diff --git a/benchmarks/fp8_matmul.py b/benchmarks/fp8_matmul.py
index 61c9e22..d45a04a 100644
--- a/benchmarks/fp8_matmul.py
+++ b/benchmarks/fp8_matmul.py
@@ -68,8 +68,9 @@ def get_e8_scales(A: torch.Tensor, B: torch.Tensor):
     n_a_cols = ceil_div(K, 32)
     n_b_rows = ceil_div(N, 128) * 128
     n_b_cols = ceil_div(K, 32)
-    a_scales = torch.randint(256, (n_a_rows, n_a_cols), dtype=torch.uint8, device="cuda")
-    b_scales = torch.randint(256, (n_b_rows, n_b_cols), dtype=torch.uint8, device="cuda")
+    a_scales = torch.randn(n_a_rows, n_a_cols, dtype=torch.float32, device="cuda").to(torch.float8_e8m0fnu)
+    b_scales = torch.randn(n_b_rows, n_b_cols, dtype=torch.float32, device="cuda").to(torch.float8_e8m0fnu)
+
     return a_scales, b_scales
 
 
@@ -110,10 +111,9 @@ def get_fp8_matmul(
             return lambda: torch._scaled_mm(
                 A_fp8,
                 B_fp8,
-                b_scale,  # swap since we haven't figured this out yet
                 a_scale,
+                b_scale,
                 out_dtype=torch.bfloat16,
-                scale_dtype=1,
             )
         return lambda: addmm_float8_unwrapped_inference(
             A_fp8, a_scale, B_fp8, b_scale, output_dtype=torch.bfloat16, use_fast_accum=True
@@ -272,7 +272,7 @@ def get_configs_varying_k(
     compile_options = [False]
     configs = []
     fp8_kernels = [
-        # FP8Kernel.SCALED_MM,
+        FP8Kernel.SCALED_MM,
         # FP8Kernel.PERSISTENT,
         # FP8Kernel.PERSISTENT_TMA,
         # FP8Kernel.DEVICE_TMA,