diff --git a/benchmarks/float8/float8_roofline.py b/benchmarks/float8/float8_roofline.py
index 2b3f631d8c..9bd4206d76 100644
--- a/benchmarks/float8/float8_roofline.py
+++ b/benchmarks/float8/float8_roofline.py
@@ -349,7 +349,7 @@ def run(
 
         # get the float8 dynamic axiswise scaling gpu kernel time
         torch._dynamo.reset()
-        config = recipe_name_to_linear_config(Float8LinearRecipeName.ALL_AXISWISE)
+        config = recipe_name_to_linear_config(Float8LinearRecipeName.ROWWISE)
         m_fp8_dyn_axs = convert_to_float8_training(copy.deepcopy(m_orig), config=config)
         m_fp8_dyn_axs = torch.compile(m_fp8_dyn_axs)
         fp8_dyn_axs_time_actual_s = get_gpu_kernel_time(m_fp8_dyn_axs, x)
@@ -358,7 +358,7 @@ def run(
         # TODO(future PR): enable below once basic performance issues
         # are fixed
         # torch._dynamo.reset()
-        # config = recipe_name_to_linear_config(Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP)
+        # config = recipe_name_to_linear_config(Float8LinearRecipeName.ROWWISE_WITH_GW_HP)
         # m_fp8_lw = convert_to_float8_training(m_orig, config=config)
         # m_fp8_lw = torch.compile(m_fp8_lw)
         # fp8_lw_time_actual_s = get_gpu_kernel_time(m_fp8_lw, x)
diff --git a/test/float8/test_base.py b/test/float8/test_base.py
index b537c7ab9f..055b3f3054 100644
--- a/test/float8/test_base.py
+++ b/test/float8/test_base.py
@@ -420,8 +420,8 @@ def test_linear_from_config_params(
     @pytest.mark.parametrize(
         "recipe_name",
         [
-            Float8LinearRecipeName.ALL_AXISWISE,
-            Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP,
+            Float8LinearRecipeName.ROWWISE,
+            Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
         ],
     )
     @pytest.mark.parametrize("x_shape", [(16, 16), (2, 16, 16), (3, 2, 16, 16)])
diff --git a/test/float8/test_compile.py b/test/float8/test_compile.py
index d9c71f7395..83ec188192 100644
--- a/test/float8/test_compile.py
+++ b/test/float8/test_compile.py
@@ -218,8 +218,8 @@ def test_inductor_from_config_params(
 @pytest.mark.parametrize(
     "recipe_name",
     [
-        Float8LinearRecipeName.ALL_AXISWISE,
-        Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP,
+        Float8LinearRecipeName.ROWWISE,
+        Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
     ],
 )
 @unittest.skipIf(
diff --git a/test/float8/test_dtensor.py b/test/float8/test_dtensor.py
index d0f34da0a9..d71e23b6b2 100644
--- a/test/float8/test_dtensor.py
+++ b/test/float8/test_dtensor.py
@@ -198,7 +198,7 @@ def _test_fp8_mlp_tensor_parallelism_base(
     device = mesh.device_type
 
     if rowwise:
-        config = recipe_name_to_linear_config(Float8LinearRecipeName.ALL_AXISWISE)
+        config = recipe_name_to_linear_config(Float8LinearRecipeName.ROWWISE)
         # hack around config being frozen
         # TODO(future PR): we should make this nicer at the config level
         object.__setattr__(config, "emulate", True)
diff --git a/test/float8/test_numerics_integration.py b/test/float8/test_numerics_integration.py
index 311964d831..e47d4310b4 100644
--- a/test/float8/test_numerics_integration.py
+++ b/test/float8/test_numerics_integration.py
@@ -198,8 +198,8 @@ def test_encoder_fw_bw_from_config_params(
     @pytest.mark.parametrize(
         "recipe_name",
         [
-            Float8LinearRecipeName.ALL_AXISWISE,
-            Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP,
+            Float8LinearRecipeName.ROWWISE,
+            Float8LinearRecipeName.ROWWISE_WITH_GW_HP,
         ],
     )
     @pytest.mark.skipif(
diff --git a/torchao/float8/config.py b/torchao/float8/config.py
index b971ff31b0..c1720ea70c 100644
--- a/torchao/float8/config.py
+++ b/torchao/float8/config.py
@@ -326,9 +326,9 @@ def __post_init__(self):
 # TODO(future PR): go through a round of design on this, and eventually expose
 # as a top level public API.
 class Float8LinearRecipeName(enum.Enum):
-    ALL_TENSORWISE = "all_tensorwise"
-    ALL_AXISWISE = "all_axiswise"
-    LW_AXISWISE_WITH_GW_HP = "lw_axiswise_with_gw_hp"
+    TENSORWISE = "tensorwise"
+    ROWWISE = "rowwise"
+    ROWWISE_WITH_GW_HP = "rowwise_with_gw_hp"
 
 
 def recipe_name_to_linear_config(
@@ -339,11 +339,11 @@ def recipe_name_to_linear_config(
     Output: a `Float8LinearConfig` configured to implement the recipe
     """
 
-    if recipe_name is Float8LinearRecipeName.ALL_TENSORWISE:
+    if recipe_name is Float8LinearRecipeName.TENSORWISE:
         # Default, dynamic per-tensor scaling with the cuBLAS tensorwise kernel
         return Float8LinearConfig()
 
-    elif recipe_name is Float8LinearRecipeName.ALL_AXISWISE:
+    elif recipe_name is Float8LinearRecipeName.ROWWISE:
         # dynamic axiswise scaling with the CUTLASS rowwise kernel
         cc_i = CastConfig(
             scaling_granularity=ScalingGranularity.AXISWISE, target_dtype=e4m3_dtype
@@ -363,7 +363,7 @@ def recipe_name_to_linear_config(
             round_scales_to_power_of_2=True,
         )
 
-    elif recipe_name is Float8LinearRecipeName.LW_AXISWISE_WITH_GW_HP:
+    elif recipe_name is Float8LinearRecipeName.ROWWISE_WITH_GW_HP:
         # lw's recipe for a modification on all-axiswise:
         #
         #   output_hp = input_fp8_axiswise_dim0 @ weight_t_axiswise_dim1