_cublas",
-        "mxfp4_cutlass",
+        # TODO(future PR): add mxfp4 back here, but ensure CI speed is not too
+        # slow
+        # "mxfp4_emulated",
+        # "mxfp4_cutlass",
     ],
 )
 @pytest.mark.parametrize("bias", [False, True])
@@ -258,7 +260,6 @@ def test_activation_checkpointing():
     "scale_calculation_mode",
     [
         ScaleCalculationMode.FLOOR,
-        ScaleCalculationMode.CEIL,
         # even + compile does not work yet:
         # https://gist.github.com/vkuzo/1a04845cd503b1c75291aa1ea3bf79c4
         # ScaleCalculationMode.EVEN,