diff --git a/1 b/1
deleted file mode 100644
index 4cbe8775199..00000000000
--- a/1
+++ /dev/null
@@ -1,11 +0,0 @@
-ERROR: file or directory not found: 2
-
-============================= test session starts ==============================
-platform linux -- Python 3.11.0, pytest-7.4.0, pluggy-1.5.0
-rootdir: /data/users/vasiliy/pytorch
-configfile: pytest.ini
-plugins: hypothesis-6.124.3, typeguard-4.3.0
-collected 0 items
-Running 0 items in this shard
-
-============================ no tests ran in 0.05s =============================
diff --git a/aten/src/ATen/cuda/CUDABlas.cpp b/aten/src/ATen/cuda/CUDABlas.cpp
index 8abd00d55da..9f335ec7dd6 100644
--- a/aten/src/ATen/cuda/CUDABlas.cpp
+++ b/aten/src/ATen/cuda/CUDABlas.cpp
@@ -1444,9 +1444,6 @@ void scaled_gemm(
   const auto scaleType = CUDA_R_32F;
   const float alpha_val = 1.0;
   const float beta_val = 0.0;
-  if (scale_dtype == DataType::UFP8){
-    // k = k * 2;
-  }
   CuBlasLtMatmulDescriptor computeDesc(computeType, scaleType);
   computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSA, _cublasOpFromChar(transa));
   computeDesc.setAttribute(CUBLASLT_MATMUL_DESC_TRANSB, _cublasOpFromChar(transb));
diff --git a/test/test_matmul_cuda.py b/test/test_matmul_cuda.py
index 96fb7703d1b..f0f802b8f2f 100644
--- a/test/test_matmul_cuda.py
+++ b/test/test_matmul_cuda.py
@@ -843,131 +843,6 @@ class TestFP8MatmulCuda(TestCase):
         )
         torch.testing.assert_close(C, C_ref, atol=0, rtol=0)
 
-    # TODO(before land): real skip condition, should only run on CUDA 12.8+ with CUDA capability 10.0+
-    @unittest.skipIf(not PLATFORM_SUPPORTS_FP8, f8_msg)
-    def test_blockwise_nvfp4(self) -> None:
-
-        # inspiration: https://github.com/pytorch/ao/pull/1625
-
-        # not for land, need to move to in-core dtypes
-        from enum import IntEnum
-        class DataType(IntEnum):
-            DEFAULT = 0
-            E8M0 = 1
-            FP4 = 2
-            UFP8 = 3
-
-        device = "cuda"
-        # TODO other shapes
-        M, K, N = 128, 128, 128
-        BLOCK_SIZE = 16
-        # torch.set_printoptions(profile='full', linewidth=320)
-        torch.set_printoptions(edgeitems=7, linewidth=100)
-
-        assert M == K == N
-        # A_ref = torch.eye(M, device=device, dtype=torch.bfloat16)
-        # B_ref = torch.eye(M, device=device, dtype=torch.bfloat16)
-        A_ref = torch.zeros(M, K, device=device, dtype=torch.bfloat16)
-        # B_ref = torch.zeros(N, K, device=device, dtype=torch.bfloat16)
-        # A_ref = torch.ones(M, K, device=device, dtype=torch.bfloat16)
-        B_ref = torch.ones(N, K, device=device, dtype=torch.bfloat16)
-
-        A_ref[0][0:64 + 4] = 1
-        # A_ref[1][0] = 1
-        print('A_ref', A_ref)
-
-        # probe the kernel
-        if False:
-            idx = M-1
-            idx = 1
-            A_ref[idx][idx] = 1
-            B_ref[idx][idx] = 1
-
-        C_ref = A_ref @ B_ref
-        print('C_ref', C_ref)
-
-        A = A_ref.to(torch.uint8)
-        B = B_ref.to(torch.uint8)
-
-        # super hacky "cast" to fp4_e2m1_x1:
-        # * we only have 1s and 0s in uint8
-        # * 1 in uint8 is 0b00000001
-        # * 1 in fp4_e2m1_x1 is 0b00000010
-        # * just replace the values
-        # TODO(before land): use a generic cast and test numerics more 
-        # comprehensively
-        A[A == 1] = 2
-        B[B == 1] = 2
-
-        # now, pack fp4_e2m1_x1 into fp4_e2m1_x2
-        A = pack_uint4(A)
-        B = pack_uint4(B)
-        B = B.t()
-        if False:
-            vala = 0b00100001
-            valb = 0b00101001
-            A[0][0] = 0b00100001
-            A[0][1] = 0b00100001
-            A[1][0] = 0b00100001
-            A[1][1] = 0b00100001
-            B[0][0] = 0b00100001
-            B[0][1] = 0b00100001
-            B[1][0] = 0b00100001
-            B[1][1] = 0b00100001
-
-        print('A', A, A.shape)
-        print('B after t')
-        print(B, B.shape)
-
-        # TODO more scale correctness testing
-        A_scale = torch.full((M, K // BLOCK_SIZE), 1, device=device, dtype=torch.float8_e4m3fn).view(torch.uint8)
-        B_scale = torch.full((N, K // BLOCK_SIZE), 1, device=device, dtype=torch.float8_e4m3fn).view(torch.uint8)
-        # A_scale = torch.full((M, 256), 1, device=device, dtype=torch.float8_e4m3fn).view(torch.uint8)
-        # B_scale = torch.full((N, 256), 1, device=device, dtype=torch.float8_e4m3fn).view(torch.uint8)
-
-        # convert to swizzled format
-        A_scale = to_blocked(A_scale)
-        B_scale = to_blocked(B_scale)
-
-        # https://docs.nvidia.com/cuda/cublas/index.html?highlight=blocked#d-block-quantization
-        # e2m1 max = 6
-        # e4m3 max = 448
-        # calculation max = 128
-        scale_result = torch.tensor(6.0 * 448.0 / 128.0, device=device)
-        print(scale_result)
-
-        # TODO sweep fast_accum
-
-        C = torch._scaled_mm(
-            A,
-            B,
-            # scales are switched
-            B_scale,
-            A_scale,
-            bias=None,
-            # scale_result=scale_result,
-            out_dtype=torch.bfloat16,
-            use_fast_accum=False,
-            a_dtype=DataType.FP4,
-            b_dtype=DataType.FP4,
-            scale_dtype=DataType.UFP8,
-        )
-        print('C', C, C.shape)
-        print('A_max', torch.max(A))
-        print('C_max', torch.max(C))
-
-        # get the indices of the set element
-        # print('in_idx', (idx, idx), 'ref_idx', (C_ref == torch.max(C_ref)).nonzero(), 'result_idx', (C == torch.max(C)).nonzero())
-
-        if False:
-            print('C_ref')
-            print(C_ref, C_ref.shape)
-            print('C')
-            print(C, C.shape)
-            print(C.max(dim=0))
-            print(C.max(dim=1))
-        # torch.testing.assert_close(C, C_ref, atol=0, rtol=0)
-
 
 @unittest.skipIf(TEST_WITH_ROCM, "ROCm doesn't support CUTLASS")
 @unittest.skipIf(IS_WINDOWS, "Windows doesn't support CUTLASS extensions")