diff --git a/fbgemm_gpu/experimental/gemm/test/fp8_gemm_benchmark.py b/fbgemm_gpu/experimental/gemm/test/fp8_gemm_benchmark.py
index 77fa04b3a2..9eb1a6c7d8 100644
--- a/fbgemm_gpu/experimental/gemm/test/fp8_gemm_benchmark.py
+++ b/fbgemm_gpu/experimental/gemm/test/fp8_gemm_benchmark.py
@@ -70,38 +70,38 @@ def _run_benchmark(
         )
 
     shapes = [
-        (8192, 8192, 512),
+        # (8192, 8192, 512),
         (8192, 8192, 8192),
-        (65536, 8192, 7168),
-        (65536, 3584, 8192),
-        (8192, 14336, 4096),
+        # (65536, 8192, 7168),
+        # (65536, 3584, 8192),
+        # (8192, 14336, 4096),
     ]
     for shape in shapes:
-        _run_benchmark(bf16_bench, shape=shape, tag="bf16")
-        _run_benchmark(scale_row_bench, shape=shape, tag="fp8 scale + row gemm")
-        _run_benchmark(scale_block_bench, shape=shape, tag="fp8 scale + block gemm")
-        _run_benchmark(
-            row_gemm_bench,
-            shape=shape,
-            tag="fp8 row gemm only | fp8_fast_accum=True",
-        )
+        # _run_benchmark(bf16_bench, shape=shape, tag="bf16")
+        # _run_benchmark(scale_row_bench, shape=shape, tag="fp8 scale + row gemm")
+        # _run_benchmark(scale_block_bench, shape=shape, tag="fp8 scale + block gemm")
+        # _run_benchmark(
+        #     row_gemm_bench,
+        #     shape=shape,
+        #     tag="fp8 row gemm only | fp8_fast_accum=True",
+        # )
         _run_benchmark(
             row_gemm_bench_no_fast_acc,
             shape=shape,
             tag="fp8 row gemm only | fp8_fast_accum=False",
         )
-        _run_benchmark(
-            row_gemm_bench_imprecise_acc,
-            shape=shape,
-            tag="fp8 row gemm only | max_num_imprecise_acc=32",
-        )
-        _run_benchmark(block_gemm_bench, shape=shape, tag="fp8 block gemm only")
-        if rowwise_tma:
-            _run_benchmark(
-                row_gemm_bench_tma,
-                shape=shape,
-                tag="fp8 row gemm only | fp8_fast_accum=True | tma_persistent=True",
-            )
+        # _run_benchmark(
+        #     row_gemm_bench_imprecise_acc,
+        #     shape=shape,
+        #     tag="fp8 row gemm only | max_num_imprecise_acc=32",
+        # )
+        # _run_benchmark(block_gemm_bench, shape=shape, tag="fp8 block gemm only")
+        # if rowwise_tma:
+        #     _run_benchmark(
+        #         row_gemm_bench_tma,
+        #         shape=shape,
+        #         tag="fp8 row gemm only | fp8_fast_accum=True | tma_persistent=True",
+        #     )
 
 
 def bf16_bench(x: Tensor, w: Tensor) -> Callable[[], Tensor]:
diff --git a/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py b/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py
index ce2cd12a38..8d31eacafe 100644
--- a/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py
+++ b/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py
@@ -362,8 +362,7 @@ def _kernel_matmul_fp8_row(
 
 
 @triton.autotune(
-    configs=MATMUL_CONFIGS
-    + [
+    configs= [
         Config(
             {"BLOCK_M": 128, "BLOCK_N": 128, "BLOCK_K": 128, "SPLIT_K": 1},
             num_stages=3,
diff --git a/fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py b/fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py
index 65b34a956d..079799085f 100644
--- a/fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py
+++ b/fbgemm_gpu/experimental/gen_ai/bench/quantize_ops.py
@@ -144,7 +144,7 @@ def get_quantize_ops() -> List[QuantizeOpBase]:
     return quantize_op_registry
 
 
-@register_quantize_op
+# @register_quantize_op
 class BF16Baseline(QuantizeOpBase):
     """
     Baseline BF16 matmul.
@@ -172,7 +172,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class ScaledMMBaseline(QuantizeOpBase):
     """
     Reference FP8 matmul implemented in native torch with cublas or hipblas.
@@ -253,7 +253,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class ScaledMMRowwise(QuantizeOpBase):
     def quantize(self, x, w):
         xq, x_scale = quantize_fp8_row(x)
@@ -292,7 +292,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class FP8TensorwiseGemm(QuantizeOpBase):
     """
     FP8 matmul with tensorwise scaling.
@@ -361,7 +361,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class FP8RowwiseGemm(QuantizeOpBase):
     """
     FP8 matmul with rowwise scaling.
@@ -430,7 +430,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class FP8TritonBlockwiseGemm(QuantizeOpBase):
     """
     FP8 matmul with block scaling.
@@ -463,7 +463,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class FP8CutlassBlockwiseGemm(QuantizeOpBase):
     """
     FP8 matmul with block scaling.
@@ -501,7 +501,7 @@ def cuda(self) -> bool:
 
 
 # CUTLASS kernel v2
-@register_quantize_op
+# @register_quantize_op
 class CutlassFP8TensorwiseGemm_v2(QuantizeOpBase):
     """
     FP8 matmul with tensorwise scaling.
@@ -535,7 +535,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class F8I4RowwiseGemm(QuantizeOpBase):
     """
     Mixed Precision FP8 Activations with Int4 Weights.
@@ -611,7 +611,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class BF16I4RowwiseGemm(F8I4RowwiseGemm):
     """
     Mixed Precision BF16 Activations with Int4 Weights.
@@ -645,7 +645,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class TinyGemmBF16I4(QuantizeOpBase):
     """
     Mixed Precision BF16 Activations with Int4 Weights using tinygemm.
@@ -682,7 +682,7 @@ def cuda(self) -> bool:
         return True
 
 
-@register_quantize_op
+# @register_quantize_op
 class MarlinBF16I4(QuantizeOpBase):
     """
     Mixed Precision BF16 Activations with Int4 Weights using Marlin.