diff --git a/test/float8/test_base.py b/test/float8/test_base.py
index c19478e02a..cec466ea1d 100644
--- a/test/float8/test_base.py
+++ b/test/float8/test_base.py
@@ -370,8 +370,9 @@ def test_linear_from_config_params(
     @pytest.mark.parametrize(
         "recipe_name",
         [
-            Float8LinearRecipeName.ROWWISE,
-            Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
+            # Float8LinearRecipeName.ROWWISE,
+            # Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
+            Float8LinearRecipeName.FWD_ROWWISE_GI_ROWWISE_GW_HP,
         ],
     )
     @pytest.mark.parametrize("x_shape", [(16, 16), (2, 16, 16), (3, 2, 16, 16)])
diff --git a/torchao/float8/config.py b/torchao/float8/config.py
index b362390946..81f99b9e5f 100644
--- a/torchao/float8/config.py
+++ b/torchao/float8/config.py
@@ -134,6 +134,12 @@ class Float8LinearRecipeName(enum.Enum):
     #   * the e4m3 dtype is used across the board, including for gradients
     ROWWISE_WITH_GW_HP = "rowwise_with_gw_hp"
 
+    # debug only, not for land
+    FWD_FLOAT8_BWD_HP = "fwd_float8_bwd_hp"
+
+    # debug only, not for land
+    FWD_ROWWISE_GI_ROWWISE_GW_HP = "fwd_rowwise_gi_rowwise_gw_hp"
+
 
 @dataclass(frozen=True)
 class Float8LinearConfig:
@@ -336,5 +342,59 @@ def from_recipe_name(
                 round_scales_to_power_of_2=True,
             )
 
+        elif recipe_name is Float8LinearRecipeName.FWD_FLOAT8_BWD_HP:
+            # output_hp = input_fp8_axiswise_dim0 @ weight_t_axiswise_dim1
+            cc_i = CastConfig(scaling_granularity=ScalingGranularity.AXISWISE)
+            cc_w = CastConfig(scaling_granularity=ScalingGranularity.AXISWISE)
+
+            # grad_input_hp = grad_output_hp @ weight_hp
+            cc_go = CastConfig(scaling_type=ScalingType.DISABLED)
+            cc_w_gi = CastConfig(scaling_type=ScalingType.DISABLED)
+
+            # grad_weight_hp = input_t_hp @ grad_output_hp
+            cc_i_gw = CastConfig(scaling_type=ScalingType.DISABLED)
+            cc_go_gw = CastConfig(
+                scaling_type=ScalingType.DISABLED, target_dtype=e4m3_dtype
+            )
+
+            return Float8LinearConfig(
+                cast_config_input=cc_i,
+                cast_config_weight=cc_w,
+                cast_config_grad_output=cc_go,
+                cast_config_input_for_grad_weight=cc_i_gw,
+                cast_config_weight_for_grad_input=cc_w_gi,
+                cast_config_grad_output_for_grad_weight=cc_go_gw,
+                round_scales_to_power_of_2=True,
+            )
+
+        elif recipe_name is Float8LinearRecipeName.FWD_ROWWISE_GI_ROWWISE_GW_HP:
+            # output_hp = input_fp8_axiswise_dim0 @ weight_t_axiswise_dim1
+            cc_i = CastConfig(scaling_granularity=ScalingGranularity.AXISWISE)
+            cc_w = CastConfig(scaling_granularity=ScalingGranularity.AXISWISE)
+
+            # grad_input_hp = grad_output_fp8_axiswise_dim0 @ weight_axiswise_dim1
+            cc_go = CastConfig(
+                scaling_granularity=ScalingGranularity.AXISWISE, target_dtype=e4m3_dtype
+            )
+            cc_w_gi = CastConfig(
+                scaling_granularity=ScalingGranularity.AXISWISE, target_dtype=e4m3_dtype
+            )
+
+            # grad_weight_hp = input_t_hp @ grad_output_hp
+            cc_i_gw = CastConfig(scaling_type=ScalingType.DISABLED)
+            cc_go_gw = CastConfig(
+                scaling_type=ScalingType.DISABLED, target_dtype=e4m3_dtype
+            )
+
+            return Float8LinearConfig(
+                cast_config_input=cc_i,
+                cast_config_weight=cc_w,
+                cast_config_grad_output=cc_go,
+                cast_config_input_for_grad_weight=cc_i_gw,
+                cast_config_weight_for_grad_input=cc_w_gi,
+                cast_config_grad_output_for_grad_weight=cc_go_gw,
+                round_scales_to_power_of_2=True,
+            )
+
         else:
             raise AssertionError(f"unknown recipe_name {recipe_name}")