diff --git a/benchmarks/float8/float8_inference_roofline.py b/benchmarks/float8/float8_inference_roofline.py
index 271fbbf530..ea28d3236e 100644
--- a/benchmarks/float8/float8_inference_roofline.py
+++ b/benchmarks/float8/float8_inference_roofline.py
@@ -46,6 +46,7 @@
     NVFP4InferenceConfig,
     NVFP4MMConfig,
 )
+from torchao.prototype.mx_formats.utils import to_blocked
 from torchao.quantization.quant_api import (
     Float8DynamicActivationFloat8WeightConfig,
     PerRow,
@@ -134,12 +135,18 @@ def get_gemm_times(
     elif recipe_name == "mxfp8_cublas":
         scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu)
         scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu)
+        scale_a = to_blocked(scale_a)
+        scale_b = to_blocked(scale_b)
     elif recipe_name == "mxfp4_cutlass":
         scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu)
         scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu)
+        scale_a = to_blocked(scale_a)
+        scale_b = to_blocked(scale_b)
     elif recipe_name == "nvfp4":
         scale_a = torch.ones(M, K // 16, device=device, dtype=torch.float8_e4m3fn)
         scale_b = torch.ones(N, K // 16, device=device, dtype=torch.float8_e4m3fn)
+        scale_a = to_blocked(scale_a)
+        scale_b = to_blocked(scale_b)
 
     else:
         assert False, "unsupported"
@@ -166,6 +173,9 @@ def run(
     recipe_name: str,
     do_benchmarks: bool = True,
     shape_gen_name: str = "pow2",
+    M: Optional[int] = None,
+    K: Optional[int] = None,
+    N: Optional[int] = None,
     n_limit: Optional[int] = None,
     save_profile_traces: bool = False,
     enable_fusion_modeling: bool = False,
@@ -174,7 +184,8 @@ def run(
     Args:
     * `recipe_name`: quantization recipe (tensorwise, rowwise, mxfp8*, mxfp4*, nvfp4*)
     * `do_benchmarks`: if True, gemm and e2e fwd+bwd of LNLinearSigmoid are benchmarked
-    * `shape_gen_name`: `llama`, `pow2`, `pow2_extended`, or `sweep`
+    * `shape_gen_name`: `llama`, `pow2`, `pow2_extended`, `sweep`, or `custom`
+    * `M|K|N`: if shape_gen_name is `custom`, then these values are used for MKN
     * `n_limit (optional)`: if specified, only runs `n_limit` iterations
     # `save_profile_traces (optional)`: if True, saves profiling traces
     # `enable_fusion_modeling`: if True, models activation -> gemm instead of just gemm
@@ -187,9 +198,13 @@ def run(
         ["do_benchmarks", do_benchmarks],
         ["shape_gen_name", shape_gen_name],
         ["enable_fusion_modeling", enable_fusion_modeling],
+        ["MKN", f"{M} {K} {N}"],
     ]
     print(tabulate(config_table, headers=["Parameter", "Value"], tablefmt="simple"))
 
+    # reassign user specified MKN, so we can use them for sympy
+    user_M, user_K, user_N = M, K, N
+
     M, K, N = sympy.symbols("M K N")
 
     fp8_ovhd_time_sympy = get_inference_float8_mem_sympy(
@@ -245,7 +260,7 @@ def run(
     ]
     results = []
 
-    name_to_shapes = get_name_to_shapes_iter(shape_gen_name, None, None, None)
+    name_to_shapes = get_name_to_shapes_iter(shape_gen_name, user_M, user_K, user_N)
 
     for idx, (name, (M_val, K_val, N_val)) in enumerate(tqdm.tqdm(name_to_shapes)):
         if n_limit is not None and idx >= n_limit: